而这就是梯度下降,一模一样:Stochastic Gradient Langevin Dynamics(SGLD)。 用神经网络比喻。量子场论的场\phi,对应神经网络的权重w。量子场论的 actionS,对应神经网络的 lossL。宇宙的运行,对应神经网络的优化过程: \frac{\partial w}{\partial \tau} = - \frac{\partial L}{\partial w} + \text{noise}...
其中最大谜团在于,Transformer为什么仅依靠一个「简单的预测损失」就能从梯度训练动态(gradient training dynamics)中涌现出高效的表征? 最近田渊栋博士公布了团队的最新研究成果,以数学严格方式,分析了1层Transformer(一个自注意力层加一个解码器层)在下一个token预测任务上的SGD训练动态。 论文链接:https://arxiv.org/...
其中最大谜团在于,Transformer为什么仅依靠一个「简单的预测损失」就能从梯度训练动态(gradient training dynamics)中涌现出高效的表征? 最近田渊栋博士公布了团队的最新研究成果,以数学严格方式,分析了1层Transformer(一个自注意力层加一个解码器层)在下一个token预测任务上的SGD训练动态。 论文链接:https://arxiv.org/...
We analyze the dynamics of streaming stochastic gradient descent (SGD) in the high-dimensional limit when applied to generalized linear models and multi-index models (e.g. logistic regression, phase retrieval) with general data-covariance. In particular, we demonstrate a deterministic equivalent of ...
标题:SDGym: Low-Code Reinforcement Learning Environments using System Dynamics Models 机构:谷歌研究院 相关领域:大模型、模型环境设计 地址:https://arxiv.org/pdf/2310.12494 19. 因果结构驱动的文本OOD泛化增强 标题:Causal-structure Driven Augmentations for Text OOD Generalization ...
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An excessively high learning rate can lead to unstable training dynamics, while an overly conservative rate can slow down the convergence. Furthermore, the stochastic nature of SGD introduces noise [72], [73] into the optimization process, potentially hindering the search for optimal solutions[74]...
Benaïm, M.: Dynamics of stochastic approximation algorithms. In: Seminaire de Probabilites XXXIII, pp. 1–68. Springer, Cham (2006) Benaïm, M., Hofbauer, J., Sorin, S.: Stochastic approximations and differential inclusions. SIAM J. Control. Optim. 44(1), 328–348 (2005) Article Ma...
Alchemist: The Potion Monger is a mixture of simulation puzzle and RPG game, in which you can leave your lab, venture into the world and change it with your brews! Take the role of apprentice of the alchemical arts, in a world full of anthropomorphic (described or thought of as having ...
This has been successfully applied to generalization theory by exploiting the fractal properties of those dynamics. However, the derived bounds depend on mutual information (decoupling) terms that are beyond the reach of computability. In this work, we prove generalization bounds over the trajectory ...