Corrigendum to: Performance of Deep and Shallow Neural Networks, the Universal Approximation Theorem, Activity Cliffs, and QSAR (Molecular Informatics, (2017), 36, 1-2, (1600118), 10.1002/minf.201600118);Performance of Deep and Shallow Neural Networks, the Universal Approximation Theorem, Activity Cliffs, and QSAR

Molecular Informatics

Volumn 36, Issue 1, 2017, Pages

  Winkler, David A ^a,b,c,d   Le, Tu C ^a  

^a CSIRO   (Australia)

^b MONASH UNIVERSITY   (Australia)

^c LA TROBE UNIVERSITY   (Australia)

^d FLINDERS UNIVERSITY   (Australia)

Author keywords

activity cliff; Bayesian regularized neural network; deep learning; deep neural network; shallow neural network; universal approximation theorem

Indexed keywords

APPROXIMATION THEORY; BAYESIAN NETWORKS; COMPUTATIONAL CHEMISTRY; IMAGE ENHANCEMENT; LANDFORMS; MATERIALS PROPERTIES; MULTILAYER NEURAL NETWORKS; STATISTICAL TESTS;

ACTIVITY CLIFF; APPROXIMATION THEOREM; BAYESIAN; BAYESIAN REGULARIZED NEURAL NETWORK; DEEP LEARNING; NEURAL-NETWORKS; REGULARIZED NEURAL NETWORKS; SHALLOW NEURAL NETWORK; UNIVERSAL APPROXIMATION; UNIVERSAL APPROXIMATION THEOREM;

DEEP NEURAL NETWORKS;

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; AUTOMATIC SPEECH RECOGNITION; BAYES THEOREM; DATA ANALYSIS; DEEP LEARNING; MACHINE LEARNING; MATHEMATICAL ANALYSIS; PREDICTION; PRIORITY JOURNAL; QUANTITATIVE STRUCTURE ACTIVITY RELATION; RANDOM FOREST; CHEMISTRY; COMPARATIVE STUDY; DRUG DESIGN; MOLECULAR DOCKING; MOLECULAR LIBRARY; PHARMACOLOGY; PROCEDURES; STANDARDS;

DRUG DESIGN; MACHINE LEARNING; MOLECULAR DOCKING SIMULATION; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; SMALL MOLECULE LIBRARIES;

EID: 84999748027     PISSN: 18681743     EISSN: 18681751     Source Type: Journal    
DOI: 10.1002/minf.201781141     Document Type: Erratum

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