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20202023
most citedAddressing catastrophic forgetting for medical domain expansion

4 citations · 11 across the 5 of their papers we have counts for

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Showing cs.LGShow all

5 papers · 1 filter

cs.LG2023

Structuring Representation Geometry with Rotationally Equivariant Contrastive Learning

Sharut Gupta, Joshua Robinson, Derek Lim +2

Self-supervised learning converts raw perceptual data such as images to a compact space where simple Euclidean distances measure meaningful variations in data. In this paper, we ex…

cs.LG20222 cited

FL Games: A Federated Learning Framework for Distribution Shifts

Sharut Gupta, Kartik Ahuja, Mohammad Havaei +2

Federated learning aims to train predictive models for data that is distributed across clients, under the orchestration of a server. However, participating clients typically each h…

cs.LG2022

FL Games: A federated learning framework for distribution shifts

Sharut Gupta, Kartik Ahuja, Mohammad Havaei +2

Federated learning aims to train predictive models for data that is distributed across clients, under the orchestration of a server. However, participating clients typically each h…

cs.LG20214 cited

Addressing catastrophic forgetting for medical domain expansion

Sharut Gupta, Praveer Singh, Ken Chang +13

Model brittleness is a key concern when deploying deep learning models in real-world medical settings. A model that has high performance at one institution may suffer a significant…

cs.LG20202 cited

The unreasonable effectiveness of Batch-Norm statistics in addressing catastrophic forgetting across medical institutions

Sharut Gupta, Praveer Singh, Ken Chang +9

Model brittleness is a primary concern when deploying deep learning models in medical settings owing to inter-institution variations, like patient demographics and intra-institutio…