104 citations · 374 across the 40 of their papers we have counts for
6 papers · 1 filter
Does the Data Induce Capacity Control in Deep Learning?
Rubing Yang, Jialin Mao, Pratik Chaudhari
We show that the input correlation matrix of typical classification datasets has an eigenspectrum where, after a sharp initial drop, a large number of small eigenvalues are distrib…
Harmonization with Flow-based Causal Inference
Rongguang Wang, Pratik Chaudhari, Christos Davatzikos
Heterogeneity in medical data, e.g., from data collected at different sites and with different protocols in a clinical study, is a fundamental hurdle for accurate prediction using…
Model Zoo: A Growing "Brain" That Learns Continually
Rahul Ramesh, Pratik Chaudhari
This paper argues that continual learning methods can benefit by splitting the capacity of the learner across multiple models. We use statistical learning theory and experimental a…
Deformable Linear Object Prediction Using Locally Linear Latent Dynamics
Wenbo Zhang, Karl Schmeckpeper, Pratik Chaudhari +1
We propose a framework for deformable linear object prediction. Prediction of deformable objects (e.g., rope) is challenging due to their non-linear dynamics and infinite-dimension…
Embracing the Disharmony in Medical Imaging: A Simple and Effective Framework for Domain Adaptation
Rongguang Wang, Pratik Chaudhari, Christos Davatzikos
Domain shift, the mismatch between training and testing data characteristics, causes significant degradation in the predictive performance in multi-source imaging scenarios. In med…
Continuous Doubly Constrained Batch Reinforcement Learning
Rasool Fakoor, Jonas Mueller, Kavosh Asadi +2
Reliant on too many experiments to learn good actions, current Reinforcement Learning (RL) algorithms have limited applicability in real-world settings, which can be too expensive…