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08/18/2023
Random Walks, Conductance, and Resistance for the Connection Graph Laplacian
We investigate the concept of effective resistance in connection graphs,...
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07/31/2023
Semi-Supervised Laplacian Learning on Stiefel Manifolds
Motivated by the need to address the degeneracy of canonical Laplace lea...
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06/14/2023
Graph Laplacian Learning with Exponential Family Noise
A common challenge in applying graph machine learning methods is that th...
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06/07/2023
SiBBlInGS: Similarity-driven Building-Block Inference using Graphs across States
Interpretable methods for extracting meaningful building blocks (BBs) un...
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05/30/2023
Hyperbolic Diffusion Embedding and Distance for Hierarchical Representation Learning
Finding meaningful representations and distances of hierarchical data is...
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03/21/2023
Non-degenerate Rigid Alignment in a Patch Framework
Given a set of overlapping local views (patches) of a dataset, we consid...
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11/10/2022
DiSC: Differential Spectral Clustering of Features
Selecting subsets of features that differentiate between two conditions ...
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11/07/2022
Implicit Graphon Neural Representation
Graphons are general and powerful models for generating graphs of varyin...
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10/31/2022
The Numerical Stability of Hyperbolic Representation Learning
Given the exponential growth of the volume of the ball w.r.t. its radius...
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10/20/2022
Learning Sample Reweighting for Accuracy and Adversarial Robustness
There has been great interest in enhancing the robustness of neural netw...
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10/04/2022
Evaluating Disentanglement in Generative Models Without Knowledge of Latent Factors
Probabilistic generative models provide a flexible and systematic framew...
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01/26/2021
LDLE: Low Distortion Local Eigenmaps
We present Low Distortion Local Eigenmaps (LDLE), a manifold learning te...
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01/23/2021
Online Adversarial Purification based on Self-Supervision
Deep neural networks are known to be vulnerable to adversarial examples,...
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09/09/2020
Kernel-based parameter estimation of dynamical systems with unknown observation functions
A low-dimensional dynamical system is observed in an experiment as a hig...
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06/30/2020
Multi-way 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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08/07/2019
Visualizing the PHATE of Neural Networks
Understanding why and how certain neural networks outperform others is k...
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02/08/2019
Learning spatially-correlated temporal dictionaries for calcium imaging
Calcium imaging has become a fundamental neural imaging technique, aimin...
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10/25/2018
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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10/16/2018
Co-manifold learning with missing data
Representation learning is typically applied to only one mode of a data ...
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11/13/2017
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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08/18/2017
Data-Driven Tree Transforms and Metrics
We consider the analysis of high dimensional data given in the form of a...
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06/05/2017
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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11/06/2015
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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06/25/2015