Sainbayar Sukhbaatar

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NYU college

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The University of Tokyo

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∙ 06/23/2022

Walk the Random Walk: Learning to Discover and Reach Goals Without Supervision

Learning a diverse set of skills by interacting with an environment with...

Lina Mezghani, et al. ∙

∙ 06/15/2022

DIRECTOR: Generator-Classifiers For Supervised Language Modeling

Current language models achieve low perplexity but their resulting gener...

Kushal Arora, et al. ∙

∙ 03/21/2022

Temporal Abstractions-Augmented Temporally Contrastive Learning: An Alternative to the Laplacian in RL

In reinforcement learning, the graph Laplacian has proved to be a valuab...

Akram Erraqabi, et al. ∙

∙ 06/08/2021

Hash Layers For Large Sparse Models

We investigate the training of sparse layers that use different paramete...

Stephen Roller, et al. ∙

∙ 06/08/2021

Staircase Attention for Recurrent Processing of Sequences

Attention mechanisms have become a standard tool for sequence modeling t...

Da Ju, et al. ∙

∙ 05/13/2021

Not All Memories are Created Equal: Learning to Forget by Expiring

Attention mechanisms have shown promising results in sequence modeling t...

Sainbayar Sukhbaatar, et al. ∙

∙ 01/13/2021

Memory-Augmented Reinforcement Learning for Image-Goal Navigation

In this work, we address the problem of image-goal navigation in the con...

Lina Mezghani, et al. ∙

∙ 04/10/2020

Learning to Visually Navigate in Photorealistic Environments Without any Supervision

Learning to navigate in a realistic setting where an agent must rely sol...

Lina Mezghani, et al. ∙

∙ 02/21/2020

Accessing Higher-level Representations in Sequential Transformers with Feedback Memory

Transformers are feedforward networks that can process input tokens in p...

Angela Fan, et al. ∙

∙ 07/02/2019

Augmenting Self-attention with Persistent Memory

Transformer networks have lead to important progress in language modelin...

Sainbayar Sukhbaatar, et al. ∙

∙ 05/19/2019

Adaptive Attention Span in Transformers

We propose a novel self-attention mechanism that can learn its optimal a...

Sainbayar Sukhbaatar, et al. ∙

∙ 12/23/2018

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

Learning when to communicate and doing that effectively is essential in ...

Amanpreet Singh, et al. ∙

∙ 11/22/2018

Learning Goal Embeddings via Self-Play for Hierarchical Reinforcement Learning

In hierarchical reinforcement learning a major challenge is determining ...

Sainbayar Sukhbaatar, et al. ∙

∙ 09/06/2018

Planning with Arithmetic and Geometric Attributes

A desirable property of an intelligent agent is its ability to understan...

David Folqué, et al. ∙

∙ 03/01/2018

Composable Planning with Attributes

The tasks that an agent will need to solve often are not known during tr...

Amy Zhang, et al. ∙

∙ 12/07/2015

Simple Baseline for Visual Question Answering

We describe a very simple bag-of-words baseline for visual question answ...

Bolei Zhou, et al. ∙

∙ 11/23/2015

MazeBase: A Sandbox for Learning from Games

This paper introduces MazeBase: an environment for simple 2D games, desi...

Sainbayar Sukhbaatar, et al. ∙

∙ 03/31/2015

End-To-End Memory Networks

We introduce a neural network with a recurrent attention model over a po...

Sainbayar Sukhbaatar, et al. ∙

∙ 06/09/2014

Training Convolutional Networks with Noisy Labels

The availability of large labeled datasets has allowed Convolutional Net...

Sainbayar Sukhbaatar, et al. ∙

∙ 01/15/2013

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

Learning invariant representations from images is one of the hardest cha...

Sainbayar Sukhbaatar, et al. ∙