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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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07/30/2021
A common variable minimax theorem for graphs
Let 𝒢 = {G_1 = (V, E_1), …, G_m = (V, E_m)} be a collection of m graphs ...
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05/31/2020
Doubly-Stochastic Normalization of the Gaussian Kernel is Robust to Heteroskedastic Noise
A fundamental step in many data-analysis techniques is the construction ...
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04/15/2020
LOCA: LOcal Conformal Autoencoder for standardized data coordinates
We propose a deep-learning based method for obtaining standardized data ...
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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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11/17/2017
Manifold learning with bi-stochastic kernels
In this paper we answer the following question: what is the infinitesima...
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09/14/2017
Two-sample Statistics Based on Anisotropic Kernels
The paper introduces a new kernel-based Maximum Mean Discrepancy (MMD) s...
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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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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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09/24/2015
Provable approximation properties for deep neural networks
We discuss approximation of functions using deep neural nets. Given a fu...
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07/01/2015