Intel AI Research at ICLR

The International Conference on Learning Representations (ICLR) is dedicated to the advancement of deep learning. ICLR is globally renowned for presenting and publishing cutting-edge research on all aspects of deep learning used in the fields of artificial intelligence, statistics and data science, as well as applications for machine vision, computational biology, speech recognition, text understanding, gaming, and robotics.

Workshops - Day 1

Monday May 6, 2019

Title Time Location Authors Abstract
Structure & Priors in Reinforcement Learning (SPiRL) 9:00am - 6:00pm New Orleans, LA Varun Kumar, Hanlin Tang – Intel, Arjun Bansal – Intel

Graph-DQN: Fast generalization to novel objects using prior relational knowledge; presented during Poster Session #1 (10:30 – 11:00 AM).

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Task-Agnostic Reinforcement Learning (TARL) Workshop 9:45am - 6:00pm New Orleans, LA

Zach Dwiel, Madhavun Vasu, Mariano Phielipp, Arjun Bansal

Hierarchical Policy Learning is Sensitive to Goal Space Design.

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Limited Labeled Data (LLD) Workshop 9:45am - 6:30pm New Orleans, LA Subarna Tripathi, Anahita Bhiwandiwalla, Alexei Bastidas, Hanlin Tang – Intel

Heuristics for Image Generation from Scene Graphs.

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Paper ›

Limited Labeled Data (LLD) Workshop 9:45am - 6:30pm New Orleans, LA

Tyler Lee, Ting Gong, Suchismita Padhy, Andrew Rouditchenko, Anthony Ndirango – Intel

Label-Efficient Audio Classification Through Multitask Learning and Self-Supervision.

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Paper ›

Poster Sessions - Day 2

Tuesday May 7, 2019

Title Time Location Authors Abstract
SPIGAN: Privileged Adversarial Learning from Simulation 4:30pm - 6:30pm New Orleans, LA German Ros Research Scientist – Intel Labs, Kuan-Hui Lee – Toyota Research Institute, Jie Li – Toyota Research Institute, Adrien Gaidon Machine Learning Lead – Toyota Research Institute

Authors propose a new unsupervised domain adaptation algorithm, called SPIGAN, relying on Simulator Privileged Information (PI) and Generative Adversarial Networks (GAN).

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Paper ›

Poster Sessions - Day 3

Wednesday May 8, 2019

Title Time Location Authors Abstract
Deep Layers as Stochastic Solvers 4:30pm - 6:30pm New Orleans, LA Adel Bibi Kaust – Intel Labs, Vladlen Koltun – Intel Labs, Rene Ranftl – Intel Labs, Bernard Ghanem – King Abdullah University of Science and Technology

Authors provide a novel perspective on the forward pass through a block of layers in a deep network.

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Paper ›

Sparse Dictionary Learning by Dynamical Neural Networks 4:30pm - 6:30pm New Orleans, LA

Tsung-Han Lin – Intel Labs, Peter Tang – Intel Labs

By combining ideas of top-down feedback and contrastive learning, a dynamical network for solving the l1-minimizing dictionary learning problem can be constructed, and the true gradients for learning are provably computable by individual neurons.

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Paper ›

Poster Sessions - Day 4

Thursday May 9, 2019

Title Time Location Authors Abstract
Trellis Networks for Sequence Modeling 11:00am - 1:00pm New Orleans, LA Vladlen Koltun – Intel Labs, Shaojie Bai – Carnegie Mellon University, Zico Kolter – Carnegie Mellon University and Bosch Center for AI

Introducing trellis networks, a new architecture for sequence modeling that outperforms current methods on a variety of benchmarks.

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Code ›

More Ways to Engage

  • Reinforcement Learning Coach — An open source research framework for training and evaluating reinforcement learning (RL) agents by harnessing the power of multi-core CPU processing to achieve state-of-the-art results.
  • Aspect Based Sentiment Analysis – Deep-Learning, powerful computing resources and greater access to useful datasets drove many advances in Natural Language Processing (NLP) in recent years. At Intel AI Research, our team of NLP researchers and developers released NLP Architect, an open source library, fully based on DL topologies, to share with the community and create a platform for future research and collaborations.
  • Generalization to Novel Objects Using Prior Knowledge – Graph-DQN, a new model, combines information from knowledge graphs and visual scenes, allowing the agent to learn, reason, and apply agent-object and object-object relations.
  • AI Lab at Intel – At Intel, we believe there is a virtuous cycle between research, algorithms and compute that’s leading to the tremendous growth we are seeing in AI.
  • The Intel AI Lab team will be presenting a talk on NLP Architect at the ICLR 2019 Expo on Tuesday, May 7th at 1:00 PM.
  • Intel® AI Research Luncheon at ICLR 2019 on Wednesday, May 8th from 12:00 PM – 2:00 PM CT at Tomas Bistro
  • Follow us @IntelAIResearch for the latest happenings at @iclr2019 ‏in New Orleans!