arXiv AI By Robert Bain, Marjan Rashidi, Elizabeth Chrastil

Pairwise Learning Increases Data Efficiency in Deep Neural Networks

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arXiv Machine Learning
Aug 3

MoPET: Parameter-Efficient Mixture-of-Experts for Unified Medical Image Classification

arXiv:2607. 29462v1 Announce Type: cross Abstract: Adapting deep learning models to profound clinical heterogeneity typically relies on parameter-efficient fine-tuning (PEFT) to avoid the severe overfitting associated with full end-to-end network updates.

By Sebastian Doerrich, Daniel W\"urtinger, Francesco Di Salvo, Shyam Nandan Rai, Christian Ledig
arXiv AI
Jun 3

FFR: Forward-Forward Learning for Regression

arXiv:2606. 03927v1 Announce Type: cross Abstract: The Forward-Forward (FF) algorithm offers a computationally efficient and biologically plausible alternative to backpropagation (BP) by training neural networks through purely local, layer-wise optimization.

By Xinyang Liu, Xuanyu Liang, Shiqi Ding, Boyang Li, Zhiqiang Que, Jiayang Li, Guosheng Hu