Can Forward Gradient Match Backpropagation?

Can Forward Gradient Match Backpropagation?

International Conference on Machine Learning (ICML), 2023

12 June 2023

Stéphane Rivaud

Eugene Belilovsky

Michael Eickenberg

Edouard Oyallon

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Papers citing "Can Forward Gradient Match Backpropagation?"

9 / 9 papers shown

Title
Towards Scalable Backpropagation-Free Gradient Estimation Daniel Wang Evan Markou Dylan Campbell 56 0 0 05 Nov 2025
A Unified Perspective on Optimization in Machine Learning and Neuroscience: From Gradient Descent to Neural Adaptation Jesus Garcia Fernandez Nasir Ahmad Marcel van Gerven AI4CE 225 0 0 21 Oct 2025
Low-rank surrogate modeling and stochastic zero-order optimization for training of neural networks with black-box layers Andrei Chertkov Artem Basharin Mikhail Saygin Evgeny Frolov Stanislav Straupe Ivan Oseledets 126 0 0 18 Sep 2025
TITAN-Guide: Taming Inference-Time AligNment for Guided Text-to-Video Diffusion Models Christian Simon Masato Ishii Akio Hayakawa Zhi-Wei Zhong Shusuke Takahashi Takashi Shibuya Yuki Mitsufuji 110 1 0 01 Aug 2025
Backpropagation-Free Metropolis-Adjusted Langevin Algorithm Adam Cobb Susmit Jha 141 0 0 23 May 2025
Beyond Backpropagation: Optimization with Multi-Tangent Forward Gradients Katharina Flügel D. Coquelin Marie Weiel Achim Streit Markus Gotz 231 2 0 23 Oct 2024
PETRA: Parallel End-to-end Training with Reversible Architectures Stéphane Rivaud Louis Fournier Thomas Pumir Eugene Belilovsky Michael Eickenberg Edouard Oyallon 307 3 0 04 Jun 2024
Cyclic Data Parallelism for Efficient Parallelism of Deep Neural Networks Louis Fournier Edouard Oyallon 206 0 0 13 Mar 2024
Scaling Supervised Local Learning with Augmented Auxiliary Networks Chenxiang Ma Jibin Wu Chenyang Si Kay Chen Tan 194 6 0 27 Feb 2024