Intel AI at NeurIPS 2019
December 8 - 14, 2019 in Vancouver, B.C.
Intel is a top sponsor of the 33rd annual Conference on Neural Information Processing Systems (NeurIPS). Each year, thousands of leading academics and researchers converge to exchange research on neural information processing systems in biological, technological, mathematical and theoretical aspects. Stop by Intel AI’s booth to discover our complete AI hardware portfolio, backed by “write once, deploy anywhere” software and groundbreaking research.
NeurIPS 2019 Expo Day: Sunday, December 8th
NeurIPS Expo is a one day event taking place Dec. 8, 2019 prior to the NeurIPS conference to give sponsors a forum to showcase technologies, make announcements, or hold a press conference. Intel AI will be presenting the following talks and demos on Expo Day. Stop by the Intel AI booth #507 in the east exhibition hall afterward.
Exposition Hall: Monday December 9th – Wednesday, December 11th
Visit the Intel booth #507 and learn how Intel AI is breaking new ground in AI research. Join us for in-booth theater presentations, demonstrations, and the opportunity to connect with fellow researchers.
Agenda
Expo Day - Day 1
Sunday December 8, 2019
| Title | Time | Location | Abstract |
|---|---|---|---|
| Intel® Nervana™ NNP: Domain-Specific Architectures for Inference & Training | 9:10am - 9:30am | Vancouver Convention Center | TALK: This talk will cover how we designed flexibility without sacrificing performance with the Intel Nervana NNP for Inference (NNP-I), scalability with the NNP for Training (NNP-T) for the most complex models, and software stacks to enable programmability through standard frameworks. |
| Efficient Deep Learning computing with Intel® Nervana™ Neural Network Processor for Training | 9:00am - 5:30pm | Vancouver Convention Center | DEMO: The NNP-T is designed to maximize efficiency in power usage, memory and communication by increasing compute utilization for AI training needs instead of just peak TOPS. We will demonstrate end-to-end training of an image classification workload, ResNet50, using a popular deep learning framework. |
Accepted Paper Presentations - Day 3
Tuesday December 10, 2019
| Title | Time | Location | Authors | Abstract |
|---|---|---|---|---|
| Deep Equilibrium Models - SPOTLIGHT PAPER | 10:40am - 12:45pm | Vancouver Convention Center | Shaojie Bai – Carnegie Mellon University, J. Zico Kolter – Carnegie Mellon University, Vladlen Koltun – Intel Intelligent Systems Lab | Oral Presentation: |
| Subspace Attack: Exploiting Promising Subspaces for Query-Efficient Black-box Attacks | 10:45am - 12:45pm | East Exhibition Hall B&C Poster #15 | Yiwen Guo – Intel Labs China, Ziang Yan – Tsinghua University, Changshui Zhang – Tsinghua University | View paper › |
| Differentiable Cloth Simulation for Inverse Problems | 10:45am - 12:45pm | East Exhibition Hall B&C Poster #138 | Junbang Liang – University of Maryland, Computer Science, Ming Lin – University of Maryland, Computer Science, Vladlen Koltun – Intel Intelligent Systems Lab | View paper › |
| DATA: Differentiable ArchiTecture Approximation | 5:30pm - 7:30pm | East Exhibition Hall B&C Poster #2 | Jianlong Chang – National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Xinbang Zhang – Institute of Automation, Chinese Academy of Science, Yiwen Guo – Intel Labs China, Gaofeng Meng – Institute of Automation, Chinese Academy of Sciences, Shiming Xiang – Chinese Academy of Sciences, China, Chunhong Pan – Institute of Automation, Chinese Academy of Sciences | View paper › |
| Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model | 5:30pm - 7:30pm | East Exhibition Hall B&C Poster #155 | Atilim Gunes Baydin – University of Oxford, Lei Shao – Intel Corporation, Wahid Bhimji – Berkeley lab, Lukas Heinrich – New York University Saeid Naderiparizi – University of British Columbia, Andreas Munk – University of British Columbia, Jialin Liu – Lawrence Berkeley National Lab, Bradley Gram-Hansen – University of Oxford, Gilles Louppe – University of Liège, Lawrence Meadows – Intel Corporation, Philip Torr – University of Oxford, Victor Lee – Intel Corporation, Kyle Cranmer – New York University, Mr. Prabhat – LBL/NERSC, Frank Wood – University of British Columbia | View paper › |
Accepted Paper Presentations - Day 4
Wednesday December 11, 2019
| Title | Time | Location | Authors | Abstract |
|---|---|---|---|---|
| Modeling Uncertainty by Learning A Hierarchy of Deep Neural Connections | 10:45am - 12:45pm | East Exhibition Hall B&C Poster #45 | Yaniv Gurwic – Intel AI Lab, Shami Nisimov – Intel AI Lab, Gal Novik – Intel AI Lab, Raanan Rohekar – Intel AI Lab |
|
| Post training 4-bit quantization of convolutional networks for rapid-deployment | 10:45am - 12:45pm | East Exhibition Hall B&C Poster #105 | Ron Banner – Intel AI Lab, Yury Nahshan – Intel AI Lab, Daniel Soudry – Technion | Learn more › |
| Untangling in Invariant Speech Recognition | 5:00pm - 7:00pm | East Exhibition Hall B&C Poster #241 | Cory Stephenson – Intel AI Lab, Suchismita Padhy – Intel AI Lab, Hanlin Tang – Intel AI Lab, Oguz Elibol – Intel AI Lab, Jenelle Feather – MIT, Josh McDermott – MIT, SueYeon Chung – MIT | Learn more › |
Accepted Paper Presentations - Day 5
Thursday December 12, 2019
| Title | Time | Location | Authors | Abstract |
|---|---|---|---|---|
| Generalization In Multitask Deep Neural Classifiers A Statistical Physics Approach | 10:45am - 12:45pm | East Exhibition Hall B&C Poster #55 | Tyler Lee – Intel AI Lab, Anthony Ndirango – Intel AI Lab | View paper › |
| Goal-conditioned Imitation Learning | 10:45am - 12:45pm | East Exhibition Hall B&C Poster #229 | Yiming Ding – University of California, Berkeley, Carlos Florensa – UC Berkeley, Pieter Abbeel – UC Berkeley, Mariano Phielipp – Intel AI Lab | View paper › |
| A Zero-Positive Learning Approach for Diagnosing Software Performance Regression | 5:00pm - 7:00pm | East Exhibition Hall B&C Poster #120 | Mejbah Alam – Intel Labs, Justin Gottschlich – Intel Labs, Nesime Tatbul – Intel Labs, Javier Turek – Intel Labs, Timothy Mattson – Intel Labs, Abdullah Muzahid – Intel Labs | Learn more › |
Workshops Accepted Paper Presentations - Day 2
Monday December 9, 2019
| Title | Time | Location | Authors | Abstract |
|---|---|---|---|---|
| Layout Composition from Attributed Scene Graphs | 8:00am - 6:00pm | Women In Machine Learning (WiML) | Subarna Tripathi – Intel AI Lab, Anahita Bhiwandiwalla – Intel AI Lab | Paper › |
| Triplet-Aware Scene Graph Embeddings | 8:00am - 6:00pm |
Women In Machine Learning (WiML) | Brigit Schroeder – Intel AI Lab, Subarna Tripathi – Intel AI Lab, Hanlin Tang – Intel AI Lab | Paper › |
| A Comparison Of Loss Weighting Strategies For Multitask Learning In Deep Neural Networks | 8:00am - 6:00pm | Women In Machine Learning (WiML) |
Ting Gong – Intel AI Lab, Suchismita Padhy – Intel AI Lab, Tyler Lee – Intel AI Lab, Cory Stephenson – Intel AI Lab, Oguz Elibol – Intel AI Lab | Paper › |
| Multimodal Understanding of Passenger Intents in Autonomous Vehicles | 8:00am - 6:00pm |
Women In Machine Learning (WiML) | Eda Okur – Intel Labs, Shachi H. Kumar – Intel Labs, Saurav Sahay – Intel Labs, Lama Nachman – Intel Labs | Paper › |
| Neural Network Autoencoders for Compressed Neuroevolution | 7:00am - 8:00pm | LatinX in AI | Somdeb Majumdar – Intel AI Lab, Santiago Miret – Intel AI Lab |
Workshops Accepted Paper Presentations - Day 6
Friday December 13, 2019
| Title | Time | Location | Authors | Abstract |
|---|---|---|---|---|
| Q8BERT, A 8Bit Quantized BERT | 8:00am - 6:40pm | EMC2: Energy Efficient Machine Learning and Cognitive Computing | Ofir Zafrir – Intel AI Lab, Guy Boudoukh – Intel AI Lab, Peter Izsak – Intel AI Lab, Moshe Wasserblat – Intel AI Lab | |
