2024
Learnable Filters for Geometric Scattering Modules
Tong A, Wenkel F, Bhaskar D, Macdonald K, Grady J, Perlmutter M, Krishnaswamy S, Wolf G. Learnable Filters for Geometric Scattering Modules. IEEE Transactions On Signal Processing 2024, 72: 2939-2952. DOI: 10.1109/tsp.2024.3378001.Peer-Reviewed Original ResearchGraph neural networksGraph classification benchmarksEncode graph structureData exploration tasksGeometric scattering transformGraph wavelet filtersClassification benchmarksLearned representationsLearnable filtersLearning parametersGraph structureNeural networkExploration tasksWavelet filtersBand-pass featureBiochemical domainNetworkAdaptive tuningWaveletGraphPredictive performanceScattering modulationScattering transformMathematical propertiesFilterAssessing 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
2022
Exploring the Geometry and Topology of Neural Network Loss Landscapes
Horoi S, Huang J, Rieck B, Lajoie G, Wolf G, Krishnaswamy S. Exploring the Geometry and Topology of Neural Network Loss Landscapes. Lecture Notes In Computer Science 2022, 13205: 171-184. DOI: 10.1007/978-3-031-01333-1_14.Peer-Reviewed Original ResearchLoss landscapeNon-linear dimensionality reductionLandscape geometryGeneralization performanceLocal minimaDimensionality reduction techniquesLinear natureReduction techniquesDimensionality reductionGeometrySuch visualization methodsCaptures featuresJumpNetwork's abilityNeural networkTrajectoriesRecent workTopologyNetwork
2020
Uncovering the Folding Landscape of RNA Secondary Structure Using Deep Graph Embeddings
Castro E, Benz A, Tong A, Wolf G, Krishnaswamy S. Uncovering the Folding Landscape of RNA Secondary Structure Using Deep Graph Embeddings. 2020, 00: 4519-4528. DOI: 10.1109/bigdata50022.2020.9378305.Peer-Reviewed Original Research
2019
TraVeLGAN: Image-to-image Translation by Transformation Vector Learning
Amodio M, Krishnaswamy S. TraVeLGAN: Image-to-image Translation by Transformation Vector Learning. 2019, 00: 8975-8984. DOI: 10.1109/cvpr.2019.00919.Peer-Reviewed Original ResearchImage translationSiamese networkHigh-level shapeVector learningDiscriminator networkUnsupervised modelTarget domainOriginal imageLatent spaceChallenging problemSignificant clutterNovel GANComplex domainNetworkImagesTwo-network systemTexture differencesVector transformationBetter resultsSemanticsRecent yearsDomainSystemLearningClutter
2012
Generalized SAT-sweeping for post-mapping optimization
Welp T, Krishnaswamy S, Kuehlmann A. Generalized SAT-sweeping for post-mapping optimization. 2012, 814-819. DOI: 10.1145/2228360.2228507.Peer-Reviewed Original ResearchTree-based approachEffective optimization algorithmSuboptimal networksMapping problemBoolean satisfiabilityEfficient implementationAlgorithm searchesMapping algorithmOptimization algorithmAlgorithmSynthesis flowTechnology libraryNetworkExperimental resultsOptimization stepImplementationNetsReimplementationSatisfiabilityPractice useSupport netLibraryOptimizationSearch