1 citations · 1 across the 4 of their papers we have counts for
6 papers
Learning Graph Energies for Finite-Time Generation
Amartya Banerjee, Xingyu Xu, Caroline Moosmüller +1
Energy-based models can represent distributions over discrete structures such as molecular graphs, but an energy that is accurate at equilibrium need not yield accurate samples whe…
Neural Wasserstein Splines: Representation-Invariant Second-Order Priors for Trajectory Inference
Amartya Banerjee, Harlin Lee, Caroline Moosmüller
Population dynamics are often observed only as unpaired snapshots, which leave the evolution between measurements underdetermined. Second-order regularization can constrain it, but…
Valid Property-Enhanced Contrastive Learning for Targeted Optimization & Resampling for Novel Drug Design
Amartya Banerjee, Somnath Kar, Anirban Pal +1
Efficiently steering generative models toward pharmacologically relevant regions of chemical space remains a major obstacle in molecular drug discovery under low-data regimes. We p…
Adaptive Multimodal Protein Plug-and-Play with Diffusion-Based Priors
Amartya Banerjee, Xingyu Xu, Caroline Moosmüller +1
In an inverse problem, the goal is to recover an unknown parameter (e.g., an image) that has typically undergone some lossy or noisy transformation during measurement. Recently, de…
Efficient Trajectory Inference in Wasserstein Space Using Consecutive Averaging
Amartya Banerjee, Harlin Lee, Nir Sharon +1
Capturing data from dynamic processes through cross-sectional measurements is seen in many fields, such as computational biology. Trajectory inference deals with the challenge of r…
Surprisal Driven -NN for Robust and Interpretable Nonparametric Learning
Amartya Banerjee, Christopher J. Hazard, Jacob Beel +4
Nonparametric learning is a fundamental concept in machine learning that aims to capture complex patterns and relationships in data without making strong assumptions about the unde…