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10/24/2022
Revisiting Sparse Convolutional Model for Visual Recognition
Despite strong empirical performance for image classification, deep neur...
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10/12/2022
Large Models are Parsimonious Learners: Activation Sparsity in Trained Transformers
This paper studies the curious phenomenon for machine learning models wi...
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10/04/2022
Are All Losses Created Equal: A Neural Collapse Perspective
While cross entropy (CE) is the most commonly used loss to train deep ne...
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03/02/2022
On the Optimization Landscape of Neural Collapse under MSE Loss: Global Optimality with Unconstrained Features
When training deep neural networks for classification tasks, an intrigui...
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02/28/2022
Robust Training under Label Noise by Over-parameterization
Recently, over-parameterized deep networks, with increasingly more netwo...
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10/08/2021
Learning a Self-Expressive Network for Subspace Clustering
State-of-the-art subspace clustering methods are based on self-expressiv...
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05/21/2021
ReduNet: A White-box Deep Network from the Principle of Maximizing Rate Reduction
This work attempts to provide a plausible theoretical framework that aim...
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05/06/2021
A Geometric Analysis of Neural Collapse with Unconstrained Features
We provide the first global optimization landscape analysis of Neural Co...
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03/01/2021
Convolutional Normalization: Improving Deep Convolutional Network Robustness and Training
Normalization techniques have become a basic component in modern convolu...
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11/30/2020
Incremental Learning via Rate Reduction
Current deep learning architectures suffer from catastrophic forgetting,...
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10/27/2020
Deep Networks from the Principle of Rate Reduction
This work attempts to interpret modern deep (convolutional) networks fro...
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10/08/2020
A Critique of Self-Expressive Deep Subspace Clustering
Subspace clustering is an unsupervised clustering technique designed to ...
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06/30/2020
Deep Isometric Learning for Visual Recognition
Initialization, normalization, and skip connections are believed to be t...
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06/16/2020
Robust Recovery via Implicit Bias of Discrepant Learning Rates for Double Over-parameterization
Recent advances have shown that implicit bias of gradient descent on ove...
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06/15/2020
Learning Diverse and Discriminative Representations via the Principle of Maximal Coding Rate Reduction
To learn intrinsic low-dimensional structures from high-dimensional data...
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06/11/2020
Recovery and Generalization in Over-Realized Dictionary Learning
In over two decades of research, the field of dictionary learning has ga...
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06/07/2020
Self-Representation 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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05/08/2020
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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05/04/2020
Stochastic Sparse Subspace Clustering
State-of-the-art subspace clustering methods are based on self-expressiv...
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02/26/2020
Rethinking Bias-Variance Trade-off for Generalization of Neural Networks
The classical bias-variance trade-off predicts that bias decreases and v...
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12/30/2019
Basis Pursuit and Orthogonal Matching Pursuit for Subspace-preserving Recovery: Theoretical Analysis
Given an overcomplete dictionary A and a signal b = Ac^* for some sparse...
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05/01/2019
Self-Supervised Convolutional Subspace Clustering Network
Subspace clustering methods based on data self-expression have become ve...
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10/03/2018
Semiparametric Regression using Variational Approximations
Semiparametric regression offers a flexible framework for modeling non-l...
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08/17/2018
On Geometric Analysis of Affine Sparse Subspace Clustering
Sparse subspace clustering (SSC) is a state-of-the-art method for segmen...
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04/12/2017
Provable Self-Representation Based Outlier Detection in a Union of Subspaces
Many computer vision tasks involve processing large amounts of data cont...
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10/17/2016
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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05/09/2016
Oracle Based Active Set Algorithm for Scalable Elastic Net Subspace Clustering
State-of-the-art subspace clustering methods are based on expressing eac...
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07/05/2015
