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function scratch close all;clear;clc; %% 人为构造具有无穷解的方程Ax=b A=randn(2,3); b=randn(2,1); %% 迭代逼近一个合格解 delta = 0.001; % 容差 rate=0.0005; % learning rate 不要太大,否则误差大 x=randn(3,1); %随机初始化 alpha=rand; %注意要非负 round=0; % 记录循环次数 % L ...
深度神经网络的组稀疏正则化 (翻译) Group Sparse Regularization for Deep Neural Networks Scardapane S, Comminiello D, Hussain A, et al 摘要: 在本论文中,我们考虑同时进行以下优化任务:深度神经网络的权重、隐层神经元的数量以及输入特征选择。虽然这三个问题通常被分开处理,但我们提出了一个简单的正则化公...
Overall, our sparse epistatic regularization method expands the machine learning toolkit for inferring and understanding fitness functions in biology. It helps us to visualize, analyze, and regularize the powerful, however less interpretable black-box models in deep learning in terms of their higher-or...
Naturally, if more intensity maps are recorded by the sensor, there will be more prior knowledge for regularization, further reducing the ill-posedness of the problem. By moving the sensor axially, the intensity maps of different defocus distances are recorded as an intensity constraint, and then...
Group sparse regularization for deep neural networks. arXiv preprint arXiv:1607.00485.Scardapane, Simone, Comminiello, Danilo, Hussain, Amir, and Uncini, Aurelio. Group sparse regularization for deep neural networks. arXiv preprint arXiv:1607.00485, 2016....
In sparsity regularization, , where W is a sparse transform with several vectorized bases. W is also termed as the dictionary. The goal of dictionary learning is to train an optimized sparse transform W, which is used for the sparse representation of x. The objective function of dictionary ...
Continual Learning with Node-Importance based Adaptive Group Sparse Regularization 2020 NeurIPS PODNet: Pooled Outputs Distillation for Small-Tasks Incremental Learning 2020 ECCV Topology-Preserving Class-Incremental Learning 2020 ECCV Uncertainty-based Continual Learning with Adaptive Regularization 2019 NeurIPS...
careful selection and preprocessing of independent variables are required53. In our study we use the LR algorithm with an l2 norm penalty. The solvers, namely newton-cholesky and liblinear, along with regularization values (1000, 100, 10), were optimized via a grid search in the validation set...
Methods based on sparse ground truth To strengthen the supervised signals, the sparse ground truth is widely incorporated into the training framework. Kuznietsov et al. [48] adopted the ground truth depth collected by LIDAR for semi-supervised learning. Besides, both the left and right depth map...