Tag Archives: conferences

Google at ICCV 2019



This week, Seoul, South Korea hosts the International Conference on Computer Vision 2019 (ICCV 2019), one of the world's premier conferences on computer vision. As a leader in computer vision research and a Gold Sponsor, Google will have a strong presence at ICCV 2019 with over 200 Googlers in attendance, more than 40 research presentations, and involvement in the organization of a number of workshops and tutorials.

If you are attending ICCV this year, please stop by our booth. There you can chat with researchers who are actively pursuing the latest innovations in computer vision and demo some of their latest research, including the technology behind MediaPipe, the new Open Images dataset, new developments for Google Lens and much more.

This year Google researchers are recipients of three prestigious ICCV awards:
More details about the Google research being presented at ICCV 2019 can be found below (Google affiliations in blue).

Organizing Committee includes:
Ming-Hsuan Yang (Program Chair)

Oral Presentations
Learning Single Camera Depth Estimation using Dual-Pixels
Rahul Garg, Neal Wadhwa, Sameer Ansari, Jonathan Barron 

RIO: 3D Object Instance Re-Localization in Changing Indoor Environments
Johanna Wald, Armen Avetisyan, Nassir Navab, Federico Tombari, Matthias Niessner 

ShapeMask: Learning to Segment Novel Objects by Refining Shape Priors
Weicheng Kuo, Anelia Angelova, Jitendra Malik, Tsung-Yi Lin 

PuppetGAN: Cross-Domain Image Manipulation by Demonstration
Ben Usman, Nick Dufour, Kate Saenko, Chris Bregler

COCO-GAN: Generation by Parts via Conditional Coordinating
Chieh Hubert Lin, Chia-Che Chang, Yu-Sheng Chen, Da-Cheng Juan, Wei Wei, Hwann-Tzong Chen

Towards Unconstrained End-to-End Text Spotting
Siyang Qin, Alessandro Bissaco, Michalis Raptis, Yasuhisa Fujii, Ying Xiao

SinGAN: Learning a Generative Model from a Single Natural Image
Tamar Rott Shaham, Tali Dekel, Tomer Michaeli 
(ICCV 2019 Marr Prize Winner — Best Paper Award)

Generative Modeling for Small-Data Object Detection
Lanlan Liu, Michael Muelly, Jia Deng, Tomas Pfister, Li-Jia Li 

Searching for MobileNetV3
Andrew Howard, Mark Sandler, Bo Chen, Weijun Wang, Liang-Chieh Chen, Mingxing Tan, Grace Chu, Vijay Vasudevan, Yukun Zhu, Ruoming Pang, Hartwig Adam, Quoc Le 

S⁴L: Self-Supervised Semi-supervised Learning
Lucas Beyer, Xiaohua Zhai, Avital Oliver, Alexander Kolesnikov 

Sampling-Free Epistemic Uncertainty Estimation Using Approximated Variance Propagation
Janis Postels, Francesco Ferroni, Huseyin Coskun, Nassir Navab, Federico Tombari

Linearized Multi-sampling for Differentiable Image Transformation
Wei Jiang, Weiwei Sun, Andrea Tagliasacchi, Eduard Trulls, Kwang Moo Yi 

Poster Presentations
ELF: Embedded Localisation of Features in Pre-trained CNN
Assia Benbihi, Matthieu Geist, Cedric Pradalier 

Depth from Videos in the Wild: Unsupervised Monocular Depth Learning from Unknown Cameras
Ariel Gordon, Hanhan Li, Rico Jonschkowski, Anelia Angelova

ForkNet: Multi-branch Volumetric Semantic Completion from a Single Depth Image
Yida Wang, David Joseph Tan, Nassir Navab, Federico Tombari 

A Learned Representation for Scalable Vector Graphics
Raphael Gontijo Lopes, David Ha, Douglas Eck, Jonathon Shlens 

FrameNet: Learning Local Canonical Frames of 3D Surfaces from a Single RGB Image
Jingwei Huang, Yichao Zhou, Thomas Funkhouser, Leonidas Guibas

Prior-Aware Neural Network for Partially-Supervised Multi-Organ Segmentation
Yuyin Zhou, Zhe Li, Song Bai, Xinlei Chen, Mei Han, Chong Wang, Elliot Fishman, Alan Yuille 

Boundless: Generative Adversarial Networks for Image Extension
Dilip Krishnan, Piotr Teterwak, Aaron Sarna, Aaron Maschinot, Ce Liu, David Belanger, William Freeman

Cap2Det: Learning to Amplify Weak Caption Supervision for Object Detection
Keren Ye, Mingda Zhang, Adriana Kovashka, Wei Li, Danfeng Qin, Jesse Berent 

NOTE-RCNN: NOise Tolerant Ensemble RCNN for Semi-supervised Object Detection
Jiyang Gao, Jiang Wang, Shengyang Dai, Li-Jia Li, Ram Nevatia 

Object-Driven Multi-Layer Scene Decomposition from a Single Image
Helisa Dhamo, Nassir Navab, Federico Tombari 

Improving Adversarial Robustness via Guided Complement Entropy
Hao-Yun Chen, Jhao-Hong Liang, Shih-Chieh Chang, Jia-Yu Pan, Yu-Ting Chen, Wei Wei, Da-Cheng Juan 

XRAI: Better Attributions Through Regions
Andrei Kapishnikov, Tolga Bolukbasi, Fernanda Viegas, Michael Terry

SegSort: Segment Sorting for Semantic Segmentation
Jyh-Jing Hwang, Stella Yu, Jianbo Shi, Maxwell Collins, Tien-Ju Yang, Xiao Zhang, Liang-Chieh Chen 

Self-Supervised Learning with Geometric Constraints in Monocular Video: Connecting Flow, Depth, and Camera
Yuhua Chen, Cordelia Schmid, Cristian Sminchisescu 

VideoBERT: A Joint Model for Video and Language Representation Learning
Chen Sun, Austin Myers, Carl Vondrick, Kevin Murphy, Cordelia Schmid 

Explaining the Ambiguity of Object Detection and 6D Pose from Visual Data
Fabian Manhardt, Diego Martín Arroyo, Christian Rupprecht, Benjamin  Busam, Tolga Birdal, Nassir Navab, Federico Tombari 

Constructing Self-Motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation
Qing Lian, Lixin Duan, Fengmao Lv, Boqing Gong 

Learning Shape Templates Using Structured Implicit Functions
Kyle Genova, Forrester Cole, Daniel Vlasic, Aaron Sarna, William Freeman, Thomas Funkhouser

Transferable Representation Learning in Vision-and-Language Navigation
Haoshuo Huang, Vihan Jain, Harsh Mehta, Alexander Ku, Gabriel Magalhaes, Jason Baldridge, Eugene Ie 

Controllable Attention for Structured Layered Video Decomposition
Jean-Baptiste Alayrac, Joao Carreira, Relja Arandjelović, Andrew Zisserman

Pixel2Mesh++: Multi-view 3D Mesh Generation via Deformation
Chao Wen, Yinda Zhang, Zhuwen Li, Yanwei Fu

Beyond Cartesian Representations for Local Descriptors
Patrick Ebel, Anastasiia Mishchuk, Kwang Moo Yi, Pascal Fua, Eduard Trulls

Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data
Xiangyu Yue, Yang Zhang, Sicheng Zhao, Alberto Sangiovanni-Vincentelli, Kurt Keutzer, Boqing Gong 

Evolving Space-Time Neural Architectures for Videos
AJ Piergiovanni, Anelia Angelova, Alexander Toshev, Michael Ryoo 

Moulding Humans: Non-parametric 3D Human Shape Estimation from Single Images
Valentin Gabeur, Jean-Sebastien Franco, Xavier Martin, Cordelia Schmid, Gregory Rogez

Multi-view Image Fusion
Marc Comino Trinidad, Ricardo Martin-Brualla, Florian Kainz, Janne Kontkanen 

EvalNorm: Estimating Batch Normalization Statistics for Evaluation
Saurabh Singh, Abhinav Shrivastava

Attention Augmented Convolutional Networks
Irwan Bello, Barret Zoph, Quoc Le, Ashish Vaswani, Jonathon Shlens 

Patchwork: A Patch-wise Attention Network for Efficient Object Detection and Segmentation in Video Streams
Yuning Chai

Workshops
Low-Power Computer Vision
Organizers include: Bo Chen

Neural Architects
Organizers include: Barret Zoph

The 3rd YouTube-8M Large-Scale Video Understanding Workshop
Organizers include: Paul NatsevCordelia SchmidRahul SukthankarJoonseok LeeGeorge Toderici

Should We Pre-register Experiments in Computer Vision?
Organizers include: Jack Valmadre

Extreme Vision Modeling
Organizers include: Rahul Sukthankar

Joint COCO and Mapillary Recognition Challenge
Organizers include: Tsung-Yi Lin, Yin Cui

Open Images Challenge
Organizers include: Vittorio Ferrari, Alina Kuznetsova, Rodrigo Benenson, Victor Gomes, Matteo Malloci

Tutorials
Meta-Learning and Metric Learning Algorithms
Organizers include: Kevin Swersky

Source: Google AI Blog


Google at ACL 2019



This week, Florence, Italy hosts the 2019 Annual Meeting of the Association for Computational Linguistics (ACL 2019), the premier conference in the field of natural language understanding, covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

As a leader in natural language processing and understanding, and a Diamond Level sponsor of ACL 2019, Google will be on hand to showcase the latest research on syntax, semantics, discourse, conversation, multilingual modeling, sentiment analysis, question answering, summarization, and generally building better systems using labeled and unlabeled data.

If you’re attending ACL 2019, we hope that you’ll stop by the Google booth to meet our researchers and discuss projects and opportunities at Google that go into solving interesting problems for billions of people. Our researchers will also be on hand to demo the Natural Questions corpus, the Multilingual Universal Sentence Encoder and more. You can also learn more about the Google research being presented at ACL 2019 below (Google affiliations in blue).

Organizing Committee includes:
Enrique Alfonseca

Accepted Publications
A Joint Named-Entity Recognizer for Heterogeneous Tag-sets Using a Tag Hierarchy
Genady Beryozkin, Yoel Drori, Oren Gilon, Tzvika Hartman, Idan Szpektor

Do Neural Dialog Systems Use the Conversation History Effectively? An Empirical Study
Chinnadhurai Sankar, Sandeep Subramanian, Chris Pal, Sarath Chandar, Yoshua Bengio

Generating Logical Forms from Graph Representations of Text and Entities
Peter Shaw, Philip Massey, Angelica Chen, Francesco Piccinno, Yasemin Altun

Extracting Symptoms and their Status from Clinical Conversations
Nan Du, Kai Chen, Anjuli Kannan, Linh Trans, Yuhui Chen, Izhak Shafran

Stay on the Path: Instruction Fidelity in Vision-and-Language Navigation
Vihan Jain, Gabriel Magalhaes, Alexander Ku, Ashish Vaswani, Eugene Le, Jason Baldridge

Meaning to Form: Measuring Systematicity as Information
Tiago Pimentel, Arya D. McCarthy, Damian Blasi, Brian Roark, Ryan Cotterell

Matching the Blanks: Distributional Similarityfor Relation Learning
Livio Baldini Soares, Nicholas FitzGerald, Jeffrey Ling, Tom Kwiatkowski

Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context
Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc Le, Ruslan Salakhutdinov

HighRES: Highlight-based Reference-less Evaluation of Summarization
Hardy Hardy, Shashi Narayan, Andreas Vlachos

Zero-Shot Entity Linking by Reading Entity Descriptions
Lajanugen Logeswaran, Ming-Wei Chang, Kristina Toutanova, Kenton Lee, Jacob Devlin, Honglak Lee

Robust Neural Machine Translation with Doubly Adversarial Inputs
Yong Cheng, Lu Jiang, Wolfgang Macherey

Natural Questions: a Benchmark for Question Answering Research
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Matthew Kelcey, Jacob Devlin, Kenton Lee, Kristina N. Toutanova, Llion Jones, Ming-Wei Chang, Andrew Dai, Jakob Uszkoreit, Quoc Le, Slav Petrov

Like a Baby: Visually Situated Neural Language Acquisition
Alexander Ororbia, Ankur Mali, Matthew Kelly, David Reitter

What Kind of Language Is Hard to Language-Model?
Sebastian J. Mielke, Ryan Cotterell, Kyle Gorman, Brian Roark, Jason Eisner

How Multilingual is Multilingual BERT?
Telmo Pires, Eva Schlinger, Dan Garrette

Handling Divergent Reference Texts when Evaluating Table-to-Text Generation
Bhuwan Dhingra, Manaal Faruqui, Ankur Parikh, Ming-Wei Chang, Dipanjan Das, William Cohen

