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06/02/2021
Decision Transformer: Reinforcement Learning via Sequence Modeling
We present a framework that abstracts Reinforcement Learning (RL) as a s...
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04/20/2021
VideoGPT: Video Generation using VQ-VAE and Transformers
We present VideoGPT: a conceptually simple architecture for scaling like...
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03/23/2021
Scaling Local Self-Attention For Parameter Efficient Visual Backbones
Self-attention has the promise of improving computer vision systems due ...
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03/13/2021
Revisiting ResNets: Improved Training and Scaling Strategies
Novel computer vision architectures monopolize the spotlight, but the im...
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03/04/2021
Improving Computational Efficiency in Visual Reinforcement Learning via Stored Embeddings
Recent advances in off-policy deep reinforcement learning (RL) have led ...
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01/27/2021
Bottleneck Transformers for Visual Recognition
We present BoTNet, a conceptually simple yet powerful backbone architect...
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12/13/2020
Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation
Building instance segmentation models that are data-efficient and can ha...
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10/19/2020
D2RL: Deep Dense Architectures in Reinforcement Learning
While improvements in deep learning architectures have played a crucial ...
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09/16/2020
Evaluating Self-Supervised Pretraining Without Using Labels
A common practice in unsupervised representation learning is to use labe...
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07/09/2020
SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning
Model-free deep reinforcement learning (RL) has been successful in a ran...
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04/30/2020
Reinforcement Learning with Augmented Data
Learning from visual observations is a fundamental yet challenging probl...
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04/08/2020
CURL: Contrastive Unsupervised Representations for Reinforcement Learning
We present CURL: Contrastive Unsupervised Representations for Reinforcem...
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02/01/2019
Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design
Flow-based generative models are powerful exact likelihood models with e...
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04/02/2018