32 citations · 69 across the 7 of their papers we have counts for
6 papers · 1 filter
S4RL: Surprisingly Simple Self-Supervision for Offline Reinforcement Learning
Samarth Sinha, Ajay Mandlekar, Animesh Garg
Offline reinforcement learning proposes to learn policies from large collected datasets without interacting with the physical environment. These algorithms have made it possible to…
D2RL: Deep Dense Architectures in Reinforcement Learning
Samarth Sinha, Homanga Bharadhwaj, Aravind Srinivas +1
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…
Uniform Priors for Data-Efficient Transfer
Samarth Sinha, Karsten Roth, Anirudh Goyal +3
Deep Neural Networks have shown great promise on a variety of downstream applications; but their ability to adapt and generalize to new data and tasks remains a challenge. However,…
Diversity inducing Information Bottleneck in Model Ensembles
Samarth Sinha, Homanga Bharadhwaj, Anirudh Goyal +3
Although deep learning models have achieved state-of-the-art performance on a number of vision tasks, generalization over high dimensional multi-modal data, and reliable predictive…
Curriculum By Smoothing
Samarth Sinha, Animesh Garg, Hugo Larochelle
Convolutional Neural Networks (CNNs) have shown impressive performance in computer vision tasks such as image classification, detection, and segmentation. Moreover, recent work in…
Variational Adversarial Active Learning
Samarth Sinha, Sayna Ebrahimi, Trevor Darrell
Active learning aims to develop label-efficient algorithms by sampling the most representative queries to be labeled by an oracle. We describe a pool-based semi-supervised active l…