
LDLE: Low Distortion Local Eigenmaps
We present Low Distortion Local Eigenmaps (LDLE), a manifold learning te...
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Online Adversarial Purification based on SelfSupervision
Deep neural networks are known to be vulnerable to adversarial examples,...
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Kernelbased parameter estimation of dynamical systems with unknown observation functions
A lowdimensional dynamical system is observed in an experiment as a hig...
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Multiway Graph Signal Processing on Tensors: Integrative analysis of irregular geometries
Graph signal processing (GSP) is an important methodology for studying a...
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Visualizing the PHATE of Neural Networks
Understanding why and how certain neural networks outperform others is k...
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Learning spatiallycorrelated temporal dictionaries for calcium imaging
Calcium imaging has become a fundamental neural imaging technique, aimin...
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Spectral Embedding Norm: Looking Deep into the Spectrum of the Graph Laplacian
The extraction of clusters from a dataset which includes multiple cluste...
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Comanifold learning with missing data
Representation learning is typically applied to only one mode of a data ...
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Randomized Near Neighbor Graphs, Giant Components, and Applications in Data Science
If we pick n random points uniformly in [0,1]^d and connect each point t...
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DataDriven Tree Transforms and Metrics
We consider the analysis of high dimensional data given in the form of a...
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The Geometry of Nodal Sets and Outlier Detection
Let (M,g) be a compact manifold and let Δϕ_k = λ_k ϕ_k be the sequence ...
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Hierarchical Coupled Geometry Analysis for Neuronal Structure and Activity Pattern Discovery
In the wake of recent advances in experimental methods in neuroscience, ...
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Diffusion Nets
Nonlinear manifold learning enables highdimensional data analysis, but...
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Gal Mishne
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