Search Results for author: Umang Gupta

Found 10 papers, 2 papers with code

Secure Federated Learning for Neuroimaging

no code implementations11 May 2022 Dimitris Stripelis, Umang Gupta, Hamza Saleem, Nikhil Dhinagar, Tanmay Ghai, Rafael Sanchez, Chrysovalantis Anastasiou, Armaghan Asghar, Greg Ver Steeg, Srivatsan Ravi, Muhammad Naveed, Paul M. Thompson, Jose Luis Ambite

Specifically, we investigate training neural models to classify Alzheimer's disease, and estimate Brain Age, from magnetic resonance imaging datasets distributed across multiple sites, including heterogeneous environments where sites have different amounts of data, statistical distributions, and computational capabilities.

Federated Learning

Federated Progressive Sparsification (Purge, Merge, Tune)+

no code implementations26 Apr 2022 Dimitris Stripelis, Umang Gupta, Greg Ver Steeg, Jose Luis Ambite

Second, the models are incrementally constrained to a smaller set of parameters, which facilitates alignment/merging of the local models and improved learning performance at high sparsification rates.

Attributing Fair Decisions with Attention Interventions

no code implementations8 Sep 2021 Ninareh Mehrabi, Umang Gupta, Fred Morstatter, Greg Ver Steeg, Aram Galstyan

The widespread use of Artificial Intelligence (AI) in consequential domains, such as healthcare and parole decision-making systems, has drawn intense scrutiny on the fairness of these methods.

Decision Making Fairness

Secure Neuroimaging Analysis using Federated Learning with Homomorphic Encryption

no code implementations7 Aug 2021 Dimitris Stripelis, Hamza Saleem, Tanmay Ghai, Nikhil Dhinagar, Umang Gupta, Chrysovalantis Anastasiou, Greg Ver Steeg, Srivatsan Ravi, Muhammad Naveed, Paul M. Thompson, Jose Luis Ambite

Federated learning (FL) enables distributed computation of machine learning models over various disparate, remote data sources, without requiring to transfer any individual data to a centralized location.

Federated Learning

Membership Inference Attacks on Deep Regression Models for Neuroimaging

no code implementations6 May 2021 Umang Gupta, Dimitris Stripelis, Pradeep K. Lam, Paul M. Thompson, José Luis Ambite, Greg Ver Steeg

In particular, we show that it is possible to infer if a sample was used to train the model given only access to the model prediction (black-box) or access to the model itself (white-box) and some leaked samples from the training data distribution.

Federated Learning

Controllable Guarantees for Fair Outcomes via Contrastive Information Estimation

2 code implementations11 Jan 2021 Umang Gupta, Aaron M Ferber, Bistra Dilkina, Greg Ver Steeg

Controlling bias in training datasets is vital for ensuring equal treatment, or parity, between different groups in downstream applications.


Deep Generative Dual Memory Network for Continual Learning

no code implementations ICLR 2018 Nitin Kamra, Umang Gupta, Yan Liu

This phenomenon called catastrophic forgetting is a fundamental challenge to overcome before neural networks can learn continually from incoming data.

Continual Learning Hippocampus

A Sentiment-and-Semantics-Based Approach for Emotion Detection in Textual Conversations

no code implementations21 Jul 2017 Umang Gupta, Ankush Chatterjee, Radhakrishnan Srikanth, Puneet Agrawal

In this paper, we propose a novel approach to detect emotions like happy, sad or angry in textual conversations using an LSTM based Deep Learning model.

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