JULY 12TH - JULY 18TH

ICML 2020

View information on Facebook AI’s published papers and presentations at ICML 2020

Visit the Virtual ICML Booth

Starting July 12th -Juy 18th, attendees can visit the ICML website to chat with Facebook engineers, researchers, and recruiters virtually.

View the Schedule

Event Highlights

Latinx in AI Workshop, with Live Q+A

July 13, 2020 7:30 am EST

Francisco Guzman is a speaker

Women in Machine Learning Un-Workshop

July 13, 2020, 6:00 GMT

Facebook AI is sponsoring this workshop.

Kalesha Bullard is a speaker

Facebook Publications at ICML 2020

July 12, 2020

RESEARCH

ML APPLICATIONS

Differentiating through the Fréchet Mean

Recent advances in deep representation learning on Riemannian manifolds extend classical deep learning operations to better capture the geometry of the manifold. One…

Aaron Lou, Isay Katsman, Qingxuan Jiang, Serge Belongie, Ser-Nam Lim, Christopher De Sa

July 12, 2020

July 13, 2020

RESEARCH

NLP

Non-autoregressive Machine Translation with Disentangled Context Transformer

State-of-the-art neural machine translation models generate a translation from left to right and every step is conditioned on the previously generated tokens. The…

Jungo Kasai, James Cross, Marjan Ghazvininejad, Jiatao Gu

July 13, 2020

July 14, 2020

RESEARCH

ML APPLICATIONS

Invariant Causal Prediction for Block MDPs

Generalization across environments is critical for the successful application of reinforcement learning algorithms to real-world challenges. In this paper, we consider…

Amy Zhang, Clare Lyle, Shagun Sodhani, Angelos Filos, Marta Kwiatkowska, Joelle Pineau, Yarin Gal, Doina Precup

July 14, 2020

July 13, 2020

RESEARCH

A Sequential Self Teaching Approach for Improving Generalization in Sound Event Recognition

An important problem in machine auditory perception is to recognize and detect sound events. In this paper, we propose a sequential self-teaching approach to learning…

Anurag Kumar, Vamsi Krishna Ithapu

July 13, 2020

August 14, 2020

RESEARCH

On the Convergence of Nesterov’s Accelerated Gradient Method in Stochastic Settings

We study Nesterov’s accelerated gradient method with constant step-size and momentum parameters in the stochastic approximation setting (unbiased gradients with bounded…

Mido Assran, Michael Rabbat

August 14, 2020

July 12, 2020

RESEARCH

ML APPLICATIONS

Stochastic Hamiltonian Gradient Methods for Smooth Games

The success of adversarial formulations in machine learning has brought renewed motivation for smooth games. In this work, we focus on the class of stochastic Hamiltonian methods and provide the first convergence guarantees for certain classes of stochastic smooth games.

Nicolas Loizou, Hugo Berard, Alexia Jolicoeur-Martineau, Pascal Vincent, Simon Lacoste-Julien, Ioannis Mitliagkas

July 12, 2020

July 14, 2020

RESEARCH

Growing Action Spaces

In complex tasks, such as those with large combinatorial action spaces, random exploration may be too inefficient to achieve meaningful learning progress. In this work, we…

Gregory Farquhar, Laura Gustafson, Zeming Lin, Shimon Whiteson, Nicolas Usunier, Gabriel Synnaeve

July 14, 2020

July 12, 2020

ML APPLICATIONS

The Differentiable Cross-Entropy Method

We study the Cross-Entropy Method (CEM) for the non-convex optimization of a continuous and parameterized objective function and introduce a differentiable variant that…

Brandon Amos, Denis Yarats

July 12, 2020

July 13, 2020

ML APPLICATIONS

RESEARCH

LEEP: A New Measure to Evaluate Transferability of Learned Representations

We introduce a new measure to evaluate the transferability of representations learned by classifiers. Our measure, the Log Expected Empirical Prediction(LEEP), is simple and easy to compute…

Cuong V. Nguyen, Tal Hassner, Matthias Seeger, Cedric Archambeau

July 13, 2020

August 13, 2020

ML APPLICATIONS

RESEARCH

Learning Robot Skills with Temporal Variational Inference

In this paper, we address the discovery of robotic options from demonstrations in an unsupervised manner. Specifically, we present a framework to jointly learn low-level…

Tanmay Shankar, Abhinav Gupta

August 13, 2020

Join us for PyTorch Q & A Sessions at ICML

Find more details at the Facebook Booth
  • 3D Deep Learning with PyTorch3D

  • PySlowFast: Deep Learning with Video

  • PyTorch Quantization

  • Multimodal learning with PyTorch

  • Serving PyTorch Models

  • PyTorch Mobile

  • Model Interpretability with Captum

  • Q & A with the PyTorch team!

Help Us Pioneer the Future of AI

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