79 citations · 202 across the 19 of their papers we have counts for
22 papers
-Patching: A Framework for Rapid Adaptation of Pre-trained Convolutional Networks without Base Performance Loss
Chaitanya Devaguptapu, Samarth Sinha, K J Joseph +2
Models pre-trained on large-scale datasets are often fine-tuned to support newer tasks and datasets that arrive over time. This process necessitates storing copies of the model ove…
Offline Policy Optimization in RL with Variance Regularizaton
Riashat Islam, Samarth Sinha, Homanga Bharadhwaj +6
Learning policies from fixed offline datasets is a key challenge to scale up reinforcement learning (RL) algorithms towards practical applications. This is often because off-policy…
SparsePose: Sparse-View Camera Pose Regression and Refinement
Samarth Sinha, Jason Y. Zhang, Andrea Tagliasacchi +2
Camera pose estimation is a key step in standard 3D reconstruction pipelines that operate on a dense set of images of a single object or scene. However, methods for pose estimation…
Common Pets in 3D: Dynamic New-View Synthesis of Real-Life Deformable Categories
Samarth Sinha, Roman Shapovalov, Jeremy Reizenstein +4
Obtaining photorealistic reconstructions of objects from sparse views is inherently ambiguous and can only be achieved by learning suitable reconstruction priors. Earlier works on…
TeST: Test-time Self-Training under Distribution Shift
Samarth Sinha, Peter Gehler, Francesco Locatello +1
Despite their recent success, deep neural networks continue to perform poorly when they encounter distribution shifts at test time. Many recently proposed approaches try to counter…
Koopman Q-learning: Offline Reinforcement Learning via Symmetries of Dynamics
Matthias Weissenbacher, Samarth Sinha, Animesh Garg +1
Offline reinforcement learning leverages large datasets to train policies without interactions with the environment. The learned policies may then be deployed in real-world setting…