Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1–22 (2010). Article Google Scholar Geweke, J. Measures of conditional linear dependence and feedback between time series. J. Am. Stat. Assoc. 79, 907–915 (1984). Article MathSciNet Google ...
Standard approaches to deep learning have not provided a solution to this problem. The standard approach trains neural networks with stochastic gradient descent (SGD) implemented by the backpropagation algorithm (BP)2. BP is a non-local learning algorithm generally considered biologically implausible3,4...
[2] I. Bayer, X. He, B. Kanagal, and S. Rendle. A generic coordinate descent frame work for learning from implicit feedback. In WWW, 2017. [3] M. Blondel, A. Fujino, N. Ueda, and M. Ishihata. Higher-order factorization machines. In NIPS, 2016. ...
We improve upon cyclic coordinate descent, which requires O(N2) operations per coordinate sweep. By using a multilevel decomposition scheme, we are able to compute a coordinate sweep in O(N log N) operations @mdash; without performing any explicit Fourier transforms. In some tests, runtime ...
machine-learning-algorithmspython3variational-inferencesparse-regressionstochastic-gradient-descentgumbel-softmaxregression-algorithmsspike-and-slab-prior UpdatedJun 7, 2019 Python tumaer/SITE Star10 Code Issues Pull requests Sparse Identification of Truncation Errors (SITE) for Data-Driven Discovery of Modifie...
Manipulating Sparse Double Descent 来自 arXiv.org 喜欢 0 阅读量: 2 作者: YS Zhang 摘要: This paper investigates the double descent phenomenon in two-layer neural networks, focusing on the role of L1 regularization and representation dimensions. It explores an alternative double descent phenomenon, ...
Unknowns are updated in the descent direction and then a new approximate matrix of the Hessian is calculated. This calculation is repeated until the gradient of the objective function becomes sufficiently small. However, since the problem is nonlinear, obtained solutions may differ depending on the ...
On the other hand, our learning scheme utilizes a stochastic gradient descent method, which requires fewer graph-cut inference computations for each training loop. To the best of our knowledge, there is little work on using discriminative sparse dictionaries for semantic segmentation. This is ...
The objective of the training process is to learn the filter weights, usually via a stochastic gradient descent-based excursion through the space of weights. The training process typically employs a forward-propagation calculation for each training example, a measurement of the error between the ...
The network can then learn from those errors using an algorithm, such as the stochastic gradient descent algorithm, to update the weights of the of the neural network. FIGS. 11A & 11B illustrate an exemplary convolutional neural network. FIG. 11A illustrates various layers within a CNN. As ...