BAM! Born-Again Multi-Task Networks for Natural Language Understanding
Kevin Clark, Minh-Thang Luong, Urvashi Khandelal, Christopher D. Manning, Quoc V. Le

Dynamically Composing Domain-Data Selection with Clean-Data Selection by “Co-Curricular Learning" for Neural Machine Translation
Wei Wang, Isaac Caswell, Ciprian Chelba

Monotonic Infinite Lookback Attention for Simultaneous Machine Translation
Naveen Arivazhagan, Colin Cherry, Wolfgang Macherey, Chung-Cheng Chiu, Semih Yavuz, Ruoming Pang, Wei Li, Colin Raffel

On the Robustness of Self-Attentive Models
Yu-Lun Hsieh, Minhao Cheng, Da-Cheng Juan, Wei Wei, Wen-Lian Hsu, Cho-Jui Hsieh

Neural Decipherment via Minimum-Cost Flow: from Ugaritic to Linear B
Jiaming Luo, Yuan Cao, Regina Barzilay

How Large Are Lions? Inducing Distributions over Quantitative Attributes
Yanai Elazar, Abhijit Mahabal, Deepak Ramachandran, Tania Bedrax-Weiss, Dan Roth

BERT Rediscovers the Classical NLP Pipeline
Ian Tenney, Dipanjan Das, Ellie Pavlick

Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling
Alex Wang, Jan Hula, Patrick Xia, Raghavendra Pappagari, R. Thomas Mccoy, Roma Patel, Najoung Kim, Ian Tenney, Yinghui Huang, Katherin Yu, Shuning Jin, Berlin Chen, Benjamin Van Durme, Edouard Grave, Ellie Pavlick, Samuel R. Bowman

Robust Zero-Shot Cross-Domain Slot Filling with Example Values
Darsh Shah, Raghav Gupta, Amir Fayazi, Dilek Hakkani-Tur

Latent Retrieval for Weakly Supervised Open Domain Question Answering
Kenton Lee, Ming-Wei Chang, Kristina Toutanova

On-device Structured and Context Partitioned Projection Networks
Sujith Ravi, Zornitsa Kozareva

Incorporating Priors with Feature Attribution on Text Classification
Frederick Liu, Besim Avci

Informative Image Captioning with External Sources of Information
Sanqiang Zhao, Piyush Sharma, Tomer Levinboim, Radu Soricut

Reducing Word Omission Errors in Neural Machine Translation: A Contrastive Learning Approach
Zonghan Yang, Yong Cheng, Yang Liu, Maosong Sun

Synthetic QA Corpora Generation with Roundtrip Consistency
Chris Alberti, Daniel Andor, Emily Pitler, Jacob Devlin, Michael Collins

Unsupervised Paraphrasing without Translation
Aurko Roy, David Grangier

Workshops
Widening NLP 2019
Organizers include: Diyi Yang

NLP for Conversational AI
Organizers include: Thang-Minh Luong, Tania Bedrax-Weiss

The Fourth Arabic Natural Language Processing Workshop
Organizers include: Imed Zitouni

The Third Workshop on Abusive Language Online
Organizers include: Zeerak Waseem

TyP-NLP, Typology for Polyglot NLP
Organizers include: Manaal Faruqui

Gender Bias in Natural Language Processing
Organizers include: Kellie Webster

Tutorials
Wikipedia as a Resource for Text Analysis and Retrieval
Organizer: Marius Pasca

Source: Google AI Blog


Come Meet the Google Open Source Team at OSCON!

Google Cloud is proud to be a Diamond Sponsor at OSCON, and we’re excited for another year of connecting, learning, and sharing with the open source community! Google is deeply grateful to all of your amazing open source efforts, so to celebrate, our booth will have an Open Gratitude wall where we will acknowledge your contributions, and where we encourage you to express your gratitude for those who have helped you in open source!

Once you’ve recognized your open source heroes on the Open Gratitude wall, stick around at the Google Open Source booth to learn about topics such as open source governance, documentation, open source in ML and gaming, encouraging non-code contributions, and about Google’s open source outreach programs in general. At our booth sessions you can also explore open source projects such as Kubernetes, Istio, Go, and Beam (as well as other Apache projects). Booth office hours run from 10:15am to 7pm Wednesday, July 17, and from 10:15am to 4:10pm on Thursday, July 18. The full schedule will be posted at the booth—please come by and check it out!

In addition to the events at the booth, the Google open source team has two workshops on Tuesday, July 16:
This half-day workshop kicks off with an overview of research-backed documentation best practices. Andrew Chen, Erin McKean, and Aizhamal Nurmamat kyzy lead you through a hands-on exercise in which you'll create the skeleton of a ready-to-deploy documentation website for your open source project.
Paris Pittman takes you through the ins and outs of the Kubernetes contributor community so you can land your first PR. You'll learn about SIGs, the GitHub workflow, its automation and continuous integration (CI), setting up your dev environment, and much more. Stick around until the end, and you'll have time to work on your first PR with the help of current contributors.
We also hope you attend the main conference sessions presented by Googlers, especially the keynotes on Wednesday (Built to last: What Google and Microsoft have learned growing open source communities) and Thursday (Be a Docs Star), and the sessions on Wednesday:
And Thursday:
As part of our commitment to creating a diverse and inclusive community, we’ve redirected our conference swag budget into diversity scholarships. (We believe you’d prefer to have more interesting conversations with a wider range of people over another pair of socks!) But if you are looking for a souvenir of your time in Portland there will be a special Portland-themed sticker featuring Pancakes, the (extremely adorable) gRPC mascot, and we encourage projects to take and leave stickers in our sticker-swap space!

OSCON is one of the highlights of the year for those of us who love open source—we’re thrilled to be able to share what we’ve learned with you, and to learn what you’re interested in and excited about (and also what you think could improve). See you in Portland!

Announcing the YouTube-8M Segments Dataset



Over the last two years, the First and Second YouTube-8M Large-Scale Video Understanding Challenge and Workshop have collectively drawn 1000+ teams from 60+ countries to further advance large-scale video understanding research. While these events have enabled great progress in video classification, the YouTube dataset on which they were based only used machine-generated video-level labels, and lacked fine-grained temporally localized information, which limited the ability of machine learning models to predict video content.

To accelerate the research of temporal concept localization, we are excited to announce the release of YouTube-8M Segments, a new extension of the YouTube-8M dataset that includes human-verified labels at the 5-second segment level on a subset of YouTube-8M videos. With the additional temporal annotations, YouTube-8M is now both a large-scale classification dataset as well as a temporal localization dataset. In addition, we are hosting another Kaggle video understanding challenge focused on temporal localization, as well as an affiliated 3rd Workshop on YouTube-8M Large-Scale Video Understanding at the 2019 International Conference on Computer Vision (ICCV’19).



YouTube-8M Segments
Video segment labels provide a valuable resource for temporal localization not possible with video-level labels, and enable novel applications, such as capturing special video moments. Instead of exhaustively labeling all segments in a video, to create the YouTube-8M Segments extension, we manually labeled 5 segments (on average) per randomly selected video on the YouTube-8M validation dataset, totalling ~237k segments covering 1000 categories.

This dataset, combined with the previous YouTube-8M release containing a very large number of machine generated video-level labels, should allow learning temporal localization models in novel ways. Evaluating such classifiers is of course very challenging if only noisy video-level labels are available. We hope that the newly added human-labeled annotations will help ensure that researchers can more accurately evaluate their algorithms.

The 3rd YouTube-8M Video Understanding Challenge
This year the YouTube-8M Video Understanding Challenge focuses on temporal localization. Participants are encouraged to leverage noisy video-level labels together with a small segment-level validation set in order to better annotate and temporally localize concepts of interest. Unlike last year, there is no model size restriction. Each of the top 10 teams will be awarded $2,500 to support their travel to Seoul to attend ICCV’19. For details, please visit the Kaggle competition page.

The 3rd Workshop on YouTube-8M Large-Scale Video Understanding
Continuing in the tradition of the previous two years, the 3rd workshop will feature four invited talks by distinguished researchers as well as presentations by top-performing challenge participants. We encourage those who wish to attend to submit papers describing their research, experiments, or applications based on the YouTube-8M dataset, including papers summarizing their participation in the challenge above. Please refer to the workshop page for more details.

It is our hope that this newest extension will serve as a unique playground for temporal localization that mimics real world scenarios. We also look forward to the new challenge and workshop, which we believe will continue to advance research in large-scale video understanding. We hope you will join us again!

Acknowledgements
This post reflects the work of many machine perception researchers including Ke Chen, Nisarg Kothari, Joonseok Lee, Hanhan Li, Paul Natsev, Joe Yue-Hei Ng, Naderi Parizi, David Ross, Cordelia Schmid, Javier Snaider, Rahul Sukthankar, George Toderici, Balakrishnan Varadarajan, Sudheendra Vijayanarasimhan, Yexin Wang, Zheng Xu, as well as Julia Elliott and Walter Reade from Kaggle. We are also grateful for the support and advice from our partners at YouTube.

Source: Google AI Blog


Google at CVPR 2019

Andrew Helton, Editor, Google AI Communications

This week, Long Beach, CA hosts the 2019 Conference on Computer Vision and Pattern Recognition (CVPR 2019), the premier annual computer vision event comprising the main conference and several co-located workshops and tutorials. As a leader in computer vision research and a Platinum Sponsor, Google will have a strong presence at CVPR 2019—over 250 Googlers will be in attendance to present papers and invited talks at the conference, and to organize and participate in multiple workshops.

If you are attending CVPR this year, please stop by our booth and chat with our researchers who are actively pursuing the next generation of intelligent systems that utilize the latest machine learning techniques applied to various areas of machine perception. Our researchers will also be available to talk about and demo several recent efforts, including the technology behind predicting pedestrian motion, the Open Images V5 dataset and much more.

You can learn more about our research being presented at CVPR 2019 in the list below (Google affiliations highlighted in blue)

Area Chairs include:
Jonathan T. Barron, William T. Freeman, Ce Liu, Michael Ryoo, Noah Snavely

Oral Presentations
Relational Action Forecasting
Chen Sun, Abhinav Shrivastava, Carl Vondrick, Rahul Sukthankar, Kevin Murphy, Cordelia Schmid

Pushing the Boundaries of View Extrapolation With Multiplane Images
Pratul P. Srinivasan, Richard Tucker, Jonathan T. Barron, Ravi Ramamoorthi, Ren Ng, Noah Snavely

Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
Chenxi Liu, Liang-Chieh Chen, Florian Schroff, Hartwig Adam, Wei Hua, Alan L. Yuille, Li Fei-Fei

AutoAugment: Learning Augmentation Strategies From Data
Ekin D. Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, Quoc V. Le

DeepView: View Synthesis With Learned Gradient Descent
John Flynn, Michael Broxton, Paul Debevec, Matthew DuVall, Graham Fyffe, Ryan Overbeck, Noah Snavely, Richard Tucker

Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation
He Wang, Srinath Sridhar, Jingwei Huang, Julien Valentin, Shuran Song, Leonidas J. Guibas

Do Better ImageNet Models Transfer Better?
Simon Kornblith, Jonathon Shlens, Quoc V. Le

TextureNet: Consistent Local Parametrizations for Learning From High-Resolution Signals on Meshes
Jingwei Huang, Haotian Zhang, Li Yi, Thomas Funkhouser, Matthias Niessner, Leonidas J. Guibas

Diverse Generation for Multi-Agent Sports Games
Raymond A. Yeh, Alexander G. Schwing, Jonathan Huang, Kevin Murphy

Occupancy Networks: Learning 3D Reconstruction in Function Space
Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, Andreas Geiger

A General and Adaptive Robust Loss Function
Jonathan T. Barron

Learning the Depths of Moving People by Watching Frozen People
Zhengqi Li, Tali Dekel, Forrester Cole, Richard Tucker, Noah Snavely, Ce Liu, William T. Freeman

Composing Text and Image for Image Retrieval - an Empirical Odyssey
Nam Vo, Lu Jiang, Chen Sun, Kevin Murphy, Li-Jia Li, Li Fei-Fei, James Hays

Learning to Synthesize Motion Blur
Tim Brooks, Jonathan T. Barron

Neural Rerendering in the Wild
Moustafa Meshry, Dan B. Goldman, Sameh Khamis, Hugues Hoppe, Rohit Pandey, Noah Snavely, Ricardo Martin-Brualla

Neural Illumination: Lighting Prediction for Indoor Environments
Shuran Song, Thomas Funkhouser

Unprocessing Images for Learned Raw Denoising
Tim Brooks, Ben Mildenhall, Tianfan Xue, Jiawen Chen, Dillon Sharlet, Jonathan T. Barron

