Biologically-plausible learning algorithms can scale to large datasets

8 November 2018

Papers citing "Biologically-plausible learning algorithms can scale to large datasets"

24 / 24 papers shown

Title
Self-Assembly of a Biologically Plausible Learning Circuit Q. Liao Liu Ziyin Yulu Gan Brian Cheung Mark Harnett Tomaso Poggio 57 0 0 31 Dec 2024
Improving equilibrium propagation without weight symmetry through Jacobian homeostasis Axel Laborieux Friedemann Zenke 30 7 0 05 Sep 2023
Brain-inspired learning in artificial neural networks: a review Samuel Schmidgall Jascha Achterberg Thomas Miconi Louis Kirsch Rojin Ziaei S. P. Hajiseyedrazi Jason Eshraghian 38 52 0 18 May 2023
Is Bio-Inspired Learning Better than Backprop? Benchmarking Bio Learning vs. Backprop Manas Gupta Sarthak Ketanbhai Modi Hang Zhang Joon Hei Lee J. Lim 32 7 0 09 Dec 2022
A Stable, Fast, and Fully Automatic Learning Algorithm for Predictive Coding Networks Tommaso Salvatori Yuhang Song Yordan Yordanov Beren Millidge Zheng R. Xu Lei Sha Cornelius Emde Rafal Bogacz Thomas Lukasiewicz 36 10 0 16 Nov 2022
Scaling Forward Gradient With Local Losses Mengye Ren Simon Kornblith Renjie Liao Geoffrey E. Hinton 81 49 0 07 Oct 2022
Towards Scaling Difference Target Propagation by Learning Backprop Targets M. Ernoult Fabrice Normandin A. Moudgil Sean Spinney Eugene Belilovsky Irina Rish Blake A. Richards Yoshua Bengio 19 28 0 31 Jan 2022
Error-driven Input Modulation: Solving the Credit Assignment Problem without a Backward Pass Giorgia Dellaferrera Gabriel Kreiman 31 53 0 27 Jan 2022
Benchmarking the Accuracy and Robustness of Feedback Alignment Algorithms Albert Jiménez Sanfiz Mohamed Akrout OOD AAML 22 8 0 30 Aug 2021
How to Train Your Wide Neural Network Without Backprop: An Input-Weight Alignment Perspective Akhilan Boopathy Ila Fiete 44 9 0 15 Jun 2021
Tightening the Biological Constraints on Gradient-Based Predictive Coding Nick Alonso Emre Neftci 27 7 0 30 Apr 2021
Reverse Differentiation via Predictive Coding Tommaso Salvatori Yuhang Song Thomas Lukasiewicz Rafal Bogacz Zhenghua Xu PINN 30 26 0 08 Mar 2021
Training Deep Architectures Without End-to-End Backpropagation: A Survey on the Provably Optimal Methods Shiyu Duan José C. Príncipe MQ 38 3 0 09 Jan 2021
Biological credit assignment through dynamic inversion of feedforward networks William F. Podlaski C. Machens 27 19 0 10 Jul 2020
Direct Feedback Alignment Scales to Modern Deep Learning Tasks and Architectures Julien Launay Iacopo Poli Franccois Boniface Florent Krzakala 41 63 0 23 Jun 2020
Kernelized information bottleneck leads to biologically plausible 3-factor Hebbian learning in deep networks Roman Pogodin P. Latham 24 34 0 12 Jun 2020
Two Routes to Scalable Credit Assignment without Weight Symmetry D. Kunin Aran Nayebi Javier Sagastuy-Breña Surya Ganguli Jonathan M. Bloom Daniel L. K. Yamins 34 32 0 28 Feb 2020
Large-Scale Gradient-Free Deep Learning with Recursive Local Representation Alignment Alexander Ororbia A. Mali Daniel Kifer C. Lee Giles 23 2 0 10 Feb 2020
Principled Training of Neural Networks with Direct Feedback Alignment Julien Launay Iacopo Poli Florent Krzakala 24 35 0 11 Jun 2019
Putting An End to End-to-End: Gradient-Isolated Learning of Representations Sindy Löwe Peter O'Connor Bastiaan S. Veeling SSL 11 143 0 28 May 2019
Deep Learning without Weight Transport Mohamed Akrout Collin Wilson Peter C. Humphreys Timothy Lillicrap D. Tweed CVBM 29 132 0 10 Apr 2019
Decoupled Greedy Learning of CNNs Eugene Belilovsky Michael Eickenberg Edouard Oyallon 11 114 0 23 Jan 2019
Feedback alignment in deep convolutional networks Theodore H. Moskovitz Ashok Litwin-Kumar L. F. Abbott 27 60 0 12 Dec 2018
Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex Q. Liao T. Poggio 213 255 0 13 Apr 2016