Constrained Parameter Inference as a Principle for Learning

22 March 2022

Papers citing "Constrained Parameter Inference as a Principle for Learning"

8 / 8 papers shown

Title
Efficient Deep Learning with Decorrelated Backpropagation Sander Dalm Joshua Offergeld Nasir Ahmad Marcel van Gerven 146 5 0 03 May 2024
Effective Learning with Node Perturbation in Multi-Layer Neural Networks Sander Dalm Marcel van Gerven Nasir Ahmad AAML 77 0 0 02 Oct 2023
GAIT-prop: A biologically plausible learning rule derived from backpropagation of error Nasir Ahmad Marcel van Gerven L. Ambrogioni AAML 50 25 0 11 Jun 2020
Explainable Deep Learning: A Field Guide for the Uninitiated Gabrielle Ras Ning Xie Marcel van Gerven Derek Doran AAML XAI 95 377 0 30 Apr 2020
Dendritic cortical microcircuits approximate the backpropagation algorithm João Sacramento Rui Ponte Costa Yoshua Bengio Walter Senn 78 312 0 26 Oct 2018
Reducing Overfitting in Deep Networks by Decorrelating Representations Michael Cogswell Faruk Ahmed Ross B. Girshick C. L. Zitnick Dhruv Batra 72 414 0 19 Nov 2015
Automatic differentiation in machine learning: a survey A. G. Baydin Barak A. Pearlmutter Alexey Radul J. Siskind PINN AI4CE ODL 156 2,800 0 20 Feb 2015
How Auto-Encoders Could Provide Credit Assignment in Deep Networks via Target Propagation Yoshua Bengio 95 184 0 29 Jul 2014