Newest papers

Forward-Only Learning for Shared-Kernel Wavelet Neural Networks without Backpropagation

Yongfei Guo, Xuan Sun, Ang Li, Zhao Zhang, Yuanhao Gong · 2026 · arXiv

DOI: 10.48550/arXiv.2610.29697 · open-access PDF

Chinese

反向扩散仍然是主导的培训范式,使用后向梯度和本地更新,在深层网络中可能遭受消失或爆炸的梯度。尽管前向扩散算法避免了传统的后向扩散,但它仍然主要是本地或层面的,激励全球整个网络学习仅使用前向评估。对于共享内核的 wavelet 神经网络,我们建议基于 CMA-ES 的全球前向只学习框架。

Machine translation (Meta M2M100); the English original follows.

Abstract

Backpropagation remains the dominant training paradigm, using backward gradients and local updates that can suffer from vanishing or exploding gradients in deep networks. Although the Forward-Forward algorithm avoids conventional backpropagation, it remains mainly local or layer-wise, motivating global whole-network learning using forward evaluations only. For shared-kernel wavelet neural networks, we therefore propose a CMA-ES-based global forward-only learning framework. We further introduce structure-aware parameter decomposition and channel-specific credit assignment, factorizing the searc

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