Sainbayar Sukhbaatar

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Facebook ∙ NYU college ∙ The University of Tokyo

Learning to Visually Navigate in Photorealistic Environments Without any Supervision

Learning to navigate in a realistic setting where an agent must rely sol...
04/10/2020 ∙ by Lina Mezghani, et al. ∙ 3 ∙ share

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Accessing Higher-level Representations in Sequential Transformers with Feedback Memory

Transformers are feedforward networks that can process input tokens in p...
02/21/2020 ∙ by Angela Fan, et al. ∙ 5 ∙ share

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Augmenting Self-attention with Persistent Memory

Transformer networks have lead to important progress in language modelin...
07/02/2019 ∙ by Sainbayar Sukhbaatar, et al. ∙ 6 ∙ share

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Adaptive Attention Span in Transformers

We propose a novel self-attention mechanism that can learn its optimal a...
05/19/2019 ∙ by Sainbayar Sukhbaatar, et al. ∙ 0 ∙ share

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Learning when to Communicate at Scale in Multiagent Cooperative and Competitive Tasks

Learning when to communicate and doing that effectively is essential in ...
12/23/2018 ∙ by Amanpreet Singh, et al. ∙ 0 ∙ share

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Learning Goal Embeddings via Self-Play for Hierarchical Reinforcement Learning

In hierarchical reinforcement learning a major challenge is determining ...
11/22/2018 ∙ by Sainbayar Sukhbaatar, et al. ∙ 0 ∙ share

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Planning with Arithmetic and Geometric Attributes

A desirable property of an intelligent agent is its ability to understan...
09/06/2018 ∙ by David Folqué, et al. ∙ 10 ∙ share

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Composable Planning with Attributes

The tasks that an agent will need to solve often are not known during tr...
03/01/2018 ∙ by Amy Zhang, et al. ∙ 0 ∙ share

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Simple Baseline for Visual Question Answering

We describe a very simple bag-of-words baseline for visual question answ...
12/07/2015 ∙ by Bolei Zhou, et al. ∙ 0 ∙ share

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MazeBase: A Sandbox for Learning from Games

This paper introduces MazeBase: an environment for simple 2D games, desi...
11/23/2015 ∙ by Sainbayar Sukhbaatar, et al. ∙ 0 ∙ share

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End-To-End Memory Networks

We introduce a neural network with a recurrent attention model over a po...
03/31/2015 ∙ by Sainbayar Sukhbaatar, et al. ∙ 0 ∙ share

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Training Convolutional Networks with Noisy Labels

The availability of large labeled datasets has allowed Convolutional Net...
06/09/2014 ∙ by Sainbayar Sukhbaatar, et al. ∙ 0 ∙ share

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Auto-pooling: Learning to Improve Invariance of Image Features from Image Sequences

Learning invariant representations from images is one of the hardest cha...
01/15/2013 ∙ by Sainbayar Sukhbaatar, et al. ∙ 0 ∙ share

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Sainbayar Sukhbaatar

Featured Co-authors

Joan Bruna

Jason Weston

Bolei Zhou

Arthur Szlam

Armand Joulin

Gabriel Synnaeve

Hervé Jégou

Rob Fergus

Edouard Grave

Yuandong Tian

Piotr Bojanowski

Learning to Visually Navigate in Photorealistic Environments Without any Supervision

Accessing Higher-level Representations in Sequential Transformers with Feedback Memory

Augmenting Self-attention with Persistent Memory

Adaptive Attention Span in Transformers

Learning when to Communicate at Scale in Multiagent Cooperative and Competitive Tasks

Learning Goal Embeddings via Self-Play for Hierarchical Reinforcement Learning

Planning with Arithmetic and Geometric Attributes

Composable Planning with Attributes

Simple Baseline for Visual Question Answering

MazeBase: A Sandbox for Learning from Games

End-To-End Memory Networks

Training Convolutional Networks with Noisy Labels

Auto-pooling: Learning to Improve Invariance of Image Features from Image Sequences