| Training Compact Models for Low Resource Entity Tagging using Pre-trained Language Models | 8:00am - 6:40pm | EMC2: Energy Efficient Machine Learning and Cognitive Computing | Peter Izsak – Intel AI Lab, Shira Guskin – Intel AI Lab, Moshe Wasserblat – Intel AI Lab | Paper › |
| Improving MFVI in Bayesian Neural Networks with Empirical Bayes: a Study with Diabetic Retinopathy Diagnosis | 8:00am - 6:45pm | Bayesian Deep Learning Workshop | Ranganath Krishnan – Intel Labs, Mahesh Subedar – Intel Labs, Omesh Tickoo – Intel Labs, Angelos Filos – Univ. of Oxford, Yarin Gal – Univ. of Oxford | Paper › |
| Deep Probabilistic Models to Detect Data Poisoning Attacks | 8:00am - 6:45pm | Bayesian Deep Learning Workshop | Mahesh Subedar – Intel Labs, Nilesh Ahuja – Intel Labs, Ranganath Krishnan – Intel Labs, Ibrahima Ndiour – Intel Labs, Omesh Tickoo – Intel Labs | Paper › |
| Probabilistic Modeling of Deep Features for Out-of-Distribution and Adversarial Detection | 8:00am - 6:45pm | Bayesian Deep Learning Workshop | Nilesh Ahuja – Intel Labs, Ibrahima Ndiour – Intel Labs, Trushant Kalyanpur, Omesh Tickoo – Intel Labs | Paper › |
| Leveraging Topics and Audio Features with Multimodal Attention for Audio Visual Scene-Aware Dialog | 8:30am - 6:30pm | Visually Grounded Interaction and Language Workshop | Shachi H. Kumar – Intel Labs, Eda Okur – Intel Labs, Saurav Sahay – Intel Labs, Jonathan Huang – Intel Labs, Lama Nachman – Intel Labs | Paper › |
| LISA: Towards Learned DNA Sequence Search | 8:00am - 6:00pm | Workshop on Systems for Machine Learning | Darryl Ho – MIT, Jialin Ding – MIT, Sanchit Misra – MIT, Nesime Tatbul – Intel Labs, Vikram Nathan – MIT, Vasimuddin Md – Intel Labs, Tim Kraska – MIT | This paper has been selected for an oral presentation. |
| Real-time Approximate Inference for Scene Understanding with Generative Models | 8:00am - 6:00pm | Perception as Generative Reasoning Workshop | Javier Felip Leon – Intel Labs, Nilesh Ahuja – Intel Labs, David Gomez-Gutierrez – Intel Labs, Omesh Tickoo – Intel Labs, Vikash Mansinghka – MIT | Paper › |
Workshops Accepted Paper Presentations - Day 7
Saturday December 14, 2019
| Title | Time | Location | Authors | Abstract |
|---|---|---|---|---|
| Evolutionary Reinforcement Learning for Sample-Efficient Multiagent Coordination | 8:00am - 7:00pm | Deep Reinforcement Learning Workshop | Shauharda Khadka – Intel AI Lab, Somdeb Majumdar – Intel AI Lab, Santiago Miret – Intel AI Lab, Stephen McAleer – Intel AI Lab, Kagan Tumer – Oregon State University | Paper › |
| SEERL: Sample Efficient Ensemble Reinforcement Learning | 8:00am - 7:00pm | Deep Reinforcement Learning Workshop | Rohan Saphal – Indian Institute of Technology Madras, Balaraman Ravindran – Indian Institute of Technology, Madras, Dheevatsa Mudigere – Facebook, Sasikanth Avancha, Bharat Kaul – Intel Labs | |
| Multi-Context Term Embeddings: the Use Case of Corpus-based Term Set Expansion | 8:00am - 6:00pm | Context and Compositionality in Biological and Artificial Neural Systems Workshop | Jonathan Mamou – Intel AI Lab, Oren Pereg – Intel AI Lab, Moshe Wasserblat – Intel AI Lab, Ido Dagan – Bar Ilan University, Israel | Paper › |
| Correlation of Auroral Dynamics and GNSS Scintillation with an Autoencoder | 8:00am - 6:30pm | Machine Learning and the Physical Sciences (ML4PS) Workshop | Kara Lamb – Cooperative Institute for Research in the Environmental Sciences, Garima Malhotra – University of Michigan, Athanasios Vlontzos – Imperial College London, Edward Wagstaff – University of Oxford, Atılım Günes Baydin – University of Oxford, Anahita Bhiwandiwalla – Intel AI Lab, Yarin Gal – University of Oxford, Alfredo Kalaitzis – Element AI, Anthony Reina – Intel AIPG, Asti Bhatt – SRI International | Paper › |
| Learning to Vectorize using Deep Reinforcement Learning | 8:00am - 6:00pm | ML for Systems Workshop | Ameer Haj-Ali, Nesreen Ahmed – Intel Labs, Ted Willke – Intel Labs, Sophia Shao, Krste Asanovic, Ion Stoica | |
