2024
Geometric scattering on measure spaces
Chew J, Hirn M, Krishnaswamy S, Needell D, Perlmutter M, Steach H, Viswanath S, Wu H. Geometric scattering on measure spaces. Applied And Computational Harmonic Analysis 2024, 70: 101635. DOI: 10.1016/j.acha.2024.101635.Peer-Reviewed Original ResearchConvolutional neural networkGeometric deep learningDeep learningNeural networkSuccess of convolutional neural networksModel of convolutional neural networkMeasure spaceScattering transformData-driven graphsInvariance propertiesRiemannian manifoldsNon-Euclidean structureUndirected graphWavelet-based transformCompact Riemannian manifoldsData structuresRate of convergenceSpherical imagesNetwork stabilityHigh-dimensional single-cell dataData setsDirected graphDiffusion-mapsSigned graphGraphLearnable 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 propertiesFilterBayesian Spectral Graph Denoising with Smoothness Prior
Leone S, Sun X, Perlmutter M, Krishnaswamy S. Bayesian Spectral Graph Denoising with Smoothness Prior. 2024, 00: 1-6. DOI: 10.1109/ciss59072.2024.10480177.Peer-Reviewed Original ResearchPresence of noisy dataGraph signal processingMaximum A PosterioriAffinity graphDenoised featuresGaussian noiseNoisy dataHigh-dimensionalComplex dataAlgorithm's abilityA-posterioriModel of noise generationSmoothness priorsRestored signalDistributed noiseSignal processingAlgorithmImage dataGraphFrequency domainNoiseNoise generationDenoisingWhite noiseSmoothing
2023
Wire Before You Walk
Asmara T, Bhaskar D, Adelstein I, Krishnaswamy S, Perlmutter M. Wire Before You Walk. 2023, 00: 714-716. DOI: 10.1109/ieeeconf59524.2023.10477089.Peer-Reviewed Original ResearchGraph Fourier MMD for Signals on Graphs
Leone S, Venkat A, Huguet G, Tong A, Wolf G, Krishnaswamy S. Graph Fourier MMD for Signals on Graphs. 2023, 00: 1-6. DOI: 10.1109/sampta59647.2023.10301384.Peer-Reviewed Original ResearchState-space characterizationEmbedding of distributionsRNA-sequencing data analysisSingle-cell RNA-sequencing data analysisMeaningful gene clustersPairs of distributionsOptimization problemProbability distributionGene clusterEuclidean spaceSpace characterizationAnalytical solutionSuch graphsGene embeddingsDisconnected graphsScale invarianceGene selectionGraphSuch distancesEmbeddingGenesNovel typeDistributionNumerous methodsBenchmark datasetsGeodesic Sinkhorn For Fast and Accurate Optimal Transport on Manifolds
Huguet G, Tong A, Zapatero M, Tape C, Wolf G, Krishnaswamy S. Geodesic Sinkhorn For Fast and Accurate Optimal Transport on Manifolds. 2023, 00: 1-6. DOI: 10.1109/mlsp55844.2023.10285995.Peer-Reviewed Original ResearchOptimal transport distanceHeat kernelHigh-dimensional single-cell dataGraph LaplacianOptimal transportSinkhornChebyshev polynomialsEfficient computationSuch computationsManifold graphComputationSingle-cell dataBarycenterGround distanceKernelData scienceManifoldPolynomialsLaplacianCell dataGraphDistributionDistanceDatapointsTransport distance
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