The term robustness is ubiquitous in modern Machine Learning (ML). However, its meaning varies depending on context and community. Researchers either focus on narrow technical definitions, such as adversarial robustness, natural distribution shifts, and performativity, or they simply leave open what ...
【机器学习基石笔记六】---Theory of Generalization(一般化理论---举一反三),程序员大本营,技术文章内容聚合第一站。
This paper introduces a novel measure-theoretic theory for machine learning that does not require statistical assumptions. Based on this theory, a new regularization method in deep learning is derived and shown to outperform previous methods in CIFAR-10, CIFAR-100, and SVHN. Moreover, the proposed...
[Baby Set Theory 2] Outline of Equinumerosity Theory Cantor-Bernstein Theorem (康托-伯恩斯坦定理)If a\preceq b and b\preceq a, then a\approx b.Insight of proof.Create a Hilbert's Hotel in b. \square Cantor's Theorem (康托… Grim STAT(3) - Asymptotic Theory 呦呦Ruming:STAT...
DeepMind早早地就预见到了这个问题,在17年A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning这篇文章里面就指出,即使是在非常简单的环境,self-play训练出的模型也会严重过拟合于对手策略,所以做星际的时候他们压根就没想过要用一个模型解决所有问题,而是要引入game theory去做一个zero-sum Markov...
机器学习基石 - Theory of Generalization Restriction of Break Points growth function mH(N) mH(N): max number of dichotomies 漏出一线曙光的点break point...机器学习基石上(Machine Learning Foundations)—Mathematical Foundations Hsuan-Tien Lin, 林轩田,副教授林轩田...
Information theory was find by Claude_ShannonClaude Shannon. It has quantified entropy. This is key measure of information which is usually expressed by the average number of bits needed to store or communicate... (Machine learning|Inverse problems) - Regularization Regularization refers to a proce...
这是我们machine learning theory大作业 我的theory水平差到爆炸。 我只是写了又觉得,不如发出来。要不这个专栏要长草了。 TL;DR 这篇paper主要在证generalization error和input output mutual information的关系。酱 下面是作者在nips做的spotlight。大概可能不知道有没有版权问题。其实作者讲的很清楚了。(我其实就是...
The fundamental tension of machine learning is between fitting our data well, but also fitting the data assimplyas possible. How Do We Know If Our Model Is Good? Theoretically: Interesting field:generalization theory Based on ideas of measuring model simplicity / complexity ...
for some Hermitian observable{O}_{{x}_{i},{y}_{i}}^{{{\rm{loss}}}. As is common in classical learning theory, the prediction error bounds will depend on the largest (absolute) value that the loss function can attain. In our case, we therefore assume{C}_{{{\rm{loss}}}:=\mat...