December 8 - 14, 2019 in Vancouver, B.C.

Intel AI at NeurIPS 2019

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.

Intel AI at NeurIPS 2019: December 8 - 14, 2019 in Vancouver, B.C.

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
9:10am - 9:30am
Vancouver Convention Center

Intel® Nervana™ NNP: Domain-Specific Architectures for Inference & Training

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.

9:00am - 5:30pm
Vancouver Convention Center

Efficient Deep Learning computing with Intel® Nervana™ Neural Network Processor for Training

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
10:40am - 12:45pm
Vancouver Convention Center

Deep Equilibrium Models - SPOTLIGHT PAPER

Shaojie Bai – Carnegie Mellon University, J. Zico Kolter – Carnegie Mellon University, Vladlen Koltun – Intel Intelligent Systems Lab

Oral Presentation:
West Ballroom C (10:40am – 10:45am)
Poster Session:
East Exhibition Hall B&C Poster #137 (10:45 am – 12:45 pm)
Paper

10:45am - 12:45pm
East Exhibition Hall B&C Poster #15

Subspace Attack: Exploiting Promising Subspaces for Query-Efficient Black-box Attacks

Yiwen Guo – Intel Labs China, Ziang Yan – Tsinghua University, Changshui Zhang – Tsinghua University

10:45am - 12:45pm
East Exhibition Hall B&C Poster #138

Differentiable Cloth Simulation for Inverse Problems

Junbang Liang – University of Maryland, Computer Science, Ming Lin– University of Maryland, Computer Science, Vladlen Koltun – Intel Intelligent Systems Lab

5:30pm - 7:30pm
East Exhibition Hall B&C Poster #2

DATA: Differentiable ArchiTecture Approximation

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

5:30pm - 7:30pm
East Exhibition Hall B&C Poster #155

Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model

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

Accepted Paper Presentations - Day 4

Wednesday December 11, 2019
10:45am - 12:45pm
East Exhibition Hall B&C Poster #45

Modeling Uncertainty by Learning A Hierarchy of Deep Neural Connections

Yaniv Gurwic – Intel AI Lab, Shami Nisimov – Intel AI Lab, Gal Novik – Intel AI Lab, Raanan Rohekar – Intel AI Lab

10:45am - 12:45pm
East Exhibition Hall B&C Poster #105

Post training 4-bit quantization of convolutional networks for rapid-deployment

Ron Banner – Intel AI Lab, Yury Nahshan – Intel AI Lab, Daniel Soudry – Technion

5:00pm - 7:00pm
East Exhibition Hall B&C Poster #241

Untangling in Invariant Speech Recognition

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

Accepted Paper Presentations - Day 5

Thursday December 12, 2019
10:45am - 12:45pm
East Exhibition Hall B&C Poster #55

Generalization in multitask deep neural classifiers: a statistical physics approach

Tyler Lee – Intel AI Lab, Anthony Ndirango – Intel AI Lab

10:45am - 12:45pm
East Exhibition Hall B&C Poster #229

Goal-conditioned Imitation Learning

Yiming Ding – University of California, Berkeley, Carlos Florensa – UC Berkeley, Pieter Abbeel – UC Berkeley, Mariano Phielipp – Intel AI Lab

5:00pm - 7:00pm
East Exhibition Hall B&C Poster #120

A Zero-Positive Learning Approach for Diagnosing Software Performance Regression

Mejbah Alam- Intel Labs, Justin Gottschlich- Intel Labs, Nesime Tatbul – Intel Labs, Javier Turek- Intel Labs, Timothy Mattson – Intel Labs, Abdullah Muzahid – Intel Labs

Workshops Accepted Paper Presentations - Day 2

Monday December 9, 2019
8:00am - 6:00pm

Layout Composition from Attributed Scene Graphs

Subarna Tripathi – Intel AI Lab, Anahita Bhiwandiwalla – Intel AI Lab

8:00am - 6:00pm

Triplet-Aware Scene Graph Embeddings

Brigit Schroeder – Intel AI Lab, Subarna Tripathi – Intel AI Lab, Hanlin Tang – Intel AI Lab

8:00am - 6:00pm

A comparison of loss weighting strategies for multitask learning in deep neural networks

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

8:00am - 6:00pm

Multimodal Understanding of Passenger Intents in Autonomous Vehicles

Eda Okur – Intel Labs, Shachi H Kumar – Intel Labs, Saurav Sahay – Intel Labs, Lama Nachman – Intel Labs

7:00am - 8:00pm

Neural Network Autoencoders for Compressed Neuroevolution

Somdeb Majumdar – Intel AI Lab, Santiago Miret – Intel AI Lab

Workshops Accepted Paper Presentations - Day 6

Friday December 13, 2019

Q8BERT, A 8Bit Quantized BERT

Ofir Zafrir – Intel AI Lab, Guy Boudoukh – Intel AI Lab, Peter Izsak – Intel AI Lab, Moshe Wasserblat – Intel AI Lab

(Read the blog here: https://www.intel.ai/q8bert/)
Paper

Training Compact Models for Low Resource Entity Tagging using Pre-trained Language Models

Peter Izsak – Intel AI Lab, Shira Guskin – Intel AI Lab, Moshe Wasserblat – Intel AI Lab

8:00am - 6:45pm

Improving MFVI in Bayesian Neural Networks with Empirical Bayes: a Study with Diabetic Retinopathy Diagnosis

Ranganath Krishnan – Intel Labs, Mahesh Subedar – Intel Labs, Omesh Tickoo – Intel Labs, Angelos Filos – Univ. of Oxford, Yarin Gal – Univ. of Oxford

8:00am - 6:45pm

Deep Probabilistic Models to Detect Data Poisoning Attacks

Mahesh Subedar – Intel Labs, Nilesh Ahuja – Intel Labs, Ranganath Krishnan – Intel Labs, Ibrahima Ndiour – Intel Labs, Omesh Tickoo – Intel Labs

8:00am - 6:45pm

Probabilistic Modeling of Deep Features for Out-of-Distribution and Adversarial Detection

Nilesh Ahuja – Intel Labs, Ibrahima Ndiour – Intel Labs, Trushant Kalyanpur, Omesh Tickoo – Intel Labs

Leveraging Topics and Audio Features with Multimodal Attention for Audio Visual Scene-Aware Dialog

Shachi H Kumar – Intel Labs, Eda Okur – Intel Labs, Saurav Sahay – Intel Labs, Jonathan Huang – Intel Labs, Lama Nachman – Intel Labs

LISA: Towards Learned DNA Sequence Search

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.

Paper

Real-time Approximate Inference for Scene Understanding with Generative Models

Javier Felip Leon – Intel Labs, Nilesh Ahuja – Intel Labs, David Gomez-Gutierrez – Intel Labs, Omesh Tickoo – Intel Labs, Vikash Mansinghka – MIT

Workshops Accepted Paper Presentations - Day 7

Saturday December 14, 2019
8:00am - 7:00pm

Evolutionary Reinforcement Learning for Sample-Efficient Multiagent Coordination

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

8:00am - 7:00pm

SEERL: Sample Efficient Ensemble Reinforcement Learning

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

Jonathan Mamou – Intel AI Lab, Oren Pereg – Intel AI Lab, Moshe Wasserblat – Intel AI Lab, Ido Dagan – Bar Ilan University, Israel

Correlation of Auroral Dynamics and GNSS Scintillation with an Autoencoder

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

8:00am - 6:00pm

Learning to Vectorize using Deep Reinforcement Learning

Ameer Haj-Ali, Nesreen Ahmed – Intel Labs, Ted Willke – Intel Labs, Sophia Shao, Krste Asanovic, Ion Stoica

11:00am - 11:15am

A Weak Supervision Approach to Detecting Visual Anomalies for Automated Testing of Graphics Units

Adi Szeskin, Lev Faivishevsky, Ashwin K Muppalla, Amitai Armon, Tom Hope (Intel Advanced Analytics)

Oral Presentation

Paper

9:00am - 6:00pm

Clone Swarms: Learning to Predict and Control Multi-Robot Systems by Imitation

Siyu Zhou, Mariano Phielipp, Jorge Sefair, Sara Walker, Heni Ben Amor

9:00am - 6:00pm

Imitation Learning of Robot Policies by Combining Language, Vision and Demonstration

Simon Stepputtis – Arizona State University, Joseph Campbell, Mariano Phielipp – Intel AI Lab, Chitta Baral – Arizona State University, Heni Ben Amor – Arizona State University

Careers

If you are interested in discovering AI careers that reshape business and society, be sure and stop by our booth and meet our recruiting team or visit the Intel AI careers page where you can explore different roles and join our talent network.

