An analysis on the use of autoencoders for representation learning: fundamentals, learning task case studies, explainability and challenges

21 May 2020

Papers citing "An analysis on the use of autoencoders for representation learning: fundamentals, learning task case studies, explainability and challenges"

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Title
GPO-VAE: Modeling Explainable Gene Perturbation Responses utilizing GRN-Aligned Parameter Optimization Seungheun Baek Soyon Park Y. T. Chok Mogan Gim Jaewoo Kang DRL 47 0 0 31 Jan 2025
Representing Camera Response Function by a Single Latent Variable and Fully Connected Neural Network Yunfeng Zhao S. Ferguson Huiyu Zhou K. Rafferty 13 3 0 08 Sep 2022
Reducing Data Complexity using Autoencoders with Class-informed Loss Functions D. Charte F. Charte Francisco Herrera 11 14 0 11 Nov 2021
Learning Adversarially Fair and Transferable Representations David Madras Elliot Creager T. Pitassi R. Zemel FaML 233 674 0 17 Feb 2018