Samarth Sinha

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S4RL: Surprisingly Simple Self-Supervision for Offline Reinforcement Learning

Offline reinforcement learning proposes to learn policies from large col...
03/10/2021 ∙ by Samarth Sinha, et al. ∙ 0 ∙ share

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Learning by Watching: Physical Imitation of Manipulation Skills from Human Videos

We present an approach for physical imitation from human videos for robo...
01/18/2021 ∙ by Haoyu Xiong, et al. ∙ 7 ∙ share

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ImCLR: Implicit Contrastive Learning for Image Classification

Contrastive learning is an effective method for learning visual represen...
11/25/2020 ∙ by John Chen, et al. ∙ 0 ∙ 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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Maximum Entropy Models for Fast Adaptation

Deep Neural Networks have shown great promise on a variety of downstream...
06/30/2020 ∙ by Samarth Sinha, et al. ∙ 12 ∙ share

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Experience Replay with Likelihood-free Importance Weights

The use of past experiences to accelerate temporal difference (TD) learn...
06/23/2020 ∙ by Samarth Sinha, et al. ∙ 12 ∙ share

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DiVA: Diverse Visual Feature Aggregation forDeep Metric Learning

Visual Similarity plays an important role in many computer vision applic...
04/28/2020 ∙ by Timo Milbich, et al. ∙ 3 ∙ share

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DiVA: Diverse Visual Feature Aggregation for Deep Metric Learning

Visual Similarity plays an important role in many computer vision applic...
04/28/2020 ∙ by Timo Milbich, et al. ∙ 0 ∙ share

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DIBS: Diversity inducing Information Bottleneck in Model Ensembles

Although deep learning models have achieved state-of-the-art performance...
03/10/2020 ∙ by Samarth Sinha, et al. ∙ 7 ∙ share

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Curriculum By Texture

Convolutional Neural Networks (CNNs) have shown impressive performance i...
03/03/2020 ∙ by Samarth Sinha, et al. ∙ 5 ∙ share

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Revisiting Training Strategies and Generalization Performance in Deep Metric Learning

Deep Metric Learning (DML) is arguably one of the most influential lines...
02/19/2020 ∙ by Karsten Roth, et al. ∙ 0 ∙ share

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Top-K Training of GANs: Improving Generators by Making Critics Less Critical

We introduce a simple (one line of code) modification to the Generative ...
02/14/2020 ∙ by Samarth Sinha, et al. ∙ 16 ∙ share

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Small-GAN: Speeding Up GAN Training Using Core-sets

Recent work by Brock et al. (2018) suggests that Generative Adversarial ...
10/29/2019 ∙ by Samarth Sinha, et al. ∙ 22 ∙ share

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Variational Adversarial Active Learning

Active learning aims to develop label-efficient algorithms by sampling t...
03/31/2019 ∙ by Samarth Sinha, et al. ∙ 0 ∙ share

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Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders

Many approaches in generalized zero-shot learning rely on cross-modal ma...
12/05/2018 ∙ by Edgar Schönfeld, et al. ∙ 0 ∙ share

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Samarth Sinha

Featured Co-authors

Yoshua Bengio

Trevor Darrell

Stefano Ermon

Hugo Larochelle

Han Zhang

Animesh Garg

Zeynep Akata

Anastasios Kyrillidis

Anirudh Goyal

Joseph Paul Cohen

Jiaming Song

S4RL: Surprisingly Simple Self-Supervision for Offline Reinforcement Learning

Learning by Watching: Physical Imitation of Manipulation Skills from Human Videos

ImCLR: Implicit Contrastive Learning for Image Classification

D2RL: Deep Dense Architectures in Reinforcement Learning

Maximum Entropy Models for Fast Adaptation

Experience Replay with Likelihood-free Importance Weights

DiVA: Diverse Visual Feature Aggregation forDeep Metric Learning

DiVA: Diverse Visual Feature Aggregation for Deep Metric Learning

DIBS: Diversity inducing Information Bottleneck in Model Ensembles

Curriculum By Texture

Revisiting Training Strategies and Generalization Performance in Deep Metric Learning

Top-K Training of GANs: Improving Generators by Making Critics Less Critical

Small-GAN: Speeding Up GAN Training Using Core-sets

Variational Adversarial Active Learning

Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders