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 propertiesFilter
2022
The Manifold Scattering Transform for High-Dimensional Point Cloud Data.
Chew J, Steach H, Viswanath S, Wu H, Hirn M, Needell D, Krishnaswamy S, Perlmutter M. The Manifold Scattering Transform for High-Dimensional Point Cloud Data. Proceedings Of Machine Learning Research 2022, 196: 67-78. PMID: 37159759, PMCID: PMC10164360.Peer-Reviewed Original ResearchDeep feature extractorDimensional point cloud dataPoint cloud dataHigh-dimensional point cloudsFeature extractorClassification taskCloud dataPoint cloudsLow-dimensional manifoldScattering transformSignal classificationPractical schemeDiffusion mapsInitial workNaturalistic systemExtractorDatasetCloudInvariance propertiesTransformTask
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