Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks

3 September 2019

Papers citing "Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks"

9 / 9 papers shown

Title
Biologically Plausible Training of Deep Neural Networks Using a Top-down Credit Assignment Network Jian-Hui Chen Cheng-Lin Liu Zuoren Wang 21 0 0 01 Aug 2022
Error-driven Input Modulation: Solving the Credit Assignment Problem without a Backward Pass Giorgia Dellaferrera Gabriel Kreiman 21 53 0 27 Jan 2022
Hardware Beyond Backpropagation: a Photonic Co-Processor for Direct Feedback Alignment Julien Launay Iacopo Poli Kilian Muller Gustave Pariente I. Carron L. Daudet Florent Krzakala S. Gigan MoE 13 18 0 11 Dec 2020
Align, then memorise: the dynamics of learning with feedback alignment Maria Refinetti Stéphane dÁscoli Ruben Ohana Sebastian Goldt 26 36 0 24 Nov 2020
Tuning Convolutional Spiking Neural Network with Biologically-plausible Reward Propagation Tielin Zhang Shuncheng Jia Xiang Cheng Bo Xu 23 47 0 09 Oct 2020
A 28-nm Convolutional Neuromorphic Processor Enabling Online Learning with Spike-Based Retinas Charlotte Frenkel J. Legat D. Bol 16 42 0 13 May 2020
A Deep Unsupervised Feature Learning Spiking Neural Network with Binarized Classification Layers for EMNIST Classification using SpykeFlow Ruthvik Vaila John N. Chiasson V. Saxena 19 22 0 26 Feb 2020
Spiking Neural Predictive Coding for Continual Learning from Data Streams Alexander Ororbia 16 25 0 23 Aug 2019
Neuromorphic Deep Learning Machines Emre Neftci C. Augustine Somnath Paul Georgios Detorakis BDL 127 257 0 16 Dec 2016