目录 概 主要内容 代码 Skorokhodov I, Burtsev M. Loss Landscape Sightseeing with Multi-Point Optimization.[J]. arXiv: Learning, 2019. @article{skorokhodov2019loss, title={Loss Landscape Sightseeing with Multi-Point Optimization.}, author={Skorokhodov, Ivan and…
http://www.ics.uci.edu/~dramanan/teaching/ics273a_winter08/lectures/lecture14.pdf Loss Function 损失函数可以看做 误差部分(loss term) + 正则化部分(regularization term) 1.1 Loss Term Gold Standard (ideal case) Hinge (SVM, soft margin) Log (logistic regression, cross en…
原文地址:An overview of gradient descent optimization algorithms An overview of gradient descent optimization algorithms Note: If you are looking for a review paper, this blog post is also available as an article on arXiv. Update 15.06.2017: Added deriva…
参考文献 1 用Net::Init().做了两件事:一.绑架所有的layers和blobs,调用 layers’SetUp() 函数.验证全部网络的正确性等一系列琐碎的事.二.初始化时给出一些日志信息Also, during initialization the Net explains its initialization by logging to INFO as it goes... 2 建网时有很多细节被隐藏,建网后,考虑有什么模式,CPU?GPU? by setting a singl…
目录 概 主要内容 feature reuse mistakes and feature similarity loss landscape module criticality pre-trained checkpoint Neyshabur B., Sedghi H., Zhang C. What is being transferred in transfer learning? arXiv preprint arXiv 2008.11687, 2020. 概 迁移学习到底迁移了什么? 主…
目录 概 主要内容 定理1 代码 Cohen J., Rosenfeld E., Kolter J. Certified Adversarial Robustness via Randomized Smoothing. International Conference on Machine Learning (ICML), 2019. @article{cohen2019certified, title={Certified Adversarial Robustness via Randomiz…
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这一讲总体上就是引入Loss Function的概念,以及让大家对优化有一个初步的认识,和其他课程里面说的内容大同小异. Loss function Multiclass svm loss multiclass svm的分类依据是,比较每个类别计算得到分数,取最大的那个作为当前的类标.该Loss鼓励的是正确的类标对应的分数应该比所有其他类标对应的分数都大至少1,否则就引入cost.具体定义如下: 这个和svm的思想是一样的,就是说不光要分对,而且要求判断对与错的分割线之间要有一定的距离,即svm…
分类器需要在识别物体变化时候具有很好的鲁棒性(robus) 线性分类器(linear classifier)理解为模板的匹配,根据数量,表达能力不足,泛化性低:理解为将图片看做在高维度区域 线性分类器对这个区域进行染色. loss function 衡量预测结果的不理性程度,并基于损失函数优化weight, 使损失函数最小,达到在训练集的理想结果. Multiclass SVM loss (两分类的SVM的泛化),使SVM损失计算了所有的不正确的例子,as follows: where 将所有不…
http://aria42.com/blog/2014/12/understanding-lbfgs/ Numerical optimization is at the core of much of machine learning. Once you’ve defined your model and have a dataset ready, estimating the parameters of your model typically boils down to minimizing…
