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Computing the Sparsity Pattern of Hessians Using Automatic Differentiation
(Assoc Computing MachineryNew YorkEUA, 2014)
Finding archetypal patterns for binary questionnaires
One of the main challenges researchers face is to identify the most relevant features in a prediction model. As a consequence, many regularized methods seeking sparsity have flourished. Although sparse, their solutions may ...
THE STRUCTURE OF THE EIGENVECTORS OF SPARSE MATRICES
(Elsevier Science IncNew York, 1994)
Discretisation Of Sparse Linear Systems: An Optimisation Approach
(ELSEVIER SCIENCE BVAMSTERDAM, 2015)
CriPAV: street-level crime patterns analysis and visualization
(2015-08)
Extracting and analyzing crime patterns in big cities is a challenging spatiotemporal problem. The hardness of the problem is linked to two main factors, the sparse nature of the crime activity and its spread in large ...
MaxDropout: Deep neural network regularization based on maximum output values
(2020-01-01)
Different techniques have emerged in the deep learning scenario, such as Convolutional Neural Networks, Deep Belief Networks, and Long Short-Term Memory Networks, to cite a few. In lockstep, regularization methods, which ...
An improved finite element space for discontinuous pressures
(ELSEVIER SCIENCE SA, 2010)
We consider incompressible Stokes flow with an internal interface at which the pressure is discontinuous, as happens for example in problems involving surface tension. We assume that the mesh does not follow the interface, ...
CompactNets: Compact Hierarchical Compositional Networks for Visual Recognition
(2020)
CNN-based models currently provide state-of-the-art performance in image categorization tasks. While these methods are powerful in terms of representational capacity, they are generally not conceived with explicit means ...
Construction Of Minimum Energy High-order Helmholtz Bases For Structured Elements
(ACADEMIC PRESS INC ELSEVIER SCIENCESAN DIEGO, 2016)
Footprint removal from seismic data with residual dictionary learning
(Society of Exploration Geophysicists, 2020-03)
Dictionary learning (DL) is a machine learning technique that can be used to find a sparse representation of a given data set by means of a relatively small set of atoms, which are learned from the input data. DL allows ...