arXiv AI By Alireza Rahmansetayesh, Amirhossein Yousefiramandi, Maxwell Spiridonov, Sumit Raut, Kent Ziti, Pierre Baldi

Rescuing Points Trapped Inside Incorrect Decision Regions: A target for Evaluating and Improving Forward-Forward Learning

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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
arXiv Machine Learning
Sep 10

Selective Posterior Margin Regularization for Forward-Corrected Classification

The paper introduces Selective Posterior Margin Regularization (SPMR), a technique that enhances Forward correction for learning with class‑conditional label noise. SPMR preserves the Forward objective while converting disagreements between the corrected likelihood’s reverse posterior and the observed annotation into a graded update on the clean classifier. Experiments on five known‑transition benchmarks show that SPMR improves performance by 2.5–7.0 percentage points over full‑length Forward and remains 0.7–2.5 percentage points better when combined with Mixup and early stopping, with gains attributed to posterior‑space coefficients, transition‑adjusted targets, and pairwise actions.

By Zexing Zhang, Jichao Li, Tianyang Lei, XiongYi Lu, Yang Kewei
Hugging Face Trending Papers
Jun 2

FFR: Forward-Forward Learning for Regression

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. However, FF is inherently designed for classification via contrastive positive-negative sample pairs, and extending it to regression poses fundamental challenges: continuous target space lack natural "opposites" for contrastive learning, and the standard goodness function carries no information about target magnitude or ordering.