32 citations · 66 across the 4 of their papers we have counts for
12 papers
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…
StackMix: A complementary Mix algorithm
John Chen, Samarth Sinha, Anastasios Kyrillidis
Techniques combining multiple images as input/output have proven to be effective data augmentations for training convolutional neural networks. In this paper, we present StackMix:…
Experience Replay with Likelihood-free Importance Weights
Samarth Sinha, Jiaming Song, Animesh Garg +1
The use of past experiences to accelerate temporal difference (TD) learning of value functions, or experience replay, is a key component in deep reinforcement learning. Prioritizat…
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,…
DiVA: Diverse Visual Feature Aggregation for Deep Metric Learning
Timo Milbich, Karsten Roth, Homanga Bharadhwaj +4
Visual Similarity plays an important role in many computer vision applications. Deep metric learning (DML) is a powerful framework for learning such similarities which not only gen…
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…