June 9 - 15 in Long Beach, CA

Intel AI Research at ICML

Intel is a sponsor of the 36th International Conference on Machine Learning (ICML). At ICML, you’ll discover cutting-edge research on all aspects of machine learning used in AI, statistics and data science, as well as applications like machine vision, computational biology, speech recognition, and robotics.

Intel AI Research at ICML June 10 - 15 in Long Beach, CA

Agenda

Accepted Paper Presentations - Day 3

Tuesday June 11, 2019
2:35pm - 2:40pm

Collaborative Evolutionary Reinforcement Learning (CERL)

Shaw Khadka – Intel, Somdeb Majumdar – Intel, Zach Dwiel – Terran Robotics, Evren Tumer – Intel, Santiago Miret – Intel, Yinyin Liu – Intel, Kagan Tumer – Oregon State University, Tarek Nassar

(Oral presentation) CERL is a sample-efficient Reinforcement Learning framework that combines gradient-based and gradient-free learning. CERL exceeds the performance of either of these two approaches in terms of sample efficiency and sensitivity to hyper-parameters.
Paper

3:05pm - 3:10pm

Non-Parametric Priors for Generative Adversarial Networks

Martin Braun – Intel, Ravi Garg – Intel, Rajhans Singh, Pavan Turaga, Suren Jayasuriya – Arizona State University

(Oral presentation) This paper proposes a novel prior which is derived using basic theorems from probability theory and off-the-shelf optimizers, to improve fidelity of image generation using GANs by interpolating along any Euclidean straight line without any additional training and architecture modifications.
Paper

Accepted Paper Presentations - Day 4

Wednesday June 12, 2019
12:10pm - 12:15pm

Parameter Efficient Training of Deep Convolutional Neural Networks by Dynamic Sparse Reparameterization

Hesham Mostafa – Intel, Xin Wang – Intel

(Oral presentation) We describe a heuristic for modifying the structure of sparse deep convolutional networks during training that allows us to train sparse networks directly to reach accuracies on par with accuracies obtained through compressing/pruning of large, dense models.
Paper

Accepted Paper Presentations - Day 6

Friday June 14, 2019

Learning a Hierarchy of Neural Connections for Modeling Uncertainty

Raanan Yehezkel – Intel, Yaniv Gurwicz – Intel, Shami Nisimov – Intel, Gal Novik – Intel

Quantifying and measuring uncertainty in deep neural networks is an open problem. In this paper we propose a new deep architecture, and demonstrate that it enables estimating various types of uncertainties.

Accepted Paper Presentations - Day 7

Saturday June 15, 2019
11:00pm - 12:00pm

Goal-conditioned Imitation Learning

Yiming Ding – UC Berkeley, Carlos Florensa – UC Berkeley, Mariano Phielipp – Intel AI Lab, Pieter Abbeel – UC Berkeley and Covariant

Solving challenge Robotics like environments in Reinforcement Learning using few demonstrations and self-supervision.
Paper

Privacy Preserving Adjacency Spectral Embedding on Stochastic Blockmodels

Li Chen – Intel

For graphs generated from stochastic blockmodels, adjacency spectral embedding is asymptotically consistent. The methodology presented in this paper can estimate the latent positions by adjacency spectral embedding and achieve comparable accuracy at desired privacy parameters in simulated and real world networks.
Paper

Sparse Representation Classification via Screening for Graphs

Censheng Shen, Li Chen – Intel, Carey Priebe, Yuexiao Dong

In this paper we propose a new implementation of the sparse representation classification (SRC) via screening, establish its equivalence to the original SRC under regularity conditions, and prove its classification consistency under a latent subspace model.
Paper

Expo Day - Day 1

Sunday June 9, 2019
2:00pm - 6:30pm
Room 101

Reaching Intent Estimation via Approximate Bayesian Computation

Demo: This interactive demo shows a system that provides real-time user intent estimation. When the user places an object on the table, the system will estimate the intended placement location and represent it as a probability density function. The system is composed of three elements: an object tracker, a model-based physically plausible trajectory generator and a probability function. The user is captured through an Intel® Realsense™ camera and the intent is obtained through approximate Bayesian computation in an analysis by synthesis approach.

3:00pm - 4:00pm
Grand Ballroom

Optimize Deep Learning on Apache Spark with Intel® DL Boost Technology and Intel® Parallel Studio

Talk: Thanks to Intel DL Boost technology with new Vector Neural Network Instructions (VNNI), deep learning inference performance in BigDL is dramatically improved on 2nd gen Intel® Xeon® Scalable processors. We will showcase the VGG-16 Fp32/Int8 throughput improvement and how to use Intel Parallel Studio to profile and optimize DL workloads.

5:30pm - 6:30pm
Grand Ballroom

NLP Architect by Intel® AI Lab

Session: NLP Architect is an open-source Python library for exploring the state-of-the-art deep learning topologies and techniques for natural language processing and natural language understanding. In this session, we will discuss NLP Architect features and demonstrate how easily non-ML/NLP developers can build advanced NLP applications such as unsupervised Aspect-Based Sentiment Analysis (ABSA), Set-Term Expansion, and Topic & Trend extraction.

Event Authors

Shauharda Khadka

Shauharda Khadka

Deep Learning Researcher, Intel AI Lab

Somdeb Majumdar

Machine Learning Research Lead, Intel AI Lab

Evren Tumer

Evren Tumer

Santiago Miret

Santiago Miret

Deep Learning Researcher, Intel AI Lab

Yinyin Liu

Yinyin Liu

Principal Engineer, Artificial Intelligence Products Group

Ravi Garg

Ravi Garg

Research Scientist, Assembly & Test Technology Development

Hesham Mostafa

AI Research Scientist, CTO Office, Artificial Intelligence Products Group

Xin Wang

Xin Wang

Data Scientist, CTO Office and Intel AI Lab

Raanan Y. Yehezkel Rohekar

Research Scientist, Intel AI Lab

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

Mariano Phielipp

Mariano Phielipp

Senior Deep Learning Data Scientist, Intel AI Lab

Li Chen

Research Scientist and Data Scientist, Security and Privacy Research Lab

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