Journal ArticleDOI
Numerical Recipes: The Art of Scientific Computing
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This article is published in Computers & Geosciences.The article was published on 1989-01-01. It has received 5246 citations till now.read more
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Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
Journal ArticleDOI
Refinement of macromolecular structures by the maximum-likelihood method.
TL;DR: The likelihood function for macromolecular structures is extended to include prior phase information and experimental standard uncertainties and the results derived are consistently better than those obtained from least-squares refinement.
Journal ArticleDOI
Phonons and related crystal properties from density-functional perturbation theory
TL;DR: In this paper, the current status of lattice-dynamical calculations in crystals, using density-functional perturbation theory, with emphasis on the plane-wave pseudopotential method, is reviewed.
Journal ArticleDOI
Extending the treatment of backbone energetics in protein force fields: limitations of gas-phase quantum mechanics in reproducing protein conformational distributions in molecular dynamics simulations.
TL;DR: To improve the treatment of the peptide backbone, quantum mechanical and molecular mechanical calculations were undertaken on the alanine, glycine, and proline dipeptides, and the results were combined with molecular dynamics simulations of proteins in crystal and aqueous environments to enhance the quality of the CHARMM force field.
Posted Content
Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions
TL;DR: In this article, a modular framework for constructing randomized algorithms that compute partial matrix decompositions is presented, which uses random sampling to identify a subspace that captures most of the action of a matrix and then the input matrix is compressed to this subspace, and the reduced matrix is manipulated deterministically to obtain the desired low-rank factorization.