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06/15/2023
Stable Tomography for Structured Quantum States
The reconstruction of quantum states from experimental measurements, oft...
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03/13/2023
OTOV2: Automatic, Generic, User-Friendly
The existing model compression methods via structured pruning typically ...
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01/25/2023
A Provable Splitting Approach for Symmetric Nonnegative Matrix Factorization
The symmetric Nonnegative Matrix Factorization (NMF), a special but impo...
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12/23/2022
Principled and Efficient Transfer Learning of Deep Models via Neural Collapse
With the ever-growing model size and the limited availability of labeled...
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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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10/04/2022
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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09/21/2022
A Validation Approach to Over-parameterized Matrix and Image Recovery
In this paper, we study the problem of recovering a low-rank matrix from...
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09/19/2022
Neural Collapse with Normalized Features: A Geometric Analysis over the Riemannian Manifold
When training overparameterized deep networks for classification tasks, ...
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09/09/2022
Sparsity-guided Network Design for Frame Interpolation
DNN-based frame interpolation, which generates intermediate frames from ...
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07/09/2022
Error Analysis of Tensor-Train Cross Approximation
Tensor train decomposition is widely used in machine learning and quantu...
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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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09/23/2021
Rank Overspecified Robust Matrix Recovery: Subgradient Method and Exact Recovery
We study the robust recovery of a low-rank matrix from sparsely and gros...
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07/15/2021
Only Train Once: A One-Shot Neural Network Training And Pruning Framework
Structured pruning is a commonly used technique in deploying deep neural...
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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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03/18/2021
CDFI: Compression-Driven Network Design for Frame Interpolation
DNN-based frame interpolation–that generates the intermediate frames giv...
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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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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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06/11/2020
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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04/07/2020
Orthant Based Proximal Stochastic Gradient Method for ℓ_1-Regularized Optimization
Sparsity-inducing regularization problems are ubiquitous in machine lear...
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01/20/2020
Finding the Sparsest Vectors in a Subspace: Theory, Algorithms, and Applications
The problem of finding the sparsest vector (direction) in a low dimensio...
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12/05/2019
Analysis of the Optimization Landscapes for Overcomplete Representation Learning
We study nonconvex optimization landscapes for learning overcomplete rep...
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12/03/2019
Viewpoint-Aware Loss with Angular Regularization for Person Re-Identification
Although great progress in supervised person re-identification (Re-ID) h...
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11/12/2019
Nonsmooth Optimization over Stiefel Manifold: Riemannian Subgradient Methods
Nonsmooth Riemannian optimization is a still under explored subfield of ...
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08/28/2019
A Nonconvex Approach for Exact and Efficient Multichannel Sparse Blind Deconvolution
We study the multi-channel sparse blind deconvolution (MCS-BD) problem, ...
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07/26/2019
Incremental Methods for Weakly Convex Optimization
We consider incremental algorithms for solving weakly convex optimizatio...
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04/22/2019
Provable Bregman-divergence based Methods for Nonconvex and Non-Lipschitz Problems
The (global) Lipschitz smoothness condition is crucial in establishing t...
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12/24/2018
Dual Principal Component Pursuit: Probability Analysis and Efficient Algorithms
Recent methods for learning a linear subspace from data corrupted by out...
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11/14/2018
Dropping Symmetry for Fast Symmetric Nonnegative Matrix Factorization
Symmetric nonnegative matrix factorization (NMF), a special but importan...
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11/07/2018
Global Optimality in Distributed Low-rank Matrix Factorization
We study the convergence of a variant of distributed gradient descent (D...
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09/24/2018
Nonconvex Robust Low-rank Matrix Recovery
In this paper we study the problem of recovering a low-rank matrix from ...
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05/13/2018
The Global Optimization Geometry of Shallow Linear Neural Networks
We examine the squared error loss landscape of shallow linear neural net...
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02/12/2018
Convergence Analysis of Alternating Nonconvex Projections
We consider the convergence properties for alternating projection algori...
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12/13/2017
ROAST: Rapid Orthogonal Approximate Slepian Transform
In this paper, we provide a Rapid Orthogonal Approximate Slepian Transfo...
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04/05/2017