Year: 2023
Type: preprint
Abstract: This report explores the theory that explains the high sparsity phenomenon \citep{tosato2023emergent} observed in the forward-forward algorithm \citep{hinton2022forward}. The two theorems proposed predict the sparsity changes of a single data point's activation in two cases: Theorem \ref{theorem:1}: Decrease the goodness of the whole batch. Theorem \ref{theorem:2}: Apply the complete forward forward algorithm to decrease the goodness for negative data and increase the goodness for positive data. The theory aligns well with the experiments tested on the MNIST dataset.
Source: arXiv (Cornell University)
Author: Yang, Yukun
Institution: Graz University of Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4388650622