Posters
Co-Occurrent Features in Semantic Segmentation
Hang Zhang, Han Zhang, Chenguang Wang, Junyuan Xie

CrDoCo: Pixel-Level Domain Transfer With Cross-Domain Consistency
Yun-Chun Chen, Yen-Yu Lin, Ming-Hsuan Yang, Jia-Bin Huang

Im2Pencil: Controllable Pencil Illustration From Photographs
Yijun Li, Chen Fang, Aaron Hertzmann, Eli Shechtman, Ming-Hsuan Yang

Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis
Qi Mao, Hsin-Ying Lee, Hung-Yu Tseng, Siwei Ma, Ming-Hsuan Yang

Revisiting Self-Supervised Visual Representation Learning
Alexander Kolesnikov, Xiaohua Zhai, Lucas Beyer

Scene Graph Generation With External Knowledge and Image Reconstruction
Jiuxiang Gu, Handong Zhao, Zhe Lin, Sheng Li, Jianfei Cai, Mingyang Ling

Scene Memory Transformer for Embodied Agents in Long-Horizon Tasks
Kuan Fang, Alexander Toshev, Li Fei-Fei, Silvio Savarese

Spatially Variant Linear Representation Models for Joint Filtering
Jinshan Pan, Jiangxin Dong, Jimmy S. Ren, Liang Lin, Jinhui Tang, Ming-Hsuan Yang

Target-Aware Deep Tracking
Xin Li, Chao Ma, Baoyuan Wu, Zhenyu He, Ming-Hsuan Yang

Temporal Cycle-Consistency Learning
Debidatta Dwibedi, Yusuf Aytar, Jonathan Tompson, Pierre Sermanet, Andrew Zisserman

Depth-Aware Video Frame Interpolation
Wenbo Bao, Wei-Sheng Lai, Chao Ma, Xiaoyun Zhang, Zhiyong Gao, Ming-Hsuan Yang

MnasNet: Platform-Aware Neural Architecture Search for Mobile
Mingxing Tan, Bo Chen, Ruoming Pang, Vijay Vasudevan, Mark Sandler, Andrew Howard, Quoc V. Le

A Compact Embedding for Facial Expression Similarity
Raviteja Vemulapalli, Aseem Agarwala

Contrastive Adaptation Network for Unsupervised Domain Adaptation
Guoliang Kang, Lu Jiang, Yi Yang, Alexander G. Hauptmann

DeepLight: Learning Illumination for Unconstrained Mobile Mixed Reality
Chloe LeGendre, Wan-Chun Ma, Graham Fyffe, John Flynn, Laurent Charbonnel, Jay Busch, Paul Debevec

Detect-To-Retrieve: Efficient Regional Aggregation for Image Search
Marvin Teichmann, Andre Araujo, Menglong Zhu, Jack Sim

Fast Object Class Labelling via Speech
Michael Gygli, Vittorio Ferrari

Learning Independent Object Motion From Unlabelled Stereoscopic Videos
Zhe Cao, Abhishek Kar, Christian Hane, Jitendra Malik

Peeking Into the Future: Predicting Future Person Activities and Locations in Videos
Junwei Liang, Lu Jiang, Juan Carlos Niebles, Alexander G. Hauptmann, Li Fei-Fei

SpotTune: Transfer Learning Through Adaptive Fine-Tuning
Yunhui Guo, Honghui Shi, Abhishek Kumar, Kristen Grauman, Tajana Rosing, Rogerio Feris

NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection
Golnaz Ghiasi, Tsung-Yi Lin, Quoc V. Le

Class-Balanced Loss Based on Effective Number of Samples
Yin Cui, Menglin Jia, Tsung-Yi Lin, Yang Song, Serge Belongie

FEELVOS: Fast End-To-End Embedding Learning for Video Object Segmentation
Paul Voigtlaender, Yuning Chai, Florian Schroff, Hartwig Adam, Bastian Leibe, Liang-Chieh Chen

Inserting Videos Into Videos
Donghoon Lee, Tomas Pfister, Ming-Hsuan Yang

Volumetric Capture of Humans With a Single RGBD Camera via Semi-Parametric Learning
Rohit Pandey, Anastasia Tkach, Shuoran Yang, Pavel Pidlypenskyi, Jonathan Taylor, Ricardo Martin-Brualla, Andrea Tagliasacchi, George Papandreou, Philip Davidson, Cem Keskin, Shahram Izadi, Sean Fanello

You Look Twice: GaterNet for Dynamic Filter Selection in CNNs
Zhourong Chen, Yang Li, Samy Bengio, Si Si

Interactive Full Image Segmentation by Considering All Regions Jointly
Eirikur Agustsson, Jasper R. R. Uijlings, Vittorio Ferrari

Large-Scale Interactive Object Segmentation With Human Annotators
Rodrigo Benenson, Stefan Popov, Vittorio Ferrari

Self-Supervised GANs via Auxiliary Rotation Loss
Ting Chen, Xiaohua Zhai, Marvin Ritter, Mario Lučić, Neil Houlsby

Sim-To-Real via Sim-To-Sim: Data-Efficient Robotic Grasping via Randomized-To-Canonical Adaptation Networks
Stephen James, Paul Wohlhart, Mrinal Kalakrishnan, Dmitry Kalashnikov, Alex Irpan, Julian Ibarz, Sergey Levine, Raia Hadsell, Konstantinos Bousmalis

Using Unknown Occluders to Recover Hidden Scenes
Adam B. Yedidia, Manel Baradad, Christos Thrampoulidis, William T. Freeman, Gregory W. Wornell

Workshops
Computer Vision for Global Challenges
Organizers include: Timnit Gebru, Ernest Mwebaze, John Quinn

Deep Vision 2019
Invited speakers include: Pierre Sermanet, Chris Bregler

Landmark Recognition
Organizers include: Andre Araujo, Bingyi Cao, Jack Sim, Tobias Weyand

Image Matching: Local Features and Beyond
Organizers include: Eduard Trulls

3D-WiDGET: Deep GEneraTive Models for 3D Understanding
Invited speakers include: Julien Valentin

Fine-Grained Visual Categorization
Organizers include: Christine Kaeser-Chen
Advisory panel includes: Hartwig Adam

Low-Power Image Recognition Challenge (LPIRC)
Organizers include: Aakanksha Chowdhery, Achille Brighton, Alec Go, Andrew Howard, Bo Chen, Jaeyoun Kim, Jeff Gilbert

New Trends in Image Restoration and Enhancement Workshop and Associated Challenges
Program chairs include: Vivek Kwatra, Peyman Milanfar, Sebastian Nowozin, George Toderici, Ming-Hsuan Yang

Spatio-temporal Action Recognition (AVA) @ ActivityNet Challenge
Organizers include: David Ross, Sourish Chaudhuri, Radhika Marvin, Arkadiusz Stopczynski, Joseph Roth, Caroline Pantofaru, Chen Sun, Cordelia Schmid

Third Workshop on Computer Vision for AR/VR
Organizers include: Sofien Bouaziz, Serge Belongie

DAVIS Challenge on Video Object Segmentation
Organizers include: Jordi Pont-Tuset, Alberto Montes

Efficient Deep Learning for Computer Vision
Invited speakers include: Andrew Howard

Fairness Accountability Transparency and Ethics in Computer Vision
Organizers include: Timnit Gebru, Margaret Mitchell

Precognition Seeing through the Future
Organizers include: Utsav Prabhu

Workshop and Challenge on Learned Image Compression
Organizers include: George Toderici, Michele Covell, Johannes Ballé, Eirikur Agustsson, Nick Johnston

When Blockchain Meets Computer Vision & AI
Invited speakers include: Chris Bregler

Applications of Computer Vision and Pattern Recognition to Media Forensics
Organizers include: Paul Natsev, Christoph Bregler

Tutorials
Towards Relightable Volumetric Performance Capture of Humans
Organizers include: Sean Fanello, Christoph Rhemann, Graham Fyffe, Jonathan Taylor, Sofien Bouaziz, Paul Debevec, Shahram Izadi

Learning Representations via Graph-structured Networks
Organizers include: Ming-Hsuan Yang

Source: Google AI Blog


Google at ICML 2019



Machine learning is a key strategic focus at Google, with highly active groups pursuing research in virtually all aspects of the field, including deep learning and more classical algorithms, exploring theory as well as application. We utilize scalable tools and architectures to build machine learning systems that enable us to solve deep scientific and engineering challenges in areas of language, speech, translation, music, visual processing and more.

As a leader in machine learning research, Google is proud to be a Sapphire Sponsor of the thirty-sixth International Conference on Machine Learning (ICML 2019), a premier annual event supported by the International Machine Learning Society taking place this week in Long Beach, CA. With nearly 200 Googlers attending the conference to present publications and host workshops, we look forward to our continued collaboration with the larger machine learning research community.

If you're attending ICML 2019, we hope you'll visit the Google booth to learn more about the exciting work, creativity and fun that goes into solving some of the field's most interesting challenges, with researchers on hand to talk about Google Research Football Environment, AdaNet, Robotics at Google and much more. You can learn more about the Google research being presented at ICML 2019 in the list below (Google affiliations highlighted in blue).

ICML 2019 Committees
Board Members include: Andrew McCallum, Corinna Cortes, Hugo Larochelle, William Cohen (Emeritus)

Senior Area Chairs include: Charles Sutton, Claudio Gentile, Corinna Cortes, Kevin Murphy, Mehryar Mohri, Nati Srebro, Samy Bengio, Surya Ganguli

Area Chairs include: Jacob Abernethy, William Cohen, Dumitru Erhan, Cho-Jui Hsieh, Chelsea Finn, Sergey Levine, Manzil Zaheer, Sergei Vassilvitskii, Boqing Gong, Been Kim, Dale Schuurmans, Danny Tarlow, Dustin Tran, Hanie Sedghi, Honglak Lee, Jasper Snoek, Lihong Li, Minmin Chen, Mohammad Norouzi, Nicolas Le Roux, Phil Long, Sanmi Koyejo, Timnit Gebru, Vitaly Feldman, Satyen Kale, Katherine Heller, Hossein Mobahi, Amir Globerson, Ilya Tolstikhin, Marco Cuturi, Sebastian Nowozin, Amin Karbasi, Ohad Shamir, Graham Taylor

Accepted Publications
Learning to Groove with Inverse Sequence Transformations
Jon Gillick, Adam Roberts, Jesse Engel, Douglas Eck, David Bamman

Metric-Optimized Example Weights
Sen Zhao, Mahdi Milani Fard, Harikrishna Narasimhan, Maya Gupta

HOList: An Environment for Machine Learning of Higher Order Logic Theorem Proving
Kshitij Bansal, Sarah Loos, Markus Rabe, Christian Szegedy, Stewart Wilcox

Learning to Clear the Market
Weiran Shen, Sebastien Lahaie, Renato Paes Leme

Shape Constraints for Set Functions
Andrew Cotter, Maya Gupta, Heinrich Jiang, Erez Louidor, James Muller, Tamann Narayan, Serena Wang, Tao Zhu

Self-Attention Generative Adversarial Networks
Han Zhang, Ian Goodfellow, Dimitris Metaxas, Augustus Odena

High-Fidelity Image Generation With Fewer Labels
Mario Lučić, Michael Tschannen, Marvin Ritter, Xiaohua Zhai, Olivier Bachem, Sylvain Gelly

Learning Optimal Linear Regularizers
Matthew Streeter

DeepMDP: Learning Continuous Latent Space Models for Representation Learning
Carles Gelada, Saurabh Kumar, Jacob Buckman, Ofir Nachum, Marc G. Bellemare

kernelPSI: a Post-Selection Inference Framework for Nonlinear Variable Selection
Lotfi Slim, Clément Chatelain, Chloe-Agathe Azencott, Jean-Philippe Vert

Learning from a Learner
Alexis Jacq, Matthieu Geist, Ana Paiva, Olivier Pietquin

Rate Distortion For Model Compression:From Theory To Practice
Weihao Gao, Yu-Han Liu, Chong Wang, Sewoong Oh

An Investigation into Neural Net Optimization via Hessian Eigenvalue Density
Behrooz Ghorbani, Shankar Krishnan, Ying Xiao

Graph Matching Networks for Learning the Similarity of Graph Structured Objects
Yujia Li, Chenjie Gu, Thomas Dullien, Oriol Vinyals, Pushmeet Kohli

Subspace Robust Wasserstein Distances
François-Pierre Paty, Marco Cuturi

Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints
Andrew Cotter, Maya Gupta, Heinrich Jiang, Nathan Srebro, Karthik Sridharan, Serena Wang, Blake Woodworth, Seungil You

