English(EN) Low-rank surrogate modeling and stochastic zero-order optimization for training of neural networks with black-box layers

新框架训练包含不可微分物理组件的混合人工智能

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-05 04:00

研究人员开发了一个新的框架，用于训练结合了数字组件和物理黑盒层的混合神经网络。该方法使用随机零阶优化和动态低秩代理模型，以实现梯度通过不可微分物理设备的传播。该方法在计算机视觉、音频分类和语言建模任务中均显示出有效性，准确性可与纯数字基线相媲美。 AI

影响为将不可微分物理组件集成到可扩展、端到端可训练的人工智能系统中提供了实际途径。

排序理由学术论文，详细介绍了用于训练具有黑盒物理层的混合神经网络的新框架。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Andrei Chertkov, Artem Basharin, Mikhail Saygin, Evgeny Frolov, Stanislav Straupe, Ivan Oseledets · 2026-05-05 04:00

低秩代理模型和随机零阶优化用于黑盒层神经网络的训练

arXiv:2509.15113v2 Announce Type: replace Abstract: The growing demand for energy-efficient, high-performance AI systems has led to increased attention on alternative computing platforms (e.g., photonic, neuromorphic) due to their potential to accelerate learning and inference. H…