Follow us @IntelAI and @IntelAIResearch for more updates from @NeurIPS and the Intel AI research team!@NeurIPSConf

Event Authors

Vladlen Koltun

Vladlen Koltun

Director, Intelligent Systems Lab

Yiwen Guo

Research Lead, Cognitive Computing Lab

AIDM-524

Lei Shao

Deep Learning Software Engineer, Data Center Group

Yaniv Gurwicz

Yaniv Gurwicz

Research Scientist, Intel AI Lab

Shami Nisimov

Shami Nisimov

Deep Learning R&D Engineer

Gal Novik

Gal Novik

Principal Engineer, Intel AI Lab

Raanan Y. Yehezkel Rohekar

Research Scientist, Intel AI Lab

AIDM-547

Suchismita Padhy

Deep Learning Data Scientist, Intel AI Lab

Hanlin Tang

Hanlin Tang

Principal Engineer, Artificial Intelligence Products Group

Oguz Elibol

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

Anthony Ndirango

Anthony Ndirango

Mohammad Mejbah Alam

Mohammad Mejbah ul Alam

Research Scientist, Parallel Computing Lab

Justin Gottschlich

Justin Gottschlich

Lead Artificial Intelligence Researcher, Parallel Computing Lab

Nesime Tatbul

Senior Research Scientist, Parallel Computing Lab

Javier Turek

ML Research Scientist, Brain-Inspired Computing Lab

Mariano Phielipp

Mariano Phielipp

Senior Deep Learning Data Scientist, Intel AI Lab

Tripathi Subarna

Subarna Tripathi

Deep Learning Data Scientist; Artificial Intelligence Product Group

Anahita Bhiwandiwalla

Anahita Bhiwandiwalla

Deep Learning Researcher and Engineer, Intel AI Lab

Ting Gong

Ting Gong

Deep Learning Data Scientist, Intel AI Lab

Eda Okur Kavil

Eda Okur Kavil

Research Scientist, Anticipatory Computing Lab

Shachi H. Kumar

Research Scientist, Anticipatory Computing Lab

Lama Nachman

Lama Nachman

Intel Fellow & Director of Anticipatory Computing Lab, Anticipatory Computing Lab

Ofir Zafrir

Deep Learning Data Scientist Intern, Intel AI Lab

Guy Boudoukh

Guy Boudoukh

Deep Learning Research, Intel AI Lab

Peter Izssak

Peter Izsak

Deep Learning Data Scientist, Intel AI Lab, Artificial Intelligence Products Group

Moshe Wasserblat

Moshe Wasserblat

Principal Engineer, Artificial Intelligence Products Group

Shira Guskin

Shira Guskin

Deep Learning Software Engineer

AIDM-500

Ranganath Krishnan

Research Scientist, Anticipatory Computing Lab

Mahesh Subedar

Mahesh Subedar

Research Scientist, Anticipatory Computing Lab

Nilesh Ahuja

Nilesh Ahuja

Engineer, Anticipatory Computing Lab

Ibrahima Ndiour

Ibrahima Ndiour

Research Scientist, Anticipatory Computing Lab

Jonathan Huang

Jonathan Huang

Research Scientist, Anticipatory Computing Lab

Shauharda Khadka

Shauharda Khadka

Deep Learning Researcher, Intel AI Lab

Somdeb Majumdar

Machine Learning Research Lead, Intel AI Lab

Santiago Miret

Santiago Miret

Deep Learning Researcher, Intel AI Lab

Bharat Kaul

Bharat Kaul

Director, Parallel Computing Lab, Intel Labs India

Jonathan Mamou

Deep Learning Data Scientist, Intel AI Lab

Oren Pereg

Oren Pereg

Senior Deep Learning Data Scientist, Intel AI Lab, Artificial Intelligence Products Group

Tony Reina

Tony Reina

Data Scientist, Deep Learning Algorithms

Nesreen K. Ahmed

Senior Research Scientist, Brain-Inspired Computing Lab

Theodore Willke

Director, Brain-Inspired Computing Lab

Javier Felip Leon

Javier Felip Leon

Research Scientist, Anticipatory Computing Lab