The Effect of Network Width on Stochastic Gradient Descent and Generalization: an Empirical Study
Daniel Park, Jascha Sohl-Dickstein, Quoc Le, Samuel Smith

A Theory of Regularized Markov Decision Processes
Matthieu Geist, Bruno Scherrer, Olivier Pietquin

Area Attention
Yang Li, Łukasz Kaiser, Samy Bengio, Si Si

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Mingxing Tan, Quoc Le

Static Automatic Batching In TensorFlow
Ashish Agarwal

The Evolved Transformer
David So, Quoc Le, Chen Liang

Policy Certificates: Towards Accountable Reinforcement Learning
Christoph Dann, Lihong Li, Wei Wei, Emma Brunskill

Self-similar Epochs: Value in Arrangement
Eliav Buchnik, Edith Cohen, Avinatan Hasidim, Yossi Matias

The Value Function Polytope in Reinforcement Learning
Robert Dadashi, Marc G. Bellemare, Adrien Ali Taiga, Nicolas Le Roux, Dale Schuurmans

Adversarial Examples Are a Natural Consequence of Test Error in Noise
Justin Gilmer, Nicolas Ford, Nicholas Carlini, Ekin Cubuk

SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning
Marvin Zhang, Sharad Vikram, Laura Smith, Pieter Abbeel, Matthew Johnson, Sergey Levine

Garbage In, Reward Out: Bootstrapping Exploration in Multi-Armed Bandits
Branislav Kveton, Csaba Szepesvari, Sharan Vaswani, Zheng Wen, Tor Lattimore, Mohammad Ghavamzadeh

Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition
Yao Qin, Nicholas Carlini, Garrison Cottrell, Ian Goodfellow, Colin Raffel

Direct Uncertainty Prediction for Medical Second Opinions
Maithra Raghu, Katy Blumer, Rory Sayres, Ziad Obermeyer, Bobby Kleinberg, Sendhil Mullainathan, Jon Kleinberg

A Large-Scale Study on Regularization and Normalization in GANs
Karol Kurach, Mario Lučić, Xiaohua Zhai, Marcin Michalski, Sylvain Gelly

Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling
Shanshan Wu, Alex Dimakis, Sujay Sanghavi, Felix Yu, Daniel Holtmann-Rice, Dmitry Storcheus, Afshin Rostamizadeh, Sanjiv Kumar

NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks
Yandong Li, Lijun Li, Liqiang Wang, Tong Zhang, Boqing Gong

Distributed Weighted Matching via Randomized Composable Coresets
Sepehr Assadi, Mohammad Hossein Bateni, Vahab Mirrokni

Monge blunts Bayes: Hardness Results for Adversarial Training
Zac Cranko, Aditya Menon, Richard Nock, Cheng Soon Ong, Zhan Shi, Christian Walder

Generalized Majorization-Minimization
Sobhan Naderi Parizi, Kun He, Reza Aghajani, Stan Sclaroff, Pedro Felzenszwalb

NAS-Bench-101: Towards Reproducible Neural Architecture Search
Chris Ying, Aaron Klein, Eric Christiansen, Esteban Real, Kevin Murphy, Frank Hutter

Variational Russian Roulette for Deep Bayesian Nonparametrics
Kai Xu, Akash Srivastava, Charles Sutton

Surrogate Losses for Online Learning of Stepsizes in Stochastic Non-Convex Optimization
Zhenxun Zhuang, Ashok Cutkosky, Francesco Orabona

Improved Parallel Algorithms for Density-Based Network Clustering
Mohsen Ghaffari, Silvio Lattanzi, Slobodan Mitrović

The Advantages of Multiple Classes for Reducing Overfitting from Test Set Reuse
Vitaly Feldman, Roy Frostig, Moritz Hardt

Submodular Streaming in All Its Glory: Tight Approximation, Minimum Memory and Low Adaptive Complexity
Ehsan Kazemi, Marko Mitrovic, Morteza Zadimoghaddam, Silvio Lattanzi, Amin Karbasi

Hiring Under Uncertainty
Manish Purohit, Sreenivas Gollapudi, Manish Raghavan

A Tree-Based Method for Fast Repeated Sampling of Determinantal Point Processes
Jennifer Gillenwater, Alex Kulesza, Zelda Mariet, Sergei Vassilvtiskii

Statistics and Samples in Distributional Reinforcement Learning
Mark Rowland, Robert Dadashi, Saurabh Kumar, Remi Munos, Marc G. Bellemare, Will Dabney

Provably Efficient Maximum Entropy Exploration
Elad Hazan, Sham Kakade, Karan Singh, Abby Van Soest

Active Learning with Disagreement Graphs
Corinna Cortes, Giulia DeSalvo,, Mehryar Mohri, Ningshan Zhang, Claudio Gentile

MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing
Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, Aram Galstyan

Understanding the Impact of Entropy on Policy Optimization
Zafarali Ahmed, Nicolas Le Roux, Mohammad Norouzi, Dale Schuurmans

Matrix-Free Preconditioning in Online Learning
Ashok Cutkosky, Tamas Sarlos

State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations
Alex Lamb, Jonathan Binas, Anirudh Goyal, Sandeep Subramanian, Ioannis Mitliagkas, Yoshua Bengio, Michael Mozer

Online Convex Optimization in Adversarial Markov Decision Processes
Aviv Rosenberg, Yishay Mansour

Bounding User Contributions: A Bias-Variance Trade-off in Differential Privacy
Kareem Amin, Alex Kulesza, Andres Munoz Medina, Sergei Vassilvtiskii

Complementary-Label Learning for Arbitrary Losses and Models
Takashi Ishida, Gang Niu, Aditya Menon, Masashi Sugiyama

Learning Latent Dynamics for Planning from Pixels
Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, James Davidson

Unifying Orthogonal Monte Carlo Methods
Krzysztof Choromanski, Mark Rowland, Wenyu Chen, Adrian Weller

Differentially Private Learning of Geometric Concepts
Haim Kaplan, Yishay Mansour, Yossi Matias, Uri Stemmer

Online Learning with Sleeping Experts and Feedback Graphs
Corinna Cortes, Giulia DeSalvo, Claudio Gentile, Mehryar Mohri, Scott Yang

Adaptive Scale-Invariant Online Algorithms for Learning Linear Models
Michal Kempka, Wojciech Kotlowski, Manfred K. Warmuth

TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing
Augustus Odena, Catherine Olsson, David Andersen, Ian Goodfellow

Online Control with Adversarial Disturbances
Naman Agarwal, Brian Bullins, Elad Hazan, Sham Kakade, Karan Singh

Adversarial Online Learning with Noise
Alon Resler, Yishay Mansour

Escaping Saddle Points with Adaptive Gradient Methods
Matthew Staib, Sashank Reddi, Satyen Kale, Sanjiv Kumar, Suvrit Sra

Fairness Risk Measures
Robert Williamson, Aditya Menon

DBSCAN++: Towards Fast and Scalable Density Clustering
Jennifer Jang, Heinrich Jiang

Learning Linear-Quadratic Regulators Efficiently with only √T Regret
Alon Cohen, Tomer Koren, Yishay Mansour

Understanding and correcting pathologies in the training of learned optimizers
Luke Metz, Niru Maheswaranathan, Jeremy Nixon, Daniel Freeman, Jascha Sohl-Dickstein

Parameter-Efficient Transfer Learning for NLP
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, Sylvain Gelly

Efficient Full-Matrix Adaptive Regularization
Naman Agarwal, Brian Bullins, Xinyi Chen, Elad Hazan, Karan Singh, Cyril Zhang, Yi Zhang

Efficient On-Device Models Using Neural Projections
Sujith Ravi

Flexibly Fair Representation Learning by Disentanglement
Elliot Creager, David Madras, Joern-Henrik Jacobsen, Marissa Weis, Kevin Swersky, Toniann Pitassi, Richard Zemel

Recursive Sketches for Modular Deep Learning
Badih Ghazi, Rina Panigrahy, Joshua Wang

POLITEX: Regret Bounds for Policy Iteration Using Expert Prediction
Yasin Abbasi-Yadkori, Peter L. Bartlett, Kush Bhatia, Nevena Lazić, Csaba Szepesvári, Gellért Weisz

Anytime Online-to-Batch, Optimism and Acceleration
Ashok Cutkosky

Insertion Transformer: Flexible Sequence Generation via Insertion Operations
Mitchell Stern, William Chan, Jamie Kiros, Jakob Uszkoreit

Robust Inference via Generative Classifiers for Handling Noisy Labels
Kimin Lee, Sukmin Yun, Kibok Lee, Honglak Lee, Bo Li, Jinwoo Shin

A Better k-means++ Algorithm via Local Search
Silvio Lattanzi, Christian Sohler

Analyzing and Improving Representations with the Soft Nearest Neighbor Loss
Nicholas Frosst, Nicolas Papernot, Geoffrey Hinton

Learning to Generalize from Sparse and Underspecified Rewards
Rishabh Agarwal, Chen Liang, Dale Schuurmans, Mohammad Norouzi

MeanSum: A Neural Model for Unsupervised Multi-Document Abstractive Summarization
Eric Chu, Peter Liu

CHiVE: Varying Prosody in Speech Synthesis with a Linguistically Driven Dynamic Hierarchical Conditional Variational Network
Tom Kenter, Vincent Wan, Chun-An Chan, Rob Clark, Jakub Vit

Similarity of Neural Network Representations Revisited
Simon Kornblith, Mohammad Norouzi, Honglak Lee, Geoffrey Hinton

Online Algorithms for Rent-Or-Buy with Expert Advice
Sreenivas Gollapudi, Debmalya Panigrahi

Breaking the Softmax Bottleneck via Learnable Monotonic Pointwise Non-linearities
Octavian Ganea, Sylvain Gelly, Gary Becigneul, Aliaksei Severyn

Non-monotone Submodular Maximization with Nearly Optimal Adaptivity and Query Complexity
Matthew Fahrbach, Vahab Mirrokni, Morteza Zadimoghaddam

Agnostic Federated Learning
Mehryar Mohri, Gary Sivek, Ananda Theertha Suresh

Categorical Feature Compression via Submodular Optimization
Mohammad Hossein Bateni, Lin Chen, Hossein Esfandiari, Thomas Fu, Vahab Mirrokni, Afshin Rostamizadeh

Cross-Domain 3D Equivariant Image Embeddings
Carlos Esteves, Avneesh Sud, Zhengyi Luo, Kostas Daniilidis, Ameesh Makadia

Faster Algorithms for Binary Matrix Factorization
Ravi Kumar, Rina Panigrahy, Ali Rahimi, David Woodruff

On Variational Bounds of Mutual Information
Ben Poole, Sherjil Ozair, Aaron Van Den Oord, Alex Alemi, George Tucker

Guided Evolutionary Strategies: Augmenting Random Search with Surrogate Gradients
Niru Maheswaranathan, Luke Metz, George Tucker, Dami Choi, Jascha Sohl-Dickstein

Semi-Cyclic Stochastic Gradient Descent
Hubert Eichner, Tomer Koren, Brendan McMahan, Nathan Srebro, Kunal Talwar

Workshops
1st Workshop on Understanding and Improving Generalization in Deep Learning
Organizers Include: Dilip Krishnan, Hossein Mobahi
Invited Speaker: Chelsea Finn

Climate Change: How Can AI Help?
Invited Speaker: John Platt

Generative Modeling and Model-Based Reasoning for Robotics and AI
Organizers Include: Dumitru Erhan, Sergey Levine, Kimberly Stachenfeld
Invited Speaker: Chelsea Finn

Human In the Loop Learning (HILL)
Organizers Include: Been Kim

ICML 2019 Time Series Workshop
Organizers Include: Vitaly Kuznetsov

Joint Workshop on On-Device Machine Learning & Compact Deep Neural Network Representations (ODML-CDNNR)
Organizers Include: Sujith Ravi, Zornitsa Kozareva

Negative Dependence: Theory and Applications in Machine Learning
Organizers Include: Jennifer Gillenwater, Alex Kulesza

Reinforcement Learning for Real Life
Organizers Include: Lihong Li
Invited Speaker: Craig Boutilier

Uncertainty and Robustness in Deep Learning
Organizers Include: Justin Gilmer

Theoretical Physics for Deep Learning
Organizers Include: Jaehoon Lee, Jeffrey Pennington, Yasaman Bahri

Workshop on the Security and Privacy of Machine Learning
Organizers Include: Nicolas Papernot
Invited Speaker: Been Kim

Exploration in Reinforcement Learning Workshop
Organizers Include: Benjamin Eysenbach, Surya Bhupatiraju, Shixiang Gu

ICML Workshop on Imitation, Intent, and Interaction (I3)
Organizers Include: Sergey Levine, Chelsea Finn
Invited Speaker: Pierre Sermanet

Identifying and Understanding Deep Learning Phenomena
Organizers Include: Hanie Sedghi, Samy Bengio, Kenji Hata, Maithra Raghu, Ali Rahimi, Ying Xiao

Workshop on Multi-Task and Lifelong Reinforcement Learning
Organizers Include: Sarath Chandar, Chelsea Finn
Invited Speakers: Karol Hausman, Sergey Levine

Workshop on Self-Supervised Learning
Organizers Include: Pierre Sermanet

Invertible Neural Networks and Normalizing Flows
Organizers Include: Rianne Van den Berg, Danilo J. Rezende
Invited Speakers: Eric Jang, Laurent Dinh

Source: Google AI Blog


Google at ICLR 2019



This week, New Orleans, LA hosts the 7th International Conference on Learning Representations (ICLR 2019), a conference focused on how one can learn meaningful and useful representations of data for machine learning. ICLR offers conference and workshop tracks, both of which include invited talks along with oral and poster presentations of some of the latest research on deep learning, metric learning, kernel learning, compositional models, non-linear structured prediction and issues regarding non-convex optimization.

