Search Results for author: Aravind Srinivas

Found 27 papers, 19 papers with code

CURL: Contrastive Unsupervised Representation Learning for Reinforcement Learning

1 code implementation ICML 2020 Michael Laskin, Pieter Abbeel, Aravind Srinivas

CURL extracts high level features from raw pixels using a contrastive learning objective and performs off-policy control on top of the extracted features.

Contrastive Learning reinforcement-learning +2

VideoGPT: Video Generation using VQ-VAE and Transformers

1 code implementation20 Apr 2021 Wilson Yan, Yunzhi Zhang, Pieter Abbeel, Aravind Srinivas

We present VideoGPT: a conceptually simple architecture for scaling likelihood based generative modeling to natural videos.

Video Generation

Scaling Local Self-Attention for Parameter Efficient Visual Backbones

7 code implementations CVPR 2021 Ashish Vaswani, Prajit Ramachandran, Aravind Srinivas, Niki Parmar, Blake Hechtman, Jonathon Shlens

Self-attention models have recently been shown to have encouraging improvements on accuracy-parameter trade-offs compared to baseline convolutional models such as ResNet-50.

Image Classification Instance Segmentation +4

Revisiting ResNets: Improved Training and Scaling Strategies

3 code implementations NeurIPS 2021 Irwan Bello, William Fedus, Xianzhi Du, Ekin D. Cubuk, Aravind Srinivas, Tsung-Yi Lin, Jonathon Shlens, Barret Zoph

Using improved training and scaling strategies, we design a family of ResNet architectures, ResNet-RS, which are 1. 7x - 2. 7x faster than EfficientNets on TPUs, while achieving similar accuracies on ImageNet.

Action Classification Document Image Classification +2

Bottleneck Transformers for Visual Recognition

13 code implementations CVPR 2021 Aravind Srinivas, Tsung-Yi Lin, Niki Parmar, Jonathon Shlens, Pieter Abbeel, Ashish Vaswani

Finally, we present a simple adaptation of the BoTNet design for image classification, resulting in models that achieve a strong performance of 84. 7% top-1 accuracy on the ImageNet benchmark while being up to 1. 64x faster in compute time than the popular EfficientNet models on TPU-v3 hardware.

Image Classification Instance Segmentation +3

Weighted Bellman Backups for Improved Signal-to-Noise in Q-Updates

no code implementations1 Jan 2021 Kimin Lee, Michael Laskin, Aravind Srinivas, Pieter Abbeel

Furthermore, since our weighted Bellman backups rely on maintaining an ensemble, we investigate how weighted Bellman backups interact with other benefits previously derived from ensembles: (a) Bootstrap; (b) UCB Exploration.

Q-Learning Reinforcement Learning (RL)

VideoGen: Generative Modeling of Videos using VQ-VAE and Transformers

no code implementations1 Jan 2021 Yunzhi Zhang, Wilson Yan, Pieter Abbeel, Aravind Srinivas

We present VideoGen: a conceptually simple architecture for scaling likelihood based generative modeling to natural videos.

Video Generation

R-LAtte: Attention Module for Visual Control via Reinforcement Learning

no code implementations1 Jan 2021 Mandi Zhao, Qiyang Li, Aravind Srinivas, Ignasi Clavera, Kimin Lee, Pieter Abbeel

Attention mechanisms are generic inductive biases that have played a critical role in improving the state-of-the-art in supervised learning, unsupervised pre-training and generative modeling for multiple domains including vision, language and speech.

reinforcement-learning Reinforcement Learning (RL) +1

Compute- and Memory-Efficient Reinforcement Learning with Latent Experience Replay

no code implementations1 Jan 2021 Lili Chen, Kimin Lee, Aravind Srinivas, Pieter Abbeel

In this paper, we present Latent Vector Experience Replay (LeVER), a simple modification of existing off-policy RL methods, to address these computational and memory requirements without sacrificing the performance of RL agents.

