2024
Assessing Neural Network Representations During Training Using Noise-Resilient Diffusion Spectral Entropy
Liao D, Liu C, Christensen B, Tong A, Huguet G, Wolf G, Nickel M, Adelstein I, Krishnaswamy S. Assessing Neural Network Representations During Training Using Noise-Resilient Diffusion Spectral Entropy. 2024, 00: 1-6. DOI: 10.1109/ciss59072.2024.10480166.Peer-Reviewed Original ResearchMutual information neural estimatorMutual informationHigh-dimensional simulation dataHigh-dimensional dataNeural network representationSpectral entropyCIFAR-10Information-theoretic measuresClass labelsSTL-10Classification networkNeural representationSelf-supervisionSupervised learningIntrinsic dimensionalityClassification accuracyNeural networkAmbient dimensionNoise-resilientNeural estimatorNetwork initializationData geometryNetwork representationOverfittingNetwork
2013
Nonlinear intrinsic variables and state reconstruction in multiscale simulations
Dsilva CJ, Talmon R, Rabin N, Coifman RR, Kevrekidis IG. Nonlinear intrinsic variables and state reconstruction in multiscale simulations. The Journal Of Chemical Physics 2013, 139: 184109. PMID: 24320256, DOI: 10.1063/1.4828457.Peer-Reviewed Original ResearchHigh-dimensional simulation dataLow-dimensional descriptionSimulation dataEnzyme reaction networksSlow time scaleStochastic simulationMeasurement noisePartial observationsPhysical phenomenaMultiscale simulationsReaction networksIntrinsic variablesSimulation ensemblesAlanine dipeptideSimulationsState reconstructionEnsembleProcess variabilityTime scalesAtomistic simulationsVariablesNoiseApproachSimple features
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