At the forefront of innovation in neural networks and deep learning, Google focuses on on both theory and application, developing learning approaches to understand and generalize. As Platinum Sponsor of ICLR 2019, Google will have a strong presence with over 200 researchers attending, contributing to and learning from the broader academic research community by presenting papers and posters, in addition to participating on organizing committees and in workshops.

If you are attending ICLR 2019, we hope you'll stop by our booth and chat with our researchers about the projects and opportunities at Google that go into solving interesting problems for billions of people. You can also learn more about our research being presented at ICLR 2019 in the list below (Googlers highlighted in blue).

Officers and Board Members
Hugo Larochelle, Samy Bengio, Tara Sainath

General Chair
Tara Sainath

Workshop Chairs
Been Kim, Graham Taylor

Program Committee includes:
Chelsea Finn, Dale Schuurmans, Dumitru Erhan, Katherine Heller, Lihong Li, Samy Bengio, Rohit Prabhavalkar, Alex Wiltschko, Slav Petrov, George Dahl

Oral Contributions
Generating High Fidelity Images with Subscale Pixel Networks and Multidimensional Upscaling
Jacob Menick, Nal Kalchbrenner

Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset
Curtis Hawthorne, Andrew Stasyuk, Adam Roberts, Ian Simon, Anna Huang, Sander Dieleman, Erich Elsen, Jesse Engel, Douglas Eck

Meta-Learning Update Rules for Unsupervised Representation Learning
Luke Metz, Niru Maheswaranathan, Brian Cheung, Jascha Sohl-Dickstein

Posters
A Data-Driven and Distributed Approach to Sparse Signal Representation and Recovery
Ali Mousavi, Gautam Dasarathy, Richard G. Baraniuk

Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes
Roman Novak, Lechao Xiao, Yasaman Bahri, Jaehoon Lee, Greg Yang, Jiri Hron, Daniel A. Abolafia, Jeffrey Pennington, Jascha Sohl-Dickstein

Diversity-Sensitive Conditional Generative Adversarial Networks
Dingdong Yang, Seunghoon Hong, Yunseok Jang, Tianchen Zhao, Honglak Lee

Diversity and Depth in Per-Example Routing Models
Prajit Ramachandran, Quoc V. Le

Eidetic 3D LSTM: A Model for Video Prediction and Beyond
Yunbo Wang, Lu Jiang, Ming-Hsuan Yang, Li-Jia Li, Mingsheng Long, Li Fei-Fei

GANSynth: Adversarial Neural Audio Synthesis
Jesse Engel, Kumar Krishna Agrawal, Shuo Chen, Ishaan Gulrajani, Chris Donahue, Adam Roberts

K for the Price of 1: Parameter-efficient Multi-task and Transfer Learning
Pramod Kaushik Mudrakarta, Mark Sandler, Andrey Zhmoginov, Andrew Howard

Learning to Describe Scenes with Programs
Yunchao Liu, Zheng Wu, Daniel Ritchie, William Freeman, Joshua B Tenenbaum, Jiajun Wu

Learning to Infer and Execute 3D Shape Programs
Yonglong Tian, Andrew Luo, Xingyuan Sun, Kevin Ellis, William Freeman, Joshua B Tenenbaum, Jiajun Wu

The Singular Values of Convolutional Layers
Hanie Sedghi, Vineet Gupta, Philip M. Long

Unsupervised Discovery of Parts, Structure, and Dynamics
Zhenjia Xu, Zhijian Liu, Chen Sun, Kevin Murphy, William Freeman, Joshua B Tenenbaum, Jiajun Wu

Adversarial Reprogramming of Neural Networks
Gamaleldin Elsayed, Ian Goodfellow (no longer at Google), Jascha Sohl-Dickstein

Discriminator Rejection Sampling
Ian Goodfellow (no longer at Google), Jascha Sohl-Dickstein

On Self Modulation for Generative Adversarial Networks
Ting Chen, Mario Lucic, Neil Houlsby, Sylvain Gelly

Towards GAN Benchmarks Which Require Generalization
Ishaan Gulrajani, Colin Raffel, Luke Metz

Understanding and Improving Interpolation in Autoencoders via an Adversarial Regularizer
David Berthelot, Colin Raffel, Aurko Roy, Ian Goodfellow (no longer at Google)

A new dog learns old tricks: RL finds classic optimization algorithms
Weiwei Kong, Christopher Liaw, Aranyak Mehta, D. Sivakumar

Contingency-Aware Exploration in Reinforcement Learning
Jongwook Choi, Yijie Guo, Marcin Moczulski, Junhyuk Oh, Neal Wu, Mohammad Norouzi, Honglak Lee

Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation Learning
Ilya Kostrikov, Kumar Krishna Agrawal, Debidatta Dwibedi, Sergey Levine, Jonathan Tompson

Diversity is All You Need: Learning Skills without a Reward Function
Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, Sergey Levine

Episodic Curiosity through Reachability
Nikolay Savinov, Anton Raichuk, Raphael Marinier, Damien Vincent, Marc Pollefeys, Timothy Lillicrap, Sylvain Gelly

Learning to Navigate the Web
Izzeddin Gur, Ulrich Rueckert, Aleksandra Faust, Dilek Hakkani-Tur

Meta-Learning Probabilistic Inference for Prediction
Jonathan Gordon, John Bronskill, Matthias Bauer, Sebastian Nowozin, Richard E. Turner

Multi-step Retriever-Reader Interaction for Scalable Open-domain Question Answering
Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, Andrew McCallum

Near-Optimal Representation Learning for Hierarchical Reinforcement Learning
Ofir Nachum, Shixiang Gu, Honglak Lee, Sergey Levine

Neural Logic Machines
Honghua Dong, Jiayuan Mao, Tian Lin, Chong Wang, Lihong Li, Dengyong Zhou

Neural Program Repair by Jointly Learning to Localize and Repair
Marko Vasic, Aditya Kanade, Petros Maniatis, David Bieber, Rishabh Singh

Optimal Completion Distillation for Sequence Learning
Sara Sabour, William Chan, Mohammad Norouzi

Recall Traces: Backtracking Models for Efficient Reinforcement Learning
Anirudh Goyal, Philemon Brakel, William Fedus, Soumye Singhal, Timothy Lillicrap, Sergey Levine, Hugo Larochelle, Yoshua Bengio

Sample Efficient Adaptive Text-to-Speech
Yutian Chen, Yannis M Assael, Brendan Shillingford, David Budden, Scott Reed, Heiga Zen, Quan Wang, Luis C. Cobo, Andrew Trask, Ben Laurie, Caglar Gulcehre, Aaron van den Oord, Oriol Vinyals, Nando de Freitas

Synthetic Datasets for Neural Program Synthesis
Richard Shin, Neel Kant, Kavi Gupta, Chris Bender, Brandon Trabucco, Rishabh Singh, Dawn Song

The Laplacian in RL: Learning Representations with Efficient Approximations
Yifan Wu, George Tucker, Ofir Nachum

A Mean Field Theory of Batch Normalization
Greg Yang, Jeffrey Pennington, Vinay Rao, Jascha Sohl-Dickstein, Samuel S Schoenholz

Efficient Training on Very Large Corpora via Gramian Estimation
Walid Krichene, Nicolas Mayoraz, Steffen Rendle, Li Zhang, Xinyang Yi, Lichan Hong, Ed Chi, John Anderson

Predicting the Generalization Gap in Deep Networks with Margin Distributions
Yiding Jiang, Dilip Krishnan, Hossein Mobahi, Samy Bengio

InfoBot: Transfer and Exploration via the Information Bottleneck
Anirudh Goyal, Riashat Islam, DJ Strouse, Zafarali Ahmed, Hugo Larochelle, Matthew Botvinick, Sergey Levine, Yoshua Bengio

AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks
Bo Chang, Minmin Chen, Eldad Haber, Ed H. Chi

Complement Objective Training
Hao-Yun Chen, Pei-Hsin Wang, Chun-Hao Liu, Shih-Chieh Chang, Jia-Yu Pan, Yu-Ting Chen, Wei Wei, Da-Cheng Juan

DOM-Q-NET: Grounded RL on Structured Language
Sheng Jia, Jamie Kiros, Jimmy Ba

From Language to Goals: Inverse Reinforcement Learning for Vision-Based Instruction Following
Justin Fu, Anoop Korattikara Balan, Sergey Levine, Sergio Guadarrama

Harmonic Unpaired Image-to-image Translation
Rui Zhang, Tomas Pfister, Li-Jia Li

Hierarchical Generative Modeling for Controllable Speech Synthesis
Wei-Ning Hsu, Yu Zhang, Ron Weiss, Heiga Zen, Yonghui Wu, Yuxuan Wang, Yuan Cao, Ye Jia, Zhifeng Chen, Jonathan Shen, Patrick Nguyen, Ruoming Pang

Learning Finite State Representations of Recurrent Policy Networks
Anurag Koul, Alan Fern, Samuel Greydanus

Learning to Screen for Fast Softmax Inference on Large Vocabulary Neural Networks
Patrick Chen, Si Si, Sanjiv Kumar, Yang Li, Cho-Jui Hsieh

Music Transformer: Generating Music with Long-Term Structure
Chen-Zhi Anna Huang, Ashish Vaswani, Jakob Uszkoreit, Ian Simon, Curtis Hawthorne, Noam Shazeer, Andrew Dai, Matthew D Hoffman, Monica Dinculescu, Douglas Eck

Universal Transformers
Mostafa Dehghani, Stephan Gouws, Oriol Vinyals, Jakob Uszkoreit, Lukasz Kaiser

What do you learn from context? Probing for sentence structure in contextualized word representations
Ian Tenney, Patrick Xia, Berlin Chen, Alex Wang, Adam Poliak, Tom McCoy, Najoung Kim, Benjamin Van Durme, Samuel R. Bowman, Dipanjan Das, Ellie Pavlick

Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives
George Tucker, Dieterich Lawson, Shixiang Gu, Chris J. Maddison

How Important Is a Neuron?
Kedar Dhamdhere, Mukund Sundararajan, Qiqi Yan

Integer Networks for Data Compression with Latent-Variable Models
Johannes Ballé, Nick Johnston, David Minnen

Modeling Uncertainty with Hedged Instance Embeddings
Seong Joon Oh, Andrew Gallagher, Kevin Murphy, Florian Schroff, Jiyan Pan, Joseph Roth

Preventing Posterior Collapse with delta-VAEs
Ali Razavi, Aaron van den Oord, Ben Poole, Oriol Vinyals

Spectral Inference Networks: Unifying Deep and Spectral Learning
David Pfau, Stig Petersen, Ashish Agarwal, David GT Barrett, Kimberly L Stachenfeld

Spreading vectors for similarity search
Alexandre Sablayrolles, Matthijs Douze, Cordelia Schmid, Hervé Jégou

Stochastic Prediction of Multi-Agent Interactions from Partial Observations
Chen Sun, Per Karlsson, Jiajun Wu, Joshua B Tenenbaum, Kevin Murphy

Workshops
Learning from Limited Labeled Data
Sponsored by Google

Deep Reinforcement Learning Meets Structured Prediction
Organizing Committee includes: Chen Liang
Invited Speaker: Mohammad Norouzi

Debugging Machine Learning Models
Organizing Committee includes: D. Sculley
Invited Speaker: Dan Moldovan

Structure & Priors in Reinforcement Learning (SPiRL)
Organizing Committee includes: Chelsea Finn

Task-Agnostic Reinforcement Learning (TARL)
Sponsored by Google
Organizing Committee includes: Danijar Hafner, Marc G. Bellemare
Invited Speaker: Chelsea Finn

AI for Social Good
Program Committee includes: Ernest Mwebaze

Safe Machine Learning Specification, Robustness and Assurance
Program Committee includes: Nicholas Carlini

Representation Learning on Graphs and Manifolds
Program Committee includes: Bryan Perozzi

Source: Google AI Blog


Google at NeurIPS 2018



This week, Montréal hosts the 32nd annual Conference on Neural Information Processing Systems (NeurIPS 2018), the biggest machine learning conference of the year. The conference includes invited talks, demonstrations and presentations of some of the latest in machine learning research. Google will have a strong presence at NeurIPS 2018, with more than 400 Googlers attending in order to contribute to, and learn from, the broader academic research community via talks, posters, workshops, competitions and tutorials. We will be presenting work that pushes the boundaries of what is possible in language understanding, translation, speech recognition and visual & audio perception, with Googlers co-authoring nearly 100 accepted papers (see below).

