我们还通过经验证明,SGD的随机性对于压缩来说是不必要的。为此,我们考虑了两种不同的训练过程:离线随机梯度下降(SGD),它从固定大小的数据集中学习,并通过从数据集中重复采样单个样本并计算相对于该单个样本的误差梯度来更新权重(实践中使用的典型过程)。批量梯度下降(BGD),它从固定大小的数据集学习,并使用所有示例的...
Mhaskar. Theory of deep learning iii: explaining the non-overfitting puzzle. ???, ???(???):???, ???T. Poggio, K. Kawaguchi, Q. Liao, B. Miranda, L. Rosasco, X. Boix, J. Hidary, and H. N. Mhaskar. Theory of deep learning III: the non-overfitting puzzle. CBMM memo 073,...
Effective Theory of the NTK at Initialization Kernel Learning Representation Learning 0.2 The Theoretical Minimum 从high-level 给出文章方法的overview,揭示为什么 a first-principles 理论可能可以解释Deep Learning (DL) 简单的假设神经网络是一个参数方程: f(x;θ) ,这里x是输入 、theta是网络参数向量用来控...
Recently, a new breed of deep learning algorithms have emerged for high-nuisance inference tasks that routinely yield pattern recognition systems with near- or super-human capabilities. But a fundamental question remains: Why do they work? Intuitions abound, but a coherent framework for understanding...
英文原版 The Principles of Deep Learning Theory 深度学习理论原理 理解神经网络 Daniel A. Roberts 精装 英文版 进口英语原版 作者:Daniel A. Roberts出版社:Cambridge出版时间:2023年11月 手机专享价 ¥ 当当价降价通知 ¥925.00 配送至 广东广州市 至北京市东城区...
This textbook establishes a theoretical framework for understanding deep learning models of practical relevance. With an approach that borrows from theoretical physics, Roberts and Yaida provide clear and pedagogical explanations of how realistic deep neural networks actually work. To make results from the...
5.2 Theoretical analysis of SGD 分析SGD的理论 Constant v.s. diminishing learning rate 这是一个常量学习速率和衰减学习速率的争论问题,常量学习速率可能在最后收敛阶段收敛不到最小值,而是在震荡。但衰减学习可能会导致收敛速度很慢。 New analysis for constant learning rate: realizable case ...
我不否认deep learning theory和rl theory比起来确实是更relevant,而且从数学的角度上来说更有价值,但...
2. Theory-guided deep-learning load forecasting (TgDLF) In this study, the TgDLF is used to predict the load ratio based on the EnLSTM algorithm [35], and the desired grid load can be obtained based on the load ratio and historical load. The inputs of the TgDLF include historical loa...
In Theory IIb we characterize with a mix of theory and experiments the optimization of deep convolutional networks by Stochastic Gradient Descent. The main new result in this paper is theoretical and experimental evidence for the following conjecture about SGD: SGD concentrates in probability -- like...