| A Weak Supervision Approach to Detecting Visual Anomalies for Automated Testing of Graphics Units | 11:00am - 11:15am | ML for Systems Workshop | Tom Hope, Data-Science Team Lead – Intel IT Advanced Analytics, Adi Szeskin, Data Scientis) – Intel IT Advanced Analytics, Dr. Itay Lieder, Data Scientist – Intel IT Advanced Analytics, Dr. Lev Faivishevsky, Data Scientist – Intel IT Advanced Analytics, and Dr. Amitai Armon, Chief Data Scientist & Principal Engineer – Intel IT Advanced Analytics | Oral Presentation |
| Workshop on Robot Learning | 9:00am - 6:00pm | Workshop on Robot Learning | Siyu Zhou, Mariano Phielipp, Jorge Sefair, Sara Walker, Heni Ben Amor | Paper › |
| Imitation Learning of Robot Policies by Combining Language, Vision and Demonstration | 9:00am - 6:00pm | Workshop on Robot Learning | Simon Stepputtis – Arizona State University, Joseph Campbell, Mariano Phielipp – Intel AI Lab, Chitta Baral – Arizona State University, Heni Ben Amor – Arizona State University | Paper › |
Event Authors
Vladlen Koltun
Director, Intelligent Systems Lab
Yiwen Guo
Research Lead, Cognitive Computing Lab
Lei Shao
Deep Learning Software Engineer, Data Center Group
Yaniv Gurwicz
Research Scientist, Intel AI Lab
Shami Nisimov
Deep Learning R&D Engineer
Gal Novik
Principal Engineer, Intel AI Lab
Raanan Y. Yehezkel Rohekar
Research Scientist, Intel AI Lab
Suchismita Padhy
Deep Learning Data Scientist, Intel AI Lab
Hanlin Tang
Senior Director of the AI Lab, Artificial Intelligence Platforms Group
Oguz Elibol
Deep Learning Data Scientist, Intel AI Lab
Ron Banner
Machine Learning Scientist, Intel AI Lab
Tyler Lee
Deep Learning Data Scientist, Intel AI Lab
Mohammad Mejbah ul Alam
Research Scientist, Parallel Computing Lab
Justin Gottschlich
Head of Machine Programming Research, Intel Labs
Nesime Tatbul
Senior Research Scientist, Parallel Computing Lab
Javier Turek
ML Research Scientist, Brain-Inspired Computing Lab
Mariano Phielipp
Senior Deep Learning Data Scientist, Intel AI Lab
Subarna Tripathi
Deep Learning Data Scientist; Artificial Intelligence Product Group
Anahita Bhiwandiwalla
Deep Learning Researcher and Engineer, Intel AI Lab
Ting Gong
Deep Learning Data Scientist, Intel AI Lab
Eda Okur Kavil
Research Scientist, Anticipatory Computing Lab
Shachi H. Kumar
Research Scientist, Anticipatory Computing Lab
Lama Nachman
Intel Fellow & Director of Anticipatory Computing Lab, Anticipatory Computing Lab
Ofir Zafrir
Deep Learning Data Scientist Intern, Intel AI Lab
Guy Boudoukh
Deep Learning Research, Intel AI Lab
Peter Izsak
Deep Learning Data Scientist, Intel AI Lab, Artificial Intelligence Products Group
Moshe Wasserblat
Principal Engineer, Artificial Intelligence Products Group
Shira Guskin
Deep Learning Software Engineer
Ranganath Krishnan
Research Scientist, Anticipatory Computing Lab
Mahesh Subedar
Research Scientist, Anticipatory Computing Lab
Nilesh Ahuja
Engineer, Anticipatory Computing Lab
Ibrahima Ndiour
Research Scientist, Anticipatory Computing Lab
Jonathan Huang
Research Scientist, Anticipatory Computing Lab
Shauharda Khadka
Deep Learning Researcher, Intel AI Lab
Somdeb Majumdar
Machine Learning Research Lead, Intel AI Lab
Santiago Miret
Deep Learning Researcher, Intel AI Lab
Bharat Kaul
Director, Parallel Computing Lab, Intel Labs India
Jonathan Mamou
Deep Learning Data Scientist, Intel AI Lab
Oren Pereg
Senior Deep Learning Data Scientist, Intel AI Lab, Artificial Intelligence Products Group
Tony Reina
Chief AI Architect for Health and Life Sciences
Nesreen K. Ahmed
Senior Research Scientist, Brain-Inspired Computing Lab
Theodore Willke
Director, Brain-Inspired Computing Lab
Javier Felip Leon
Research Scientist, Anticipatory Computing Lab
Amitai Armon
Chief Data Scientist & Principal Engineer, Intel Advanced Analytics
Tom Hope
AI Research Team Lead, Advanced Analytics
Careers
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