Aravind Srinivas

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berkeley college

VideoGPT: Video Generation using VQ-VAE and Transformers

We present VideoGPT: a conceptually simple architecture for scaling like...
04/20/2021 ∙ by Wilson Yan, et al. ∙ 11 ∙ share

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Scaling Local Self-Attention For Parameter Efficient Visual Backbones

Self-attention has the promise of improving computer vision systems due ...
03/23/2021 ∙ by Ashish Vaswani, et al. ∙ 0 ∙ share

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Revisiting ResNets: Improved Training and Scaling Strategies

Novel computer vision architectures monopolize the spotlight, but the im...
03/13/2021 ∙ by Irwan Bello, et al. ∙ 0 ∙ share

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Improving Computational Efficiency in Visual Reinforcement Learning via Stored Embeddings

Recent advances in off-policy deep reinforcement learning (RL) have led ...
03/04/2021 ∙ by Lili Chen, et al. ∙ 0 ∙ share

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Bottleneck Transformers for Visual Recognition

We present BoTNet, a conceptually simple yet powerful backbone architect...
01/27/2021 ∙ by Aravind Srinivas, et al. ∙ 83 ∙ share

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Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation

Building instance segmentation models that are data-efficient and can ha...
12/13/2020 ∙ by Golnaz Ghiasi, et al. ∙ 18 ∙ share

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D2RL: Deep Dense Architectures in Reinforcement Learning

While improvements in deep learning architectures have played a crucial ...
10/19/2020 ∙ by Samarth Sinha, et al. ∙ 0 ∙ share

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Evaluating Self-Supervised Pretraining Without Using Labels

A common practice in unsupervised representation learning is to use labe...
09/16/2020 ∙ by Colorado Reed, et al. ∙ 8 ∙ share

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SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning

Model-free deep reinforcement learning (RL) has been successful in a ran...
07/09/2020 ∙ by Kimin Lee, et al. ∙ 66 ∙ share

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Reinforcement Learning with Augmented Data

Learning from visual observations is a fundamental yet challenging probl...
04/30/2020 ∙ by Michael (Misha) Laskin, et al. ∙ 14 ∙ share

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CURL: Contrastive Unsupervised Representations for Reinforcement Learning

We present CURL: Contrastive Unsupervised Representations for Reinforcem...
04/08/2020 ∙ by Aravind Srinivas, et al. ∙ 23 ∙ share

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Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design

Flow-based generative models are powerful exact likelihood models with e...
02/01/2019 ∙ by Jonathan Ho, et al. ∙ 97 ∙ share

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Universal Planning Networks

A key challenge in complex visuomotor control is learning abstract repre...
04/02/2018 ∙ by Aravind Srinivas, et al. ∙ 0 ∙ share

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Aravind Srinivas

Featured Co-authors

Sergey Levine

Pieter Abbeel

Xi Chen

Trevor Darrell

Chelsea Finn

Quoc V. Le

Kurt Keutzer

Animesh Garg

Jonathon Shlens

Lerrel Pinto

Barret Zoph

VideoGPT: Video Generation using VQ-VAE and Transformers

Scaling Local Self-Attention For Parameter Efficient Visual Backbones

Revisiting ResNets: Improved Training and Scaling Strategies

Improving Computational Efficiency in Visual Reinforcement Learning via Stored Embeddings

Bottleneck Transformers for Visual Recognition

Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation

D2RL: Deep Dense Architectures in Reinforcement Learning

Evaluating Self-Supervised Pretraining Without Using Labels

SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning

Reinforcement Learning with Augmented Data

CURL: Contrastive Unsupervised Representations for Reinforcement Learning

Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design

Universal Planning Networks