
Incremental Learning via Rate Reduction
Current deep learning architectures suffer from catastrophic forgetting,...
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Deep Networks from the Principle of Rate Reduction
This work attempts to interpret modern deep (convolutional) networks fro...
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A Critique of SelfExpressive Deep Subspace Clustering
Subspace clustering is an unsupervised clustering technique designed to ...
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Deep Isometric Learning for Visual Recognition
Initialization, normalization, and skip connections are believed to be t...
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Robust Recovery via Implicit Bias of Discrepant Learning Rates for Double Overparameterization
Recent advances have shown that implicit bias of gradient descent on ove...
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Learning Diverse and Discriminative Representations via the Principle of Maximal Coding Rate Reduction
To learn intrinsic lowdimensional structures from highdimensional data...
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Recovery and Generalization in OverRealized Dictionary Learning
In over two decades of research, the field of dictionary learning has ga...
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SelfRepresentation Based Unsupervised Exemplar Selection in a Union of Subspaces
Finding a small set of representatives from an unlabeled dataset is a co...
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Is an Affine Constraint Needed for Affine Subspace Clustering?
Subspace clustering methods based on expressing each data point as a lin...
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Stochastic Sparse Subspace Clustering
Stateoftheart subspace clustering methods are based on selfexpressiv...
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Rethinking BiasVariance Tradeoff for Generalization of Neural Networks
The classical biasvariance tradeoff predicts that bias decreases and v...
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Basis Pursuit and Orthogonal Matching Pursuit for Subspacepreserving Recovery: Theoretical Analysis
Given an overcomplete dictionary A and a signal b = Ac^* for some sparse...
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SelfSupervised Convolutional Subspace Clustering Network
Subspace clustering methods based on data selfexpression have become ve...
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Semiparametric Regression using Variational Approximations
Semiparametric regression offers a flexible framework for modeling nonl...
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On Geometric Analysis of Affine Sparse Subspace Clustering
Sparse subspace clustering (SSC) is a stateoftheart method for segmen...
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Provable SelfRepresentation Based Outlier Detection in a Union of Subspaces
Many computer vision tasks involve processing large amounts of data cont...
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Structured Sparse Subspace Clustering: A Joint Affinity Learning and Subspace Clustering Framework
Subspace clustering refers to the problem of segmenting data drawn from ...
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Oracle Based Active Set Algorithm for Scalable Elastic Net Subspace Clustering
Stateoftheart subspace clustering methods are based on expressing eac...
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Scalable Sparse Subspace Clustering by Orthogonal Matching Pursuit
Subspace clustering methods based on ℓ_1, ℓ_2 or nuclear norm regulariza...
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Chong You
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