At the forefront of machine learning, Google is actively exploring virtually all aspects of the field spanning both theory and applications. This research is often inspired by real product needs but increasingly more often driven by scientific curiosity. Given the range of research projects that we pursue, we have found it useful to define a new framework which helps crystalize the goals of projects and allows us to measure progress and success in appropriate ways. Our contributions to NeurIPS and to the broader research community in general are integral to our research mission.

If you are attending NeurIPS 2018, we hope you’ll stop by our booth and chat with our researchers about the projects and opportunities at Google that go into solving the world's most challenging research problems, and to see demonstrations of some of the exciting research we pursue. You can also learn more about our work being presented in the list below (Googlers highlighted in blue).

Google is a Platinum Sponsor of NeurIPS 2018.

NeurIPS Foundation Board
Corinna Cortes, John C. Platt, Fernando Pereira

NeurIPS Organizing Committee
General Chair: Samy Bengio
Program Co-Chair: Hugo Larochelle
Party Chair: Douglas Eck
Diversity and Inclusion Co-Chair: Katherine A. Heller

NeurIPS Program Committee
Senior Area Chairs include:Angela Yu, Claudio Gentile, Cordelia Schmid, Corinna Cortes, Csaba Szepesvari, Dale Schuurmans, Elad Hazan, Mehryar Mohri, Raia Hadsell, Satyen Kale, Yishay Mansour, Afshin Rostamizadeh, Alex Kulesza

Area Chairs include: Amin Karbasi, Amir Globerson, Amit Daniely, Andras Gyorgy, Andriy Mnih, Been Kim, Branislav Kveton, Ce Liu, D Sculley, Danilo Rezende, Danny TarlowDavid Balduzzi, Denny Zhou, Dilan Gorur, Dumitru Erhan, George Dahl, Graham Taylor, Ian Goodfellow, Jasper Snoek, Jean-Philippe Vert, Jia Deng, Jon Shlens, Karen Simonyan, Kevin Swersky, Kun Zhang, Lihong Li, Marc G. Bellemare, Marco Cuturi, Maya Gupta, Michael BowlingMichalis Titsias, Mohammad Norouzi, Mouhamadou Moustapha Cisse, Nicolas Le Roux, Remi Munos, Sanjiv Kumar, Sanmi Koyejo, Sergey Levine, Silvia Chiappa, Slav PetrovSurya Ganguli, Timnit Gebru, Timothy Lillicrap, Viren Jain, Vitaly Feldman, Vitaly Kuznetsov

Workshops Program Committee includes: Mehryar Mohri, Sergey Levine

Accepted Papers
3D-Aware Scene Manipulation via Inverse Graphics
Shunyu Yao, Tzu Ming Harry Hsu, Jun-Yan Zhu, Jiajun Wu, Antonio Torralba, William T. Freeman, Joshua B. Tenenbaum

A Retrieve-and-Edit Framework for Predicting Structured Outputs
Tatsunori Hashimoto, Kelvin Guu, Yonatan Oren, Percy Liang

Adversarial Attacks on Stochastic Bandits
Kwang-Sung Jun, Lihong Li, Yuzhe Ma, Xiaojin Zhu

Adversarial Examples that Fool both Computer Vision and Time-Limited Humans
Gamaleldin F. Elsayed, Shreya Shankar, Brian Cheung, Nicolas Papernot, Alex Kurakin, Ian Goodfellow, Jascha Sohl-Dickstein

Adversarially Robust Generalization Requires More Data
Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, Aleksander Madry

Are GANs Created Equal? A Large-Scale Study
Mario Lucic, Karol Kurach, Marcin Michalski, Olivier Bousquet, Sylvain Gelly

Collaborative Learning for Deep Neural Networks
Guocong Song, Wei Chai

Completing State Representations using Spectral Learning
Nan Jiang, Alex Kulesza, Santinder Singh

Content Preserving Text Generation with Attribute Controls
Lajanugen Logeswaran, Honglak Lee, Samy Bengio

Context-aware Synthesis and Placement of Object Instances
Donghoon Lee, Sifei Liu, Jinwei Gu, Ming-Yu Liu, Ming-Hsuan Yang, Jan Kautz

Co-regularized Alignment for Unsupervised Domain Adaptation
Abhishek Kumar, Prasanna Sattigeri, Kahini Wadhawan, Leonid Karlinsky, Rogerlo Feris, William T. Freeman, Gregory Wornell

cpSGD: Communication-efficient and differentially-private distributed SGD
Naman Agarwal, Ananda Theertha Suresh, Felix Yu, Sanjiv Kumar, H. Brendan Mcmahan

Data Center Cooling Using Model-Predictive Control
Nevena Lazic, Craig Boutilier, Tyler Lu, Eehern Wong, Binz Roy, MK Ryu, Greg Imwalle

Data-Efficient Hierarchical Reinforcement Learning
Ofir Nachum, Shixiang Gu, Honglak Lee, Sergey Levine

Deep Attentive Tracking via Reciprocative Learning
Shi Pu, Yibing Song, Chao Ma, Honggang Zhang, Ming-Hsuan Yang

Generalizing Point Embeddings Using the Wasserstein Space of Elliptical Distributions
Boris Muzellec, Marco Cuturi

GLoMo: Unsupervised Learning of Transferable Relational Graphs
Zhilin Yang, Jake (Junbo) Zhao, Bhuwan Dhingra, Kaiming He, William W. Cohen, Ruslan Salakhutdinov, Yann LeCun

GroupReduce: Block-Wise Low-Rank Approximation for Neural Language Model Shrinking
Patrick Chen, Si Si, Yang Li, Ciprian Chelba, Cho-Jui Hsieh

Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic Corrections
Xin Zhang, Armando Solar-Lezama, Rishabh Singh

Learning Hierarchical Semantic Image Manipulation through Structured Representations
Seunghoon Hong, Xinchen Yan, Thomas Huang, Honglak Lee

Learning Temporal Point Processes via Reinforcement Learning
Shuang Li, Shuai Xiao, Shixiang Zhu, Nan Du, Yao Xie, Le Song

Learning Towards Minimum Hyperspherical Energy
Weiyang Liu, Rongmei Lin, Zhen Liu, Lixin Liu, Zhiding Yu, Bo Dai, Le Song

Mesh-TensorFlow: Deep Learning for Supercomputers
Noam Shazeer, Youlong Cheng, Niki Parmar, Dustin Tran, Ashish Vaswani, Penporn Koanantakool, Peter Hawkins, HyoukJoong Lee, Mingsheng Hong, Cliff Young, Ryan Sepassi, Blake Hechtman

MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcare
Edward Choi, Cao Xiao, Walter F. Stewart, Jimeng Sun

Searching for Efficient Multi-Scale Architectures for Dense Image Prediction
Liang-Chieh Chen, Maxwell D. Collins, Yukun Zhu, George Papandreou, Barret Zoph, Florian Schroff, Hartwig Adam, Jonathon Shlens

SplineNets: Continuous Neural Decision Graphs
Cem Keskin, Shahram Izadi

Task-Driven Convolutional Recurrent Models of the Visual System
Aran Nayebi, Daniel Bear, Jonas Kubilius, Kohitij Kar, Surya Ganguli, David Sussillo, James J. DiCarlo, Daniel L. K. Yamins

To Trust or Not to Trust a Classifier
Heinrich Jiang, Been Kim, Melody Guan, Maya Gupta

Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis
Ye Jia, Yu Zhang, Ron J. Weiss, Quan Wang, Jonathan Shen, Fei Ren, Zhifeng Chen, Patrick Nguyen, Ruoming Pang, Ignacio Lopez Moreno, Yonghui Wu

Algorithms and Theory for Multiple-Source Adaptation
Judy Hoffman, Mehryar Mohri, Ningshan Zhang

A Lyapunov-based Approach to Safe Reinforcement Learning
Yinlam Chow, Ofir Nachum, Edgar Duenez-Guzman, Mohammad Ghavamzadeh

Adaptive Methods for Nonconvex Optimization
Manzil Zaheer, Sashank Reddi, Devendra Sachan, Satyen Kale, Sanjiv Kumar

Assessing Generative Models via Precision and Recall
Mehdi S. M. Sajjadi, Olivier Bachem, Mario Lucic, Olivier Bousquet, Sylvain Gelly

A Loss Framework for Calibrated Anomaly Detection
Aditya Menon, Robert Williamson

Blockwise Parallel Decoding for Deep Autoregressive Models
Mitchell Stern, Noam Shazeer, Jakob Uszkoreit

Breaking the Curse of Horizon: Infinite-Horizon Off-Policy Estimation
Qiang Liu, Lihong Li, Ziyang Tang, Dengyong Zhou

Contextual Pricing for Lipschitz Buyers
Jieming Mao, Renato Leme, Jon Schneider

Coupled Variational Bayes via Optimization Embedding
Bo Dai, Hanjun Dai, Niao He, Weiyang Liu, Zhen Liu, Jianshu Chen, Lin Xiao, Le Song

Data Amplification: A Unified and Competitive Approach to Property Estimation
Yi HAO, Alon Orlitsky, Ananda Theertha Suresh, Yihong Wu

Deep Network for the Integrated 3D Sensing of Multiple People in Natural Images
Elisabeta Marinoiu, Mihai Zanfir, Alin-Ionut Popa, Cristian Sminchisescu

Deep Non-Blind Deconvolution via Generalized Low-Rank Approximation
Wenqi Ren, Jiawei Zhang, Lin Ma, Jinshan Pan, Xiaochun Cao, Wei Liu, Ming-Hsuan Yang

Diminishing Returns Shape Constraints for Interpretability and Regularization
Maya Gupta, Dara Bahri, Andrew Cotter, Kevin Canini

DropBlock: A Regularization Method for Convolutional Networks
Golnaz Ghiasi, Tsung-Yi Lin, Quoc V. Le

Generalization Bounds for Uniformly Stable Algorithms
Vitaly Feldman, Jan Vondrak

Geometrically Coupled Monte Carlo Sampling
Mark Rowland, Krzysztof Choromanski, Francois Chalus, Aldo Pacchiano, Tamas Sarlos, Richard E. Turner, Adrian Weller

GILBO: One Metric to Measure Them All
Alexander A. Alemi, Ian Fischer

Insights on Representational Similarity in Neural Networks with Canonical Correlation
Ari S. Morcos, Maithra Raghu, Samy Bengio

Improving Online Algorithms via ML Predictions
Manish Purohit, Zoya Svitkina, Ravi Kumar

Learning to Exploit Stability for 3D Scene Parsing
Yilun Du, Zhijan Liu, Hector Basevi, Ales Leonardis, William T. Freeman, Josh Tenembaum, Jiajun Wu

Maximizing Induced Cardinality Under a Determinantal Point Process
Jennifer Gillenwater, Alex Kulesza, Sergei Vassilvitskii, Zelda Mariet

Memory Augmented Policy Optimization for Program Synthesis and Semantic Parsing
Chen Liang, Mohammad Norouzi, Jonathan Berant, Quoc V. Le, Ni Lao

PCA of High Dimensional Random Walks with Comparison to Neural Network Training
Joseph M. Antognini, Jascha Sohl-Dickstein

Predictive Approximate Bayesian Computation via Saddle Points
Yingxiang Yang, Bo Dai, Negar Kiyavash, Niao He

Recurrent World Models Facilitate Policy Evolution
David Ha, Jürgen Schmidhuber

Sanity Checks for Saliency Maps
Julius Adebayo, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, Been Kim

Simple, Distributed, and Accelerated Probabilistic Programming
Dustin Tran, Matthew Hoffman, Dave Moore, Christopher Suter, Srinivas Vasudevan, Alexey Radul, Matthew Johnson, Rif A. Saurous

