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English(EN) Sparse Bayesian Neural Networks via Sticky Piecewise Deterministic Markov Processes

新方法实现具有精确零权重采样的稀疏贝叶斯神经网络

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种使用粘性分段确定性马尔可夫过程（PDMPs）创建稀疏贝叶斯神经网络（BNNs）的新颖方法。该方法通过采用 spike-and-slab 先验，在贝叶斯框架内直接整合剪枝，从而管理与剪枝和参数估计相关的各种不确定性。所提出的基于 PDMP 的采样技术允许对 BNN 进行渐近精确的后验采样，其中“粘性”特性使得在采样过程中可以将权重精确设置为零。该方法已证明稀疏 BNN 与其密集对应物相比，可以提供对输入噪声的更好鲁棒性。 AI

影响 引入了一种用于稀疏贝叶斯神经网络的新型采样技术，有望提高鲁棒性和效率。

排序理由 关于贝叶斯神经网络新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

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新方法实现具有精确零权重采样的稀疏贝叶斯神经网络

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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · August Arnstad, Geir Olve Storvik, Filippo Pagani, Leiv R{\o}nneberg ·

    稀疏贝叶斯神经网络通过粘性分段确定性马尔可夫过程实现

    arXiv:2610.32753v1 Announce Type: new Abstract: There is growing evidence that the performance of a neural network depends largely on a small subset of its weights, which are typically identified by training a dense network and pruning it afterwards. Within a Bayesian framework, …

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