Atari Games reinforcement-learning +2

D2RL: Deep Dense Architectures in Reinforcement Learning

4 code implementations19 Oct 2020 Samarth Sinha, Homanga Bharadhwaj, Aravind Srinivas, Animesh Garg

While improvements in deep learning architectures have played a crucial role in improving the state of supervised and unsupervised learning in computer vision and natural language processing, neural network architecture choices for reinforcement learning remain relatively under-explored.

reinforcement-learning Reinforcement Learning (RL)

Reinforcement Learning with Augmented Data

2 code implementations NeurIPS 2020 Michael Laskin, Kimin Lee, Adam Stooke, Lerrel Pinto, Pieter Abbeel, Aravind Srinivas

To this end, we present Reinforcement Learning with Augmented Data (RAD), a simple plug-and-play module that can enhance most RL algorithms.

Data Augmentation OpenAI Gym +2

CURL: Contrastive Unsupervised Representations for Reinforcement Learning

7 code implementations8 Apr 2020 Aravind Srinivas, Michael Laskin, Pieter Abbeel

On the DeepMind Control Suite, CURL is the first image-based algorithm to nearly match the sample-efficiency of methods that use state-based features.

Atari Games Atari Games 100k +4

PatchFormer: A neural architecture for self-supervised representation learning on images

no code implementations25 Sep 2019 Aravind Srinivas, Pieter Abbeel

In this paper, we propose a neural architecture for self-supervised representation learning on raw images called the PatchFormer which learns to model spatial dependencies across patches in a raw image.

Representation Learning Self-Supervised Learning

Universal Planning Networks: Learning Generalizable Representations for Visuomotor Control

1 code implementation ICML 2018 Aravind Srinivas, Allan Jabri, Pieter Abbeel, Sergey Levine, Chelsea Finn

A key challenge in complex visuomotor control is learning abstract representations that are effective for specifying goals, planning, and generalization.

Imitation Learning

Universal Planning Networks

1 code implementation2 Apr 2018 Aravind Srinivas, Allan Jabri, Pieter Abbeel, Sergey Levine, Chelsea Finn

We find that the representations learned are not only effective for goal-directed visual imitation via gradient-based trajectory optimization, but can also provide a metric for specifying goals using images.

Imitation Learning Representation Learning +1

Learning to Repeat: Fine Grained Action Repetition for Deep Reinforcement Learning

no code implementations20 Feb 2017 Sahil Sharma, Aravind Srinivas, Balaraman Ravindran

Reinforcement Learning algorithms can learn complex behavioral patterns for sequential decision making tasks wherein an agent interacts with an environment and acquires feedback in the form of rewards sampled from it.

Car Racing Decision Making +2

Option Discovery in Hierarchical Reinforcement Learning using Spatio-Temporal Clustering

no code implementations17 May 2016 Aravind Srinivas, Ramnandan Krishnamurthy, Peeyush Kumar, Balaraman Ravindran

This paper introduces an automated skill acquisition framework in reinforcement learning which involves identifying a hierarchical description of the given task in terms of abstract states and extended actions between abstract states.

Clustering Hierarchical Reinforcement Learning +3

Dynamic Frame skip Deep Q Network

no code implementations17 May 2016 Aravind Srinivas, Sahil Sharma, Balaraman Ravindran

Deep Reinforcement Learning methods have achieved state of the art performance in learning control policies for the games in the Atari 2600 domain.

Atari Games

Attend, Adapt and Transfer: Attentive Deep Architecture for Adaptive Transfer from multiple sources in the same domain

2 code implementations10 Oct 2015 Janarthanan Rajendran, Aravind Srinivas, Mitesh M. Khapra, P. Prasanna, Balaraman Ravindran

Second, the agent should be able to selectively transfer, which is the ability to select and transfer from different and multiple source tasks for different parts of the state space of the target task.

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