b'Zhihui Zhu'

research

∙ 06/15/2023

Stable Tomography for Structured Quantum States

The reconstruction of quantum states from experimental measurements, oft...

0 Zhen Qin, et al. ∙

research

∙ 03/13/2023

OTOV2: Automatic, Generic, User-Friendly

The existing model compression methods via structured pruning typically ...

0 Tianyi Chen, et al. ∙

research

∙ 01/25/2023

A Provable Splitting Approach for Symmetric Nonnegative Matrix Factorization

The symmetric Nonnegative Matrix Factorization (NMF), a special but impo...

0 Xiao Li, et al. ∙

research

∙ 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...

0 Xiao Li, et al. ∙

research

∙ 10/24/2022

Revisiting Sparse Convolutional Model for Visual Recognition

Despite strong empirical performance for image classification, deep neur...

0 Xili Dai, et al. ∙

research

∙ 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...

8 Jinxin Zhou, et al. ∙

research

∙ 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...

16 Lijun Ding, et al. ∙

research

∙ 09/19/2022

Neural Collapse with Normalized Features: A Geometric Analysis over the Riemannian Manifold

When training overparameterized deep networks for classification tasks, ...

0 Can Yaras, et al. ∙

research

∙ 09/09/2022

Sparsity-guided Network Design for Frame Interpolation

DNN-based frame interpolation, which generates intermediate frames from ...

5 Tianyu Ding, et al. ∙

research

∙ 07/09/2022

Error Analysis of Tensor-Train Cross Approximation

Tensor train decomposition is widely used in machine learning and quantu...

19 Zhen Qin, et al. ∙

research

∙ 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...

4 Jinxin Zhou, et al. ∙

research

∙ 02/28/2022

Robust Training under Label Noise by Over-parameterization

Recently, over-parameterized deep networks, with increasingly more netwo...

0 Sheng Liu, et al. ∙

research

∙ 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...

0 Lijun Ding, et al. ∙

research

∙ 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...

18 Tianyi Chen, et al. ∙

research

∙ 05/06/2021

A Geometric Analysis of Neural Collapse with Unconstrained Features

We provide the first global optimization landscape analysis of Neural Co...

18 Zhihui Zhu, et al. ∙

research

∙ 03/18/2021

CDFI: Compression-Driven Network Design for Frame Interpolation

DNN-based frame interpolation–that generates the intermediate frames giv...

3 Tianyu Ding, et al. ∙

research

∙ 03/01/2021

Convolutional Normalization: Improving Deep Convolutional Network Robustness and Training

Normalization techniques have become a basic component in modern convolu...

13 Sheng Liu, et al. ∙

research

∙ 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...

6 Chong You, et al. ∙

research

∙ 06/11/2020

Recovery and Generalization in Over-Realized Dictionary Learning

In over two decades of research, the field of dictionary learning has ga...

0 Jeremias Sulam, et al. ∙

research

∙ 04/07/2020

Orthant Based Proximal Stochastic Gradient Method for ℓ_1-Regularized Optimization

Sparsity-inducing regularization problems are ubiquitous in machine lear...

0 Tianyi Chen, et al. ∙

research

∙ 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...

46 Qing Qu, et al. ∙

research

∙ 12/05/2019

Analysis of the Optimization Landscapes for Overcomplete Representation Learning

We study nonconvex optimization landscapes for learning overcomplete rep...

8 Qing Qu, et al. ∙

research

∙ 12/03/2019

Viewpoint-Aware Loss with Angular Regularization for Person Re-Identification

Although great progress in supervised person re-identification (Re-ID) h...

0 Zhihui Zhu, et al. ∙

research

∙ 11/12/2019

Nonsmooth Optimization over Stiefel Manifold: Riemannian Subgradient Methods

Nonsmooth Riemannian optimization is a still under explored subfield of ...

0 Xiao Li, et al. ∙

research

∙ 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, ...

9 Qing Qu, et al. ∙

research

∙ 07/26/2019

Incremental Methods for Weakly Convex Optimization

We consider incremental algorithms for solving weakly convex optimizatio...

0 Xiao Li, et al. ∙

research

∙ 04/22/2019

Provable Bregman-divergence based Methods for Nonconvex and Non-Lipschitz Problems

The (global) Lipschitz smoothness condition is crucial in establishing t...

0 Qiuwei Li, et al. ∙

research

∙ 12/24/2018

Dual Principal Component Pursuit: Probability Analysis and Efficient Algorithms

Recent methods for learning a linear subspace from data corrupted by out...

7 Zhihui Zhu, et al. ∙

research

∙ 11/14/2018

Dropping Symmetry for Fast Symmetric Nonnegative Matrix Factorization

Symmetric nonnegative matrix factorization (NMF), a special but importan...

0 Zhihui Zhu, et al. ∙

research

∙ 11/07/2018

Global Optimality in Distributed Low-rank Matrix Factorization

We study the convergence of a variant of distributed gradient descent (D...

0 Zhihui Zhu, et al. ∙

research

∙ 09/24/2018

Nonconvex Robust Low-rank Matrix Recovery

In this paper we study the problem of recovering a low-rank matrix from ...

0 Xiao Li, et al. ∙

research

∙ 05/13/2018

The Global Optimization Geometry of Shallow Linear Neural Networks

We examine the squared error loss landscape of shallow linear neural net...

0 Zhihui Zhu, et al. ∙

research

∙ 02/12/2018

Convergence Analysis of Alternating Nonconvex Projections

We consider the convergence properties for alternating projection algori...

0 Zhihui Zhu, et al. ∙

research

∙ 12/13/2017

ROAST: Rapid Orthogonal Approximate Slepian Transform

In this paper, we provide a Rapid Orthogonal Approximate Slepian Transfo...

0 Zhihui Zhu, et al. ∙

research

∙ 04/05/2017

Geometry of Factored Nuclear Norm Regularization

This work investigates the geometry of a nonconvex reformulation of mini...

0 Qiuwei Li, et al. ∙

Zhihui Zhu

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