b'Ju Sun'

research

∙ 03/23/2023

Predicting the Future of the CMS Detector: Crystal Radiation Damage and Machine Learning at the LHC

The 75,848 lead tungstate crystals in CMS experiment at the CERN Large H...

0 Bhargav Joshi, et al. ∙

research

∙ 03/23/2023

Optimization and Optimizers for Adversarial Robustness

Empirical robustness evaluation (RE) of deep learning models against adv...

0 Hengyue Liang, et al. ∙

research

∙ 03/06/2023

Robust Autoencoders for Collective Corruption Removal

Robust PCA is a standard tool for learning a linear subspace in the pres...

0 Taihui Li, et al. ∙

research

∙ 02/17/2023

Welfare and Fairness Dynamics in Federated Learning: A Client Selection Perspective

Federated learning (FL) is a privacy-preserving learning technique that ...

0 Yash Travadi, et al. ∙

research

∙ 02/15/2023

Interpretable Deep Learning Methods for Multiview Learning

Technological advances have enabled the generation of unique and complem...

0 Hengkang Wang, et al. ∙

research

∙ 11/02/2022

Practical Phase Retrieval Using Double Deep Image Priors

Phase retrieval (PR) concerns the recovery of complex phases from comple...

0 Zhong Zhuang, et al. ∙

research

∙ 10/03/2022

NCVX: A General-Purpose Optimization Solver for Constrained Machine and Deep Learning

Imposing explicit constraints is relatively new but increasingly pressin...

0 Buyun Liang, et al. ∙

research

∙ 10/02/2022

Optimization for Robustness Evaluation beyond ℓ_p Metrics

Empirical evaluation of deep learning models against adversarial attacks...

0 Hengyue Liang, et al. ∙

research

∙ 08/18/2022

Blind Image Deblurring with Unknown Kernel Size and Substantial Noise

Blind image deblurring (BID) has been extensively studied in computer vi...

20 Zhong Zhuang, et al. ∙

research

∙ 12/11/2021

Early Stopping for Deep Image Prior

Deep image prior (DIP) and its variants have showed remarkable potential...

0 Hengkang Wang, et al. ∙

research

∙ 11/27/2021

NCVX: A User-Friendly and Scalable Package for Nonconvex Optimization in Machine Learning

Optimizing nonconvex (NCVX) problems, especially nonsmooth and constrain...

0 Buyun Liang, et al. ∙

research

∙ 10/23/2021

Self-Validation: Early Stopping for Single-Instance Deep Generative Priors

Recent works have shown the surprising effectiveness of deep generative ...

0 Taihui Li, et al. ∙

research

∙ 06/09/2021

Rethink Transfer Learning in Medical Image Classification

Transfer learning (TL) with deep convolutional neural networks (DCNNs) h...

0 Le Peng, et al. ∙

research

∙ 06/09/2021

Phase Retrieval using Single-Instance Deep Generative Prior

Several deep learning methods for phase retrieval exist, but most of the...

0 Kshitij Tayal, et al. ∙

research

∙ 06/03/2021

A Prospective Observational Study to Investigate Performance of a Chest X-ray Artificial Intelligence Diagnostic Support Tool Across 12 U.S. Hospitals

Importance: An artificial intelligence (AI)-based model to predict COVID...

0 Ju Sun, et al. ∙

research

∙ 05/24/2021

Towards Low-Photon Nanoscale Imaging: Holographic Phase Retrieval via Maximum Likelihood Optimization

A new algorithmic framework is presented for holographic phase retrieval...

0 David A. Barmherzig, et al. ∙

research

∙ 03/20/2020

Inverse Problems, Deep Learning, and Symmetry Breaking

In many physical systems, inputs related by intrinsic system symmetries ...

1 Kshitij Tayal, et al. ∙

research

∙ 02/07/2019

Dual-Reference Design for Holographic Coherent Diffraction Imaging

A new reference design is introduced for Holographic Coherent Diffractio...

0 David A. Barmherzig, et al. ∙

research

∙ 01/19/2019

Holographic Phase Retrieval and Optimal Reference Design

A general mathematical framework and recovery algorithm is presented for...

0 David A. Barmherzig, et al. ∙

research

∙ 10/25/2018

Subgradient Descent Learns Orthogonal Dictionaries

This paper concerns dictionary learning, i.e., sparse coding, a fundamen...

0 Yu Bai, et al. ∙

research

∙ 08/10/2018

Weighted AdaGrad with Unified Momentum

Integrating adaptive learning rate and momentum techniques into SGD lead...

0 Fangyu Zou, et al. ∙

research

∙ 12/06/2017

A Local Analysis of Block Coordinate Descent for Gaussian Phase Retrieval

While convergence of the Alternating Direction Method of Multipliers (AD...

0 David Barmherzig, et al. ∙

research

∙ 02/22/2016

A Geometric Analysis of Phase Retrieval

Can we recover a complex signal from its Fourier magnitudes? More genera...

0 Ju Sun, et al. ∙

research

∙ 11/15/2015

Complete Dictionary Recovery over the Sphere II: Recovery by Riemannian Trust-region Method

We consider the problem of recovering a complete (i.e., square and inver...

0 Ju Sun, et al. ∙

research

∙ 11/11/2015

Complete Dictionary Recovery over the Sphere I: Overview and the Geometric Picture

We consider the problem of recovering a complete (i.e., square and inver...

0 Ju Sun, et al. ∙

research

∙ 10/21/2015

When Are Nonconvex Problems Not Scary?

In this note, we focus on smooth nonconvex optimization problems that ob...

0 Ju Sun, et al. ∙

research

∙ 04/26/2015

Complete Dictionary Recovery over the Sphere

We consider the problem of recovering a complete (i.e., square and inver...

0 Ju Sun, et al. ∙

research

∙ 12/15/2014

Finding a sparse vector in a subspace: Linear sparsity using alternating directions

Is it possible to find the sparsest vector (direction) in a generic subs...

0 Qing Qu, et al. ∙

research

∙ 11/05/2012

Efficient Point-to-Subspace Query in ℓ^1: Theory and Applications in Computer Vision

Motivated by vision tasks such as robust face and object recognition, we...

0 Ju Sun, et al. ∙

research

∙ 08/02/2012

Efficient Point-to-Subspace Query in ℓ^1 with Application to Robust Object Instance Recognition

Motivated by vision tasks such as robust face and object recognition, we...

0 Ju Sun, et al. ∙

research

∙ 11/22/2010

Closed-Form Solutions to A Category of Nuclear Norm Minimization Problems

It is an efficient and effective strategy to utilize the nuclear norm ap...

0 Guangcan Liu, et al. ∙

research

∙ 10/27/2010

Selective Image Super-Resolution

In this paper we propose a vision system that performs image Super Resol...

0 Ju Sun, et al. ∙

research

∙ 10/14/2010

Robust Recovery of Subspace Structures by Low-Rank Representation

In this work we address the subspace recovery problem. Given a set of da...

0 Guangcan Liu, et al. ∙

research

∙ 09/20/2010

Robust Low-Rank Subspace Segmentation with Semidefinite Guarantees

Recently there is a line of research work proposing to employ Spectral C...

0 Yuzhao Ni, et al. ∙

Ju Sun

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