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A General Framework for Interpretable Neural Learning based on Local Information-Theoretic Goal Functions

A General Framework for Interpretable Neural Learning based on Local Information-Theoretic Goal Functions

Abdullah Makkeh
Marcel Graetz
Andreas C. Schneider
David A. Ehrlich
V. Priesemann
Michael Wibral
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Papers citing "A General Framework for Interpretable Neural Learning based on Local Information-Theoretic Goal Functions"

3 / 3 papers shown
Title
Forward-Forward Contrastive Learning
Forward-Forward Contrastive Learning
Md. Atik Ahamed
Jin Chen
A. Imran
MedIm
15
6
0
04 May 2023
Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges
Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges
B. Bischl
Martin Binder
Michel Lang
Tobias Pielok
Jakob Richter
...
Theresa Ullmann
Marc Becker
A. Boulesteix
Difan Deng
Marius Lindauer
77
268
0
13 Jul 2021
Local plasticity rules can learn deep representations using self-supervised contrastive predictions
Local plasticity rules can learn deep representations using self-supervised contrastive predictions
Bernd Illing
Jean-Paul Ventura
G. Bellec
W. Gerstner
SSL
DRL
28
66
0
16 Oct 2020