42 citations · 57 across the 14 of their papers we have counts for
5 papers · 1 filter
Reinforcement Learning for Molecular Dynamics Optimization: A Stochastic Pontryagin Maximum Principle Approach
Chandrajit Bajaj, Minh Nguyen, Conrad Li
In this paper, we present a novel reinforcement learning framework designed to optimize molecular dynamics by focusing on the entire trajectory rather than just the final molecular…
A Particle-based Sparse Gaussian Process Optimizer
Chandrajit Bajaj, Omatharv Bharat Vaidya, Yi Wang
Task learning in neural networks typically requires finding a globally optimal minimizer to a loss function objective. Conventional designs of swarm based optimization methods appl…
Sample Efficient Learning of Factored Embeddings of Tensor Fields
Taemin Heo, Chandrajit Bajaj
Data tensors of orders 2 and greater are now routinely being generated. These data collections are increasingly huge and growing. Many scientific and medical data tensors are tenso…
E-CIR: Event-Enhanced Continuous Intensity Recovery
Chen Song, Qixing Huang, Chandrajit Bajaj
A camera begins to sense light the moment we press the shutter button. During the exposure interval, relative motion between the scene and the camera causes motion blur, a common u…
A distribution-dependent Mumford-Shah model for unsupervised hyperspectral image segmentation
Jan-Christopher Cohrs, Chandrajit Bajaj, Benjamin Berkels
Hyperspectral images provide a rich representation of the underlying spectrum for each pixel, allowing for a pixel-wise classification/segmentation into different classes. As the a…