
Permutation invariant networks to learn Wasserstein metrics
Understanding the space of probability measures on a metric space equipp...
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Learning Potentials of Quantum Systems using Deep Neural Networks
Machine Learning has wide applications in a broad range of subjects, inc...
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Gaining insight into SARSCoV2 infection and COVID19 severity using selfsupervised edge features and Graph Neural Networks
Graph Neural Networks (GNN) have been extensively used to extract meanin...
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Learning aligned embeddings for semisupervised word translation using Maximum Mean Discrepancy
Word translation is an integral part of language translation. In machine...
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Disease State Prediction From SingleCell Data Using Graph Attention Networks
Singlecell RNA sequencing (scRNAseq) has revolutionized biological dis...
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TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics
It is increasingly common to encounter data from dynamic processes captu...
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Coarse Graining of Data via Inhomogeneous Diffusion Condensation
Big data often has emergent structure that exists at multiple levels of ...
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Compressed Diffusion
Diffusion maps are a commonly used kernelbased method for manifold lear...
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Finding Archetypal Spaces for Data Using Neural Networks
Archetypal analysis is a type of factor analysis where data is fit by a ...
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Graph Spectral Regularization for Neural Network Interpretability
Deep neural networks can learn meaningful representations of data. Howev...
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Modeling Dynamics with Deep TransitionLearning Networks
Markov processes, both classical and higher order, are often used to mod...
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David van Dijk
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