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20182023
most citedFederated Learning Enables Big Data for Rare Cancer Boundary Detection

390 citations · 416 across the 19 of their papers we have counts for

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6 papers · 1 filter

cs.LG2022★ 390 cited

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Sarthak Pati, Ujjwal Baid, Brandon Edwards +276

Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally shar…

cs.LG2022★ 6 cited

Prospective Learning: Principled Extrapolation to the Future

Ashwin De Silva, Rahul Ramesh, Lyle Ungar +40

Learning is a process which can update decision rules, based on past experience, such that future performance improves. Traditionally, machine learning is often evaluated under the…

cs.LG2020★ 1 cited

Deep sr-DDL: Deep Structurally Regularized Dynamic Dictionary Learning to Integrate Multimodal and Dynamic Functional Connectomics data for Multidimensional Clinical Characterizations

Niharika Shimona D'Souza, Mary Beth Nebel, Deana Crocetti +4

We propose a novel integrated framework that jointly models complementary information from resting-state functional MRI (rs-fMRI) connectivity and diffusion tensor imaging (DTI) tr…

cs.LG2020

A Coupled Manifold Optimization Framework to Jointly Model the Functional Connectomics and Behavioral Data Spaces

Niharika Shimona D'Souza, Mary Beth Nebel, Nicholas Wymbs +2

The problem of linking functional connectomics to behavior is extremely challenging due to the complex interactions between the two distinct, but related, data domains. We propose…

cs.LG2020★ 4 cited

A Deep-Generative Hybrid Model to Integrate Multimodal and Dynamic Connectivity for Predicting Spectrum-Level Deficits in Autism

Niharika Shimona D'Souza, Mary Beth Nebel, Deana Crocetti +4

We propose an integrated deep-generative framework, that jointly models complementary information from resting-state functional MRI (rs-fMRI) connectivity and diffusion tensor imag…

cs.LG2020

Integrating Neural Networks and Dictionary Learning for Multidimensional Clinical Characterizations from Functional Connectomics Data

Niharika Shimona D'Souza, Mary Beth Nebel, Nicholas Wymbs +2

We propose a unified optimization framework that combines neural networks with dictionary learning to model complex interactions between resting state functional MRI and behavioral…