Search Results for author: Neil Joshi

Found 5 papers, 0 papers with code

Triangular Dropout: Variable Network Width without Retraining

no code implementations29 Sep 2021 Edward W Staley, Corban G Rivera, Neil Joshi

One of the most fundamental design choices in neural networks is layer width: it affects the capacity of what a network can learn and determines the complexity of the solution.

Reinforcement Learning (RL)

AI Fairness via Domain Adaptation

no code implementations15 Mar 2021 Neil Joshi, Phil Burlina

While deep learning (DL) approaches are reaching human-level performance for many tasks, including for diagnostics AI, the focus is now on challenges possibly affecting DL deployment, including AI privacy, domain generalization, and fairness.

Domain Generalization Fairness

TARA: Training and Representation Alteration for AI Fairness and Domain Generalization

no code implementations11 Dec 2020 William Paul, Armin Hadzic, Neil Joshi, Fady Alajaji, Phil Burlina

Our experiments also demonstrate the ability of these novel metrics in assessing the Pareto efficiency of the proposed methods.

Domain Generalization Fairness +1

Addressing Artificial Intelligence Bias in Retinal Disease Diagnostics

no code implementations28 Apr 2020 Philippe Burlina, Neil Joshi, William Paul, Katia D. Pacheco, Neil M. Bressler

Using novel generative methods for addressing missing subpopulation training data (DR-referable darker-skin) achieved instead accuracy, for lighter-skin, of 72. 0% (65. 8%, 78. 2%), and for darker-skin, of 71. 5% (65. 2%, 77. 8%), demonstrating closer parity (delta=0. 5%) in accuracy across subpopulations (Welch t-test t=0. 111, P=. 912).

Domain Generalization

Deep Learning based Retinal OCT Segmentation

no code implementations29 Jan 2018 Mike Pekala, Neil Joshi, David E. Freund, Neil M. Bressler, Delia Cabrera DeBuc, Philippe M. Burlina

The results show that the proposed methods compare favorably with state of the art techniques, resulting in the smallest mean unsigned error values and associated standard deviations, and performance is comparable with human annotation of retinal layers from OCT when there is only mild retinopathy.

regression Segmentation +1

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