and using the graph poly-laplacian to define a prior. in this bayesian context, these works identify the higher-order regularizers as beneficial for fitting functions which are expected to have higher degree of regularity. our focus will be on recovering the labels g as well as some of its ...
In this paper, we develop a general framework for 3D 2-phase image segmentation, based on constrained 2 2 minimization of a non-local regularizer, the Euler鈥揕agrange derivative of which is the discrete graph-Laplacian of a weighted graph, associated with the image voxels. It involves a ...
The matrices \(L_{x} = L_{y}\) are the 3-point graph Laplacians for the 1-D chain. We depict the true toy connectivity \(W_{\text{true}}\) as well as a number of low-rank solutions output by our method in Fig. 2. Both the mask and the regularization are required for ...
This is our third contribution: we give a formal boosting algorithm for the regularized exponential rado-loss which boost with any of the ridge, lasso, SLOPE, `1, or elastic net regularizer, using the same master routine for all. Because the regularized exponential rado-loss is the equivalent ...
Motivated by the desire to preserve the expected lengths of vectors regardless of the direction, we formulate our regularizer as Ew,y∼N (0,I) JTwy 2 − a 2 , (4) where y are random images with normally distributed pixel intensities, and w ∼ f (z), where z are...
IJCAI 2013, Proceedings of the 23rd International Joint Conference on Artificial Intelligence, Beijing, China, August 3-9, 2013. Computational Disaster Management. Pascal Van Hentenryck 电商所评分:2 原文链接 谷歌学术 必应学术 百度学术 Soft Robotics: The Next Generation of Intelligent Machines. ...
For instance, one should be able to add the residual of the Allen–Cahn equation as a regularizer [72] when training the RNN. This idea of training the network from additional information obtained by enforcing physical laws via penalty constraints has been recently put forward in fluid mechanics...
Optical Flow Estimation using Laplacian Mesh Energy (project) Wenbin Li, Darren Cosker, Matthew Brown, Rui Tang Computationally Efficient Regression on a Dependency Graph for Human Pose Estimation (PDF, poster) Kota Hara (UMD), Rama Chellappa (UMD) Consensus of k-NNs for Robust Neighborhood Sele...
Manifold-RegularizerPinballLossIn this paper, we consider the regularized learning schemes based on l1-regularizer and pinball loss in a data dependent hypothesis space. The target is the error analysis for the quantile regression learning. There is no regularized condition with the kernel function, ...
It is a natural generalization from the graph Laplacian and spread-out regularizers, and empirically it addresses the drawback of each regularizer alone when applied to the extreme classification setup. With the proposed techniques, we attain or improve upon the state-of-the-art on most widely ...