Tangent: Automatic Differentiation Using Source-Code Transformation for Dynamically Typed Array Programming
Bart van Merriënboer, Dan Moldovan, Alex Wiltschko

The Emergence of Multiple Retinal Cell Types Through Efficient Coding of Natural Movies
Samuel A. Ocko, Jack Lindsey, Surya Ganguli, Stephane Deny

The Everlasting Database: Statistical Validity at a Fair Price
Blake Woodworth, Vitaly Feldman, Saharon Rosset, Nathan Srebro

The Spectrum of the Fisher Information Matrix of a Single-Hidden-Layer Neural Network
Jeffrey Pennington, Pratik Worah

A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks
Kimin Lee, Kibok Lee, Honglak Lee, Jinwoo Shin

Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language
Matthew D. Hoffman, Matthew Johnson, Dustin Tran

A Bayesian Nonparametric View on Count-Min Sketch
Diana Cai, Michael Mitzenmacher, Ryan Adams (no longer at Google)

Automatic Differentiation in ML: Where We are and Where We Should be Going
Bart van Merriënboer, Olivier Breuleux, Arnaud Bergeron, Pascal Lamblin

Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures
Sergey Bartunov, Adam Santoro, Blake A. Richards, Geoffrey E. Hinton, Timothy P. Lillicrap

Deep Generative Models for Distribution-Preserving Lossy Compression
Michael Tschannen, Eirikur Agustsson, Mario Lucic

Deep Structured Prediction with Nonlinear Output Transformations
Colin Graber, Ofer Meshi, Alexander Schwing

Discovery of Latent 3D Keypoints via End-to-end Geometric Reasoning
Supasorn Suwajanakorn, Noah Snavely, Jonathan Tompson, Mohammad Norouzi

Transfer Learning with Neural AutoML
Catherine Wong, Neil Houlsby, Yifeng Lu, Andrea Gesmundo

Efficient Gradient Computation for Structured Output Learning with Rational and Tropical Losses
Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri, Dmitry Storcheus, Scott Yang

Cooperative neural networks (CoNN): Exploiting prior independence structure for improved classification
Harsh Shrivastava, Eugene Bart, Bob Price, Hanjun Dai, Bo Dai, Srinivas Aluru

Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization
Blake Woodworth, Jialei Wang, Brendan McMahan, Nathan Srebro

Hierarchical Reinforcement Learning for Zero-shot Generalization with Subtask Dependencies
Sungryull Sohn, Junhyuk Oh, Honglak Lee

Human-in-the-Loop Interpretability Prior
Isaac Lage, Andrew Slavin Ross, Been Kim, Samuel J. Gershman, Finale Doshi-Velez

Joint Autoregressive and Hierarchical Priors for Learned Image Compression
David Minnen, Johannes Ballé, George D Toderici

Large-Scale Computation of Means and Clusters for Persistence Diagrams Using Optimal Transport
Théo Lacombe, Steve Oudot, Marco Cuturi

Learning to Reconstruct Shapes from Unseen Classes
Xiuming Zhang, Zhoutong Zhang, Chengkai Zhang, Joshua B. Tenenbaum, William T. Freeman, Jiajun Wu

Large Margin Deep Networks for Classification
Gamaleldin Fathy Elsayed, Dilip Krishnan, Hossein Mobahi, Kevin Regan, Samy Bengio

Mallows Models for Top-k Lists
Flavio Chierichetti, Anirban Dasgupta, Shahrzad Haddadan, Ravi Kumar, Silvio Lattanzi

Meta-Learning MCMC Proposals
Tongzhou Wang, YI WU, Dave Moore, Stuart Russell

Non-delusional Q-Learning and Value-Iteration
Tyler Lu, Dale Schuurmans, Craig Boutilier

Online Learning of Quantum States
Scott Aaronson, Xinyi Chen, Elad Hazan, Satyen Kale, Ashwin Nayak

Online Reciprocal Recommendation with Theoretical Performance Guarantees
Fabio Vitale, Nikos Parotsidis, Claudio Gentile

Optimal Algorithms for Continuous Non-monotone Submodular and DR-Submodular Maximization
Rad Niazadeh, Tim Roughgarden, Joshua R. Wang

Policy Regret in Repeated Games
Raman Arora, Michael Dinitz, Teodor Vanislavov Marinov, Mehryar Mohri

Provable Variational Inference for Constrained Log-Submodular Models
Josip Djolonga, Stefanie Jegelka, Andreas Krause

Realistic Evaluation of Deep Semi-Supervised Learning Algorithms
Avital Oliver, Augustus Odena, Colin Raffel, Ekin D. Cubuk, Ian J. Goodfellow

Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion
Jacob Buckman, Danijar Hafner, George Tucker, Eugene Brevdo, Honglak Lee

Visual Object Networks: Image Generation with Disentangled 3D Representations
JunYan Zhu, Zhoutong Zhang, Chengkai Zhang, Jiajun Wu, Antonio Torralba, Josh Tenenbaum, William T. Freeman

Watch Your Step: Learning Node Embeddings via Graph Attention
Sami Abu-El-Haija, Bryan Perozzi, Rami AlRfou, Alexander Alemi

Workshops
2nd Workshop on Machine Learning on the Phone and Other Consumer Devices
Co-Chairs include: Sujith Ravi, Wei Chai, Hrishikesh Aradhye

Bayesian Deep Learning
Workshop Organizers include: Kevin Murphy

Continual Learning
Workshop Organizers include: Marc Pickett

The Second Conversational AI Workshop – Today's Practice and Tomorrow's Potential
Workshop Organizers include: Dilek Hakkani-Tur

Visually Grounded Interaction and Language
Workshop Organizers include: Olivier Pietquin

Workshop on Ethical, Social and Governance Issues in AI
Workshop Organizers include: D. Sculley

AI for Social Good
Workshop Program Committee includes: Samuel Greydanus

Black in AI
Workshop Organizers: Mouhamadou Moustapha Cisse, Timnit Gebru
Program Committee: Irwan Bello, Samy Bengio, Ian Goodfellow, Hugo Larochelle, Margaret Mitchell

Interpretability and Robustness in Audio, Speech, and Language
Workshop Organizers include: Ehsan Variani, Bhuvana Ramabhadran

LatinX in AI
Workshop Organizers includes: Pablo Samuel Castro
Program Committee includes: Sergio Guadarrama

Machine Learning for Systems
Workshop Organizers include: Anna Goldie, Azalia Mirhoseini, Kevin Swersky, Milad Hashemi
Program Committee includes: Simon Kornblith, Nicholas Frosst, Amir Yazdanbakhsh, Azade Nazi, James Bradbury, Sharan Narang, Martin Maas, Carlos Villavieja

Queer in AI
Workshop Organizers include: Raphael Gontijo Lopes

Second Workshop on Machine Learning for Creativity and Design
Workshop Organizers include: Jesse Engel, Adam Roberts

Workshop on Security in Machine Learning
Workshop Organizers include: Nicolas Papernot

Tutorial
Visualization for Machine Learning
Fernanda Viégas, Martin Wattenberg

Source: Google AI Blog


Google at EMNLP 2018



This week, the annual conference on Empirical Methods in Natural Language Processing (EMNLP 2018) will be held in Brussels, Belgium. Google will have a strong presence at EMNLP with several of our researchers presenting their research on a diverse set of topics, including language identification, segmentation, semantic parsing and question answering, additionally serving in various levels of organization in the conference. Googlers will also be presenting their papers and participating in the co-located Conference on Computational Natural Language Learning (CoNLL 2018) shared task on multilingual parsing.

In addition to this involvement, we are sharing several new datasets with the academic community that are released with papers published at EMNLP, with the goal of accelerating progress in empirical natural language processing (NLP). These releases are designed to help account for mismatches between the datasets a machine learning model is trained and tested on, and the inputs an NLP system would be asked to handle “in the wild”. All of the datasets we are releasing include realistic, naturally occurring text, and fall into two main categories: 1) challenge sets for well-studied core NLP tasks (part-of-speech tagging, coreference) and 2) datasets to encourage new directions of research on meaning preservation under rephrasings/edits (query well-formedness, split-and-rephrase, atomic edits):
  • Noun-Verb Ambiguity in POS Tagging Dataset: English part-of-speech taggers regularly make egregious errors related to noun-verb ambiguity, despite high accuracies on standard datasets. For example: in “Mark which area you want to distress” several state-of-the-art taggers annotate “Mark” as a noun instead of a verb. We release a new dataset of over 30,000 naturally occurring non-trivial annotated examples of noun-verb ambiguity. Taggers previously indistinguishable from each other have accuracies ranging from 57% to 75% accuracy on this challenge set.
  • Query Wellformedness Dataset: Web search queries are usually “word-salad” style queries with little resemblance to natural language questions (“barack obama height” as opposed to “What is the height of Barack Obama?”). Differentiating a natural language question from a query is of importance to several applications include dialogue. We annotate and release 25,100 queries from the open-source Paralex corpus with ratings on how close they are to well-formed natural language questions.
  • WikiSplit: Split and Rephrase Dataset Extracted from Wikipedia Edits: We extract examples of sentence splits from Wikipedia edits where one sentence gets split into two sentences that together preserve the original meaning of the sentence (E.g. “Street Rod is the first in a series of two games released for the PC and Commodore 64 in 1989.” is split into “Street Rod is the first in a series of two games.” and “It was released for the PC and Commodore 64 in 1989.”) The released corpus contains one million sentence splits with a vocabulary of more than 600,000 words. 
  • WikiAtomicEdits: A Multilingual Corpus of Atomic Wikipedia Edits: Information about how people edit language in Wikipedia can be used to understand the structure of language itself. We pay particular attention to two atomic edits: insertions and deletions that consist of a single contiguous span of text. We extract around 43 million such edits in 8 languages and show that they provide valuable information about entailment and discourse. For example, insertion of “in 1949” adds a prepositional phrase to the sentence “She died there after a long illness” resulting in “She died there in 1949 after a long illness”.
These datasets join the others that Google has recently released, such as Conceptual Captions and GAP Coreference Resolution in addition to our past contributions.

Below is a full list of Google’s involvement and publications being presented at EMNLP and CoNLL (Googlers highlighted in blue). We are particularly happy to announce that the paper “Linguistically-Informed Self-Attention for Semantic Role Labeling” was awarded one of the two Best Long Paper awards. This work was done by our 2017 intern Emma Strubell, Googlers Daniel Andor, David Weiss and Google Faculty Advisor Andrew McCallum. We congratulate these authors, and all other researchers who are presenting their work at the conference.

Area Chairs Include:
Ming-Wei Chang, Marius Pasca, Slav Petrov, Emily Pitler, Meg Mitchell, Taro Watanabe

EMNLP Publications
A Challenge Set and Methods for Noun-Verb Ambiguity
Ali Elkahky, Kellie Webster, Daniel Andor, Emily Pitler

A Fast, Compact, Accurate Model for Language Identification of Codemixed Text
Yuan Zhang, Jason Riesa, Daniel Gillick, Anton Bakalov, Jason Baldridge, David Weiss

AirDialogue: An Environment for Goal-Oriented Dialogue Research
Wei Wei, Quoc Le, Andrew Dai, Jia Li

Content Explorer: Recommending Novel Entities for a Document Writer
Michal Lukasik, Richard Zens

Deep Relevance Ranking using Enhanced Document-Query Interactions
Ryan McDonald, George Brokos, Ion Androutsopoulos

HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, Christopher D. Manning

Identifying Well-formed Natural Language Questions
Manaal Faruqui, Dipanjan Das

Learning To Split and Rephrase From Wikipedia Edit History
Jan A. Botha, Manaal Faruqui, John Alex, Jason Baldridge, Dipanjan Das

Linguistically-Informed Self-Attention for Semantic Role Labeling
Emma Strubell, Patrick Verga, Daniel Andor, David Weiss, Andrew McCallum

Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text
Haitian Sun, Bhuwan Dhingra, Manzil Zaheer, Kathryn Mazaitis, Ruslan Salakhutdinov, William Cohen

Noise Contrastive Estimation for Conditional Models: Consistency and Statistical Efficiency
Zhuang Ma, Michael Collins

Part-of-Speech Tagging for Code-Switched, Transliterated Texts without Explicit Language Identification
Kelsey Ball, Dan Garrette

Phrase-Indexed Question Answering: A New Challenge for Scalable Document Comprehension
Minjoon Seo, Tom Kwiatkowski, Ankur P. Parikh, Ali Farhadi, Hannaneh Hajishirzi

Policy Shaping and Generalized Update Equations for Semantic Parsing from Denotations
Dipendra Misra, Ming-Wei Chang, Xiaodong He, Wen-tau Yih

