5、 Max-Norm Regularization 虽然单独使用 dropout 就可以使得模型获得良好表现,不过,如果搭配Max-Norm 食用的话,那么效果更佳。 对于每一个神经元 Max-Norm Regularization 的目的在于限制输入链接权重的大小,使得||w||_2 \ll r,其中 r 是Max-Norm 可调节超参数,||.||_2是L2范数。在每一个 training step...
回到顶部 Max-Norm Regularization 对于每个节点,max-norm regularization 会对权重w进行限制‖w‖2≤r: (1)w←wr‖w‖2 实例代码: View Code
内容提示: Practical Large-Scale Optimizationfor Max-Norm RegularizationJason LeeInstitute of Computational and Mathematical EngineeringStanford Universityemail: jl115@yahoo.comBenjamin RechtDepartment of Computer SciencesUniversity of Wisconsin-Madisonemail: brecht@cs.wisc.eduRuslan SalakhutdinovBrain and Cognitive...
Xu, and P. Li, "Online optimization for max-norm regularization," in NIPS, 2014.Jie Shen, Huan Xu, and Ping Li. Online optimization for max-norm regularization. In Advances in Neural Information Processing Systems, pages 1718-1726, 2014....
Practical Large-Scale Optimization for Max-Norm Regularization 来自 掌桥科研 喜欢 0 阅读量: 206 作者:J Lee,B Recht,R Salakhutdinov,N Srebro 摘要: The max-norm was proposed as a convex matrix regularizer in [1] and was shown to be empirically superior to the trace-norm for collaborative ...
Practical Large-Scale Optimization for Max-Norm Regularization Jason Lee Benjamin Recht Institute of Computational and Mathematical Engineering Department of Computer Sciences Stanford University University of Wisconsin-Madison email: jl115@ email: brecht@ Ruslan Salakhutdinov Nathan Srebro Brain and Cognitive ...
kernel_regularizer=l2(regularization), kernel_constraint=maxnorm(weight_constraint), activation='sigmoid')) model.compile( loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])returnmodel 开发者ID:bogdan-kulynych,项目名称:textfool,代码行数:45,代码来源:model.py ...
对于每一个神经元 Max-Norm Regularization 的目的在于限制输入链接权重的大小,使得||w||_2 \ll r,其中 r 是Max-Norm 可调节超参数,||.||_2是L2范数。在每一个 training step 需要计算||w||_2,以确保它小于r,如果需要对w进行调整,则使用下式进行调整: ...
Online Optimization for Large-Scale Max-Norm Regularizationdoi:10.1007/S10994-017-5628-6Jie ShenHuan XuPing LiSpringer US
, \\(\\ell _{2,0}\\) -norm regularization, with the Softmax model to find a stable row-sparse solution, where we can select the features in group according to the solution of the projected matrix, and the classification performance can be improved by Softmax. Extensive experiments on ...