Beyond Backprop: Online Alternating Minimization with Auxiliary Variables

24 June 2018

Papers citing "Beyond Backprop: Online Alternating Minimization with Auxiliary Variables"

38 / 38 papers shown

Title
Strengthening the Internal Adversarial Robustness in Lifted Neural Networks Christopher Zach AAML 51 0 0 10 Mar 2025
Two Tales of Single-Phase Contrastive Hebbian Learning R. Høier Christopher Zach 33 1 0 13 Feb 2024
Duality Principle and Biologically Plausible Learning: Connecting the Representer Theorem and Hebbian Learning Yanis Bahroun D. Chklovskii Anirvan M. Sengupta 24 0 0 02 Aug 2023
Local Learning with Neuron Groups Adeetya Patel Michael Eickenberg Eugene Belilovsky 29 5 0 18 Jan 2023
Convergence Rates of Training Deep Neural Networks via Alternating Minimization Methods Jintao Xu Chenglong Bao W. Xing 8 3 0 30 Aug 2022
A Theoretical Framework for Inference Learning Nick Alonso Beren Millidge J. Krichmar Emre Neftci 17 16 0 01 Jun 2022
Deep Regression Ensembles Antoine Didisheim Bryan Kelly Semyon Malamud UQCV 17 4 0 10 Mar 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
Low-Pass Filtering SGD for Recovering Flat Optima in the Deep Learning Optimization Landscape Devansh Bisla Jing Wang A. Choromańska 27 34 0 20 Jan 2022
A Convergent ADMM Framework for Efficient Neural Network Training Junxiang Wang Hongyi Li Liang Zhao 11 1 0 22 Dec 2021
Layer-Parallel Training of Residual Networks with Auxiliary-Variable Networks Qi Sun Hexin Dong Zewei Chen Jiacheng Sun Zhenguo Li Bin Dong 29 1 0 10 Dec 2021
Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications Khaled B. Letaief Yuanming Shi Jianmin Lu Jianhua Lu 43 417 0 24 Nov 2021
Latent Equilibrium: A unified learning theory for arbitrarily fast computation with arbitrarily slow neurons Paul Haider B. Ellenberger Laura Kriener Jakob Jordan Walter Senn Mihai A. Petrovici 27 24 0 27 Oct 2021
Gradient Forward-Propagation for Large-Scale Temporal Video Modelling Mateusz Malinowski Dimitrios Vytiniotis G. Swirszcz Viorica Patraucean João Carreira 30 8 0 15 Jun 2021
Front Contribution instead of Back Propagation Swaroop Mishra Anjana Arunkumar 17 0 0 10 Jun 2021
Stochastic Block-ADMM for Training Deep Networks Saeed Khorram Xiao Fu Mohamad H. Danesh Zhongang Qi Li Fuxin 34 3 0 01 May 2021
Finding High-Value Training Data Subset through Differentiable Convex Programming Soumik Das Arshdeep Singh Saptarshi Chatterjee S. Bhattacharya Sourangshu Bhattacharya TDI 27 7 0 28 Apr 2021
Learning DNN networks using un-rectifying ReLU with compressed sensing application W. Hwang Shih-Shuo Tung 11 2 0 18 Jan 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
Etat de lárt sur lápplication des bandits multi-bras Djallel Bouneffouf 21 0 0 04 Jan 2021
SGB: Stochastic Gradient Bound Method for Optimizing Partition Functions Junchang Wang A. Choromańska 27 0 0 03 Nov 2020
Adaptive Subcarrier, Parameter, and Power Allocation for Partitioned Edge Learning Over Broadband Channels Dingzhu Wen Ki-Jun Jeon M. Bennis Kaibin Huang 33 10 0 08 Oct 2020
A Practical Layer-Parallel Training Algorithm for Residual Networks Qi Sun Hexin Dong Zewei Chen Weizhen Dian Jiacheng Sun Yitong Sun Zhenguo Li Bin Dong ODL 22 2 0 03 Sep 2020
Do ideas have shape? Idea registration as the continuous limit of artificial neural networks H. Owhadi 90 14 0 10 Aug 2020
Deriving Differential Target Propagation from Iterating Approximate Inverses Yoshua Bengio 12 24 0 29 Jul 2020
Spectral Clustering using Eigenspectrum Shape Based Nystrom Sampling Djallel Bouneffouf 7 1 0 21 Jul 2020
Computing the Dirichlet-Multinomial Log-Likelihood Function Djallel Bouneffouf 6 2 0 17 Jul 2020
Contextual Bandit with Missing Rewards Djallel Bouneffouf Sohini Upadhyay Y. Khazaeni OffRL 12 9 0 13 Jul 2020
Online learning with Corrupted context: Corrupted Contextual Bandits Djallel Bouneffouf 6 11 0 26 Jun 2020
Sideways: Depth-Parallel Training of Video Models Mateusz Malinowski G. Swirszcz João Carreira Viorica Patraucean MDE 43 13 0 17 Jan 2020
Learning Without Loss V. Elser 9 11 0 29 Oct 2019
How can AI Automate End-to-End Data Science? Charu C. Aggarwal Djallel Bouneffouf Horst Samulowitz Beat Buesser T. Hoang ... Tejaswini Pedapati Parikshit Ram Ambrish Rawat Martin Wistuba Alexander G. Gray 88 14 0 22 Oct 2019
The HSIC Bottleneck: Deep Learning without Back-Propagation Kurt Wan-Duo Ma J. P. Lewis W. Kleijn BDL 31 128 0 05 Aug 2019
Contrastive Learning for Lifted Networks Christopher Zach V. Estellers SSL 17 12 0 07 May 2019
Predict Globally, Correct Locally: Parallel-in-Time Optimal Control of Neural Networks P. Parpas Corey Muir OOD 11 12 0 07 Feb 2019
DANTE: Deep AlterNations for Training nEural networks Vaibhav Sinha Sneha Kudugunta Adepu Ravi Sankar Surya Teja Chavali Purushottam Kar V. Balasubramanian 6 3 0 01 Feb 2019
Decoupled Greedy Learning of CNNs Eugene Belilovsky Michael Eickenberg Edouard Oyallon 8 114 0 23 Jan 2019
Lifted Neural Networks Armin Askari Geoffrey Negiar Rajiv Sambharya L. Ghaoui 31 37 0 03 May 2018