Revisiting Character-Based Neural Machine Translation with Capacity and Compression
Colin Cherry, George Foster, Ankur Bapna, Orhan Firat, Wolfgang Macherey

Self-governing neural networks for on-device short text classification
Sujith Ravi, Zornitsa Kozareva

Semi-Supervised Sequence Modeling with Cross-View Training
Kevin Clark, Minh-Thang Luong, Christopher D. Manning, Quoc Le

State-of-the-art Chinese Word Segmentation with Bi-LSTMs
Ji Ma, Kuzman Ganchev, David Weiss

Subgoal Discovery for Hierarchical Dialogue Policy Learning
Da Tang, Xiujun Li, Jianfeng Gao, Chong Wang, Lihong Li, Tony Jebara

SwitchOut: an Efficient Data Augmentation Algorithm for Neural Machine Translation
Xinyi Wang, Hieu Pham, Zihang Dai, Graham Neubig

The Importance of Generation Order in Language Modeling
Nicolas Ford, Daniel Duckworth, Mohammad Norouzi, George Dahl

Training Deeper Neural Machine Translation Models with Transparent Attention
Ankur Bapna, Mia Chen, Orhan Firat, Yuan Cao, Yonghui Wu

Understanding Back-Translation at Scale
Sergey Edunov, Myle Ott, Michael Auli, David Grangier

Unsupervised Natural Language Generation with Denoising Autoencoders
Markus Freitag, Scott Roy

WikiAtomicEdits: A Multilingual Corpus of Wikipedia Edits for Modeling Language and Discourse
Manaal Faruqui, Ellie Pavlick, Ian Tenney, Dipanjan Das

WikiConv: A Corpus of the Complete Conversational History of a Large Online Collaborative Community
Yiqing Hua, Cristian Danescu-Niculescu-Mizil, Dario Taraborelli, Nithum Thain, Jeffery Sorensen, Lucas Dixon

EMNLP Demos
SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing
Taku Kudo, John Richardson

Universal Sentence Encoder for English
Daniel Cer, Yinfei Yang, Sheng-yi Kong, Nan Hua, Nicole Limtiaco, Rhomni St. John, Noah Constant, Mario Guajardo-Cespedes, Steve Yuan, Chris Tar, Brian Strope, Ray Kurzweil

CoNLL Shared Task
Multilingual Parsing from Raw Text to Universal Dependencies
Slav Petrov, co-organizer

Universal Dependency Parsing with Multi-Treebank Models
Aaron Smith, Bernd Bohnet, Miryam de Lhoneux, Joakim Nivre, Yan Shao, Sara Stymne
(Winner of the Universal POS Tagging and Morphological Tagging subtasks, using the open-sourced Meta-BiLSTM tagger)

CoNLL Publication
Sentence-Level Fluency Evaluation: References Help, But Can Be Spared!
Katharina Kann, Sascha Rothe, Katja Filippova

Source: Google AI Blog


Google at ICML 2018



Machine learning is a key strategic focus at Google, with highly active groups pursuing research in virtually all aspects of the field, including deep learning and more classical algorithms, exploring theory as well as application. We utilize scalable tools and architectures to build machine learning systems that enable us to solve deep scientific and engineering challenges in areas of language, speech, translation, music, visual processing and more.

As a leader in machine learning research, Google is proud to be a Platinum Sponsor of the thirty-fifth International Conference on Machine Learning (ICML 2018), a premier annual event supported by the International Machine Learning Society taking place this week in Stockholm, Sweden. With over 130 Googlers attending the conference to present publications and host workshops, we look forward to our continued collaboration with the larger ML research community.

If you're attending ICML 2018, we hope you'll visit the Google booth and talk with our researchers to learn more about the exciting work, creativity and fun that goes into solving some of the field's most interesting challenges. Our researchers will also be available to talk about TensorFlow Hub, the latest work from the Magenta project, a Q&A session on the Google AI Residency program and much more. You can also learn more about our research being presented at ICML 2018 in the list below (Googlers highlighted in blue).

ICML 2018 Committees
Board Members include: Andrew McCallumCorinna CortesHugo LarochelleWilliam Cohen
Sponsorship Co-Chair: Ryan Adams

Accepted Publications
Predict and Constrain: Modeling Cardinality in Deep Structured Prediction
Nataly Brukhim, Amir Globerson

Quickshift++: Provably Good Initializations for Sample-Based Mean Shift
Heinrich Jiang, Jennifer Jang, Samory Kpotufe

Learning a Mixture of Two Multinomial Logits
Flavio Chierichetti, Ravi KumarAndrew Tomkins

Structured Evolution with Compact Architectures for Scalable Policy Optimization
Krzysztof Choromanski, Mark Rowland, Vikas Sindhwani, Richard E Turner, Adrian Weller

Fixing a Broken ELBO
Alexander Alemi, Ben Poole, Ian FischerJoshua DillonRif SaurousKevin Murphy

Hierarchical Long-term Video Prediction without Supervision
Nevan Wichers, Ruben Villegas, Dumitru ErhanHonglak Lee

Self-Consistent Trajectory Autoencoder: Hierarchical Reinforcement Learning with Trajectory Embeddings
John Co-Reyes, Yu Xuan Liu, Abhishek Gupta, Benjamin Eysenbach, Pieter Abbeel, Sergey Levine

Well Tempered Lasso
Yuanzhi Li, Yoram Singer

Programmatically Interpretable Reinforcement Learning
Abhinav Verma, Vijayaraghavan Murali, Rishabh Singh, Pushmeet Kohli, Swarat Chaudhuri

Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks
Lechao XiaoYasaman BahriJascha Sohl-DicksteinSamuel SchoenholzJeffrey Pennington

On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization
Sanjeev Arora, Nadav Cohen, Elad Hazan

Scalable Deletion-Robust Submodular Maximization: Data Summarization with Privacy and Fairness Constraints
Ehsan Kazemi, Morteza Zadimoghaddam, Amin Karbasi

Data Summarization at Scale: A Two-Stage Submodular Approach
Marko Mitrovic, Ehsan Kazemi, Morteza Zadimoghaddam, Amin Karbasi

Machine Theory of Mind
Neil Rabinowitz, Frank Perbet, Francis Song, Chiyuan Zhang, S. M. Ali Eslami, Matthew Botvinick

Learning to Optimize Combinatorial Functions
Nir Rosenfeld, Eric Balkanski, Amir Globerson, Yaron Singer

Proportional Allocation: Simple, Distributed, and Diverse Matching with High Entropy
Shipra Agarwal, Morteza ZadimoghaddamVahab Mirrokni

Path Consistency Learning in Tsallis Entropy Regularized MDPs
Yinlam Chow, Ofir NachumMohammad Ghavamzadeh

Efficient Neural Architecture Search via Parameters Sharing
Hieu Pham, Melody Guan, Barret ZophQuoc LeJeff Dean

Adafactor: Adaptive Learning Rates with Sublinear Memory Cost
Noam Shazeer, Mitchell Stern

Learning Memory Access Patterns
Milad HashemiKevin SwerskyJamie Smith, Grant Ayers, Heiner Litz, Jichuan Chang, Christos Kozyrakis, Parthasarathy Ranganathan

SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation
Bo Dai, Albert Shaw, Lihong Li, Lin Xiao, Niao He, Zhen Liu, Jianshu Chen, Le Song

Scalable Bilinear Pi Learning Using State and Action Features
Yichen Chen, Lihong Li, Mengdi Wang

Distributed Asynchronous Optimization with Unbounded Delays: How Slow Can You Go?
Zhengyuan Zhou, Panayotis Mertikopoulos, Nicholas Bambos, Peter Glynn, Yinyu Ye, Li-Jia Li, Li Fei-Fei

Shampoo: Preconditioned Stochastic Tensor Optimization
Vineet Gupta, Tomer Koren, Yoram Singer

Parallel and Streaming Algorithms for K-Core Decomposition
Hossein Esfandiari, Silvio LattanziVahab Mirrokni

Can Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games?
Maithra RaghuAlexander Irpan, Jacob Andreas, Bobby Kleinberg, Quoc Le, Jon Kleinberg

Is Generator Conditioning Causally Related to GAN Performance?
Augustus OdenaJacob BuckmanCatherine OlssonTom BrownChristopher OlahColin RaffelIan Goodfellow

The Mirage of Action-Dependent Baselines in Reinforcement Learning
George TuckerSurya Bhupatiraju, Shixiang Gu, Richard E Turner, Zoubin Ghahramani, Sergey Levine

MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels
Lu Jiang, Zhengyuan Zhou, Thomas Leung, Li-Jia LiLi Fei-Fei

Loss Decomposition for Fast Learning in Large Output Spaces
En-Hsu Yen, Satyen KaleFelix Xinnan YuDaniel Holtmann-RiceSanjiv Kumar, Pradeep Ravikumar

A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music
Adam RobertsJesse EngelColin RaffelCurtis HawthorneDouglas Eck

Smoothed Action Value Functions for Learning Gaussian Policies
Ofir NachumMohammad NorouziGeorge TuckerDale Schuurmans

Fast Decoding in Sequence Models Using Discrete Latent Variables
Lukasz KaiserSamy BengioAurko RoyAshish VaswaniNiki ParmarJakob UszkoreitNoam Shazeer

Accelerating Greedy Coordinate Descent Methods
Haihao Lu, Robert Freund, Vahab Mirrokni

Approximate Leave-One-Out for Fast Parameter Tuning in High Dimensions
Shuaiwen Wang, Wenda Zhou, Haihao Lu, Arian Maleki, Vahab Mirrokni

Image Transformer
Niki Parmar, Ashish Vaswani, Jakob Uszkoreit, Lukasz Kaiser, Noam Shazeer, Alexander Ku, Dustin Tran

Towards End-to-End Prosody Transfer for Expressive Speech Synthesis with Tacotron
RJ Skerry-Ryan, Eric Battenberg, Ying Xiao, Yuxuan Wang, Daisy Stanton, Joel Shor, Ron Weiss, Robert Clark, Rif Saurous

Dynamical Isometry and a Mean Field Theory of RNNs: Gating Enables Signal Propagation in Recurrent Neural Networks
Minmin Chen, Jeffrey Pennington,, Samuel Schoenholz

Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis
Yuxuan Wang, Daisy Stanton, Yu Zhang, RJ Skerry-Ryan, Eric Battenberg, Joel ShorYing Xiao, Ye Jia, Fei Ren, Rif Saurous

Constrained Interacting Submodular Groupings
Andrew CotterMahdi Milani FardSeungil YouMaya Gupta, Jeff Bilmes

Reinforcing Adversarial Robustness using Model Confidence Induced by Adversarial Training
Xi Wu, Uyeong Jang, Jiefeng Chen, Lingjiao Chen, Somesh Jha

Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)
Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viégas, Rory Sayres

Online Learning with Abstention
Corinna CortesGiulia DeSalvoClaudio GentileMehryar Mohri, Scott Yang

Online Linear Quadratic Control
Alon CohenAvinatan HasidimTomer KorenNevena LazicYishay MansourKunal Talwar

Competitive Caching with Machine Learned Advice
Thodoris Lykouris, Sergei Vassilvitskii

Efficient Neural Audio Synthesis
Nal Kalchbrenner, Erich Elsen, Karen Simonyan, Seb Noury, Norman Casagrande, Edward Lockhart, Florian Stimberg, Aäron van den Oord, Sander Dieleman, Koray Kavukcuoglu

Gradient Descent with Identity Initialization Efficiently Learns Positive Definite Linear Transformations by Deep Residual Networks
Peter Bartlett, Dave Helmbold, Phil Long

Understanding and Simplifying One-Shot Architecture Search
Gabriel BenderPieter-Jan KindermansBarret ZophVijay VasudevanQuoc Le

Approximation Algorithms for Cascading Prediction Models
Matthew Streeter

Learning Longer-term Dependencies in RNNs with Auxiliary Losses
Trieu TrinhAndrew DaiThang LuongQuoc Le

Self-Imitation Learning
Junhyuk Oh, Yijie Guo, Satinder Singh, Honglak Lee

Adaptive Sampled Softmax with Kernel Based Sampling
Guy Blanc, Steffen Rendle

Workshops
2018 Workshop on Human Interpretability in Machine Learning (WHI)
Organizers: Been Kim, Kush Varshney, Adrian Weller
Invited Speakers include: Fernanda ViégasMartin Wattenberg

Exploration in Reinforcement Learning
Organizers: Ben EysenbachSurya BhupatirajuShane Gu, Junhyuk Oh, Vincent Vanhoucke, Oriol Vinyals, Doina Precup

Theoretical Foundations and Applications of Deep Generative Models
Invited speakers include: Honglak Lee

Source: Google AI Blog