Core Concepts
"Forward-only" algorithms like PEPITA and Forward-Forward offer biologically plausible solutions to the credit assignment problem in neural networks.
Abstract
This content explores the development and analysis of "forward-only" algorithms, focusing on PEPITA and Forward-Forward. It addresses challenges, theoretical insights, empirical results, comparisons, and future directions.
Introduction
Discusses the credit assignment problem in machine learning.
Highlights issues with backpropagation in neural network training.
Bio-Inspired Learning Algorithms
Various training rules proposed for artificial neural networks.
Table comparing properties and accuracy metrics of different bio-inspired alternatives to BP.
Theoretical Analyses
Study of online learning with 1-hidden-layer neural networks.
Insights into learning dynamics with biologically plausible algorithms.
The PEPITA Learning Rule
Description of the PEPITA algorithm's clean and modulated forward passes.
Formulation of weight updates based on input modulation through error feedback.
Testing PEPITA on Deeper Networks
Experiments testing PEPITA's performance on deeper networks.
Improvement strategies like weight decay, activation normalization, and weight mirroring.
On the Relationship Between Forward-Forward and PEPITA
Comparison between FF and PEPITA algorithms.
Formulation of PEPITA as Hebbian and anti-Hebbian phases.
Discussion
Addressing challenges in forward-learning algorithms.
Limitations and Future Work
Acknowledgment of limitations in current algorithms.
Suggestions for future research directions.
Acknowledgements
Support acknowledgment from NIH grant R01EY026025 and NSF grant CCF-1231216.
References
List of references cited in the content.
Stats
"PEPITA can be approximated by an FA-like algorithm."
"Weight decay improved PEPITA's performance by 0.6%."
"WM significantly improved alignment between feedback and feedforward weights."
Quotes
"No biological plausibility issue for ML but may help understand shortcomings or offer insights into biological systems."
"PEPITA effectively implements 'feedback-alignment' with an adaptive feedback matrix."
"PEPITA-TL distinguishes between two forward passes using neuromodulatory signals."