125 citations · 162 across the 12 of their papers we have counts for
5 papers · 1 filter
Towards Sparsified Federated Neuroimaging Models via Weight Pruning
Dimitris Stripelis, Umang Gupta, Nikhil Dhinagar +3
Federated training of large deep neural networks can often be restrictive due to the increasing costs of communicating the updates with increasing model sizes. Various model prunin…
Secure & Private Federated Neuroimaging
Dimitris Stripelis, Umang Gupta, Hamza Saleem +9
The amount of biomedical data continues to grow rapidly. However, collecting data from multiple sites for joint analysis remains challenging due to security, privacy, and regulator…
Federated Progressive Sparsification (Purge, Merge, Tune)+
Dimitris Stripelis, Umang Gupta, Greg Ver Steeg +1
To improve federated training of neural networks, we develop FedSparsify, a sparsification strategy based on progressive weight magnitude pruning. Our method has several benefits.…
Controllable Guarantees for Fair Outcomes via Contrastive Information Estimation
Umang Gupta, Aaron M Ferber, Bistra Dilkina +1
Controlling bias in training datasets is vital for ensuring equal treatment, or parity, between different groups in downstream applications. A naive solution is to transform the da…
Deep Generative Dual Memory Network for Continual Learning
Nitin Kamra, Umang Gupta, Yan Liu
Despite advances in deep learning, neural networks can only learn multiple tasks when trained on them jointly. When tasks arrive sequentially, they lose performance on previously l…