Aravind Srinivas | DeepAI

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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...

0 Lili Chen, et al. ∙

∙ 04/20/2021

VideoGPT: Video Generation using VQ-VAE and Transformers

We present VideoGPT: a conceptually simple architecture for scaling like...

12 Wilson Yan, et al. ∙

∙ 03/23/2021

Scaling Local Self-Attention For Parameter Efficient Visual Backbones

Self-attention has the promise of improving computer vision systems due ...

0 Ashish Vaswani, et al. ∙

∙ 03/13/2021

Revisiting ResNets: Improved Training and Scaling Strategies

Novel computer vision architectures monopolize the spotlight, but the im...

0 Irwan Bello, et al. ∙

∙ 03/04/2021

Improving Computational Efficiency in Visual Reinforcement Learning via Stored Embeddings

Recent advances in off-policy deep reinforcement learning (RL) have led ...

0 Lili Chen, et al. ∙

∙ 01/27/2021

Bottleneck Transformers for Visual Recognition

We present BoTNet, a conceptually simple yet powerful backbone architect...

83 Aravind Srinivas, et al. ∙

∙ 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...

18 Golnaz Ghiasi, et al. ∙

∙ 10/19/2020

D2RL: Deep Dense Architectures in Reinforcement Learning

While improvements in deep learning architectures have played a crucial ...

0 Samarth Sinha, et al. ∙

∙ 09/16/2020

Evaluating Self-Supervised Pretraining Without Using Labels

A common practice in unsupervised representation learning is to use labe...

8 Colorado Reed, et al. ∙

∙ 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...

66 Kimin Lee, et al. ∙

∙ 04/30/2020

Reinforcement Learning with Augmented Data

Learning from visual observations is a fundamental yet challenging probl...

14 Michael (Misha) Laskin, et al. ∙

∙ 04/08/2020

CURL: Contrastive Unsupervised Representations for Reinforcement Learning

We present CURL: Contrastive Unsupervised Representations for Reinforcem...

23 Aravind Srinivas, et al. ∙

∙ 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...

97 Jonathan Ho, et al. ∙

∙ 04/02/2018

Universal Planning Networks

A key challenge in complex visuomotor control is learning abstract repre...

0 Aravind Srinivas, et al. ∙

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