no code implementations • 7 Dec 2023 • Hui Xie, Weiyu Xu, Ya Xing Wang, John Buatti, Xiaodong Wu
To combine the strengths of both approaches, we propose in this study to integrate the graph-cut approach into a deep learning network for end-to-end learning.
no code implementations • 1 Nov 2023 • Ziqing Lu, Guanlin Liu, Lifeng Cai, Weiyu Xu
Finding optimal adversarial attack strategies is an important topic in reinforcement learning and the Markov decision process.
no code implementations • 25 Oct 2023 • Fahim Ahmed Zaman, Xiaodong Wu, Weiyu Xu, Milan Sonka, Raghuraman Mudumbai
We describe a method for verifying the output of a deep neural network for medical image segmentation that is robust to several classes of random as well as worst-case perturbations i. e. adversarial attacks.
no code implementations • 16 Oct 2023 • Jirong Yi, Jingchao Gao, Tianming Wang, Xiaodong Wu, Weiyu Xu
We propose an outlier detection approach for reconstructing the ground-truth signals modeled by generative models under sparse outliers.
no code implementations • 27 Sep 2023 • Eva Riherd, Raghu Mudumbai, Weiyu Xu
We propose a general method for semantic representation of images and other data using progressive coding.
no code implementations • 28 Aug 2023 • Myung Cho, Lifeng Lai, Weiyu Xu
In this paper, we investigate the impact of imbalanced data on the convergence of distributed dual coordinate ascent in a tree network for solving an empirical loss minimization problem in distributed machine learning.
no code implementations • 9 May 2023 • Huan Cai, Catherine Xu, Weiyu Xu
In the past several decades, the world's economy has become increasingly globalized.
no code implementations • 3 Nov 2022 • Jingchao Gao, Ao Tang, Weiyu Xu
We then provide analytical and computational methods to characterize the optimal trade-off between data compression and classification error probability.
no code implementations • 8 Oct 2022 • Hui Xie, Weiyu Xu, Xiaodong Wu
Unfortunately, due to the scarcity of training data in medical imaging, it is challenging for DL networks to learn the global structure of the target surfaces, including surface smoothness.
no code implementations • 5 Aug 2020 • Jirong Yi, Myung Cho, Xiaodong Wu, Raghu Mudumbai, Weiyu Xu
In this paper, we consider the problem of designing optimal pooling matrix for group testing (for example, for COVID-19 virus testing) with the constraint that no more than $r>0$ samples can be pooled together, which we call "dilution constraint".
no code implementations • 28 Jul 2020 • Jirong Yi, Raghu Mudumbai, Weiyu Xu
We consider the theoretical problem of designing an optimal adversarial attack on a decision system that maximally degrades the achievable performance of the system as measured by the mutual information between the degraded signal and the label of interest.
no code implementations • 26 Mar 2020 • Zain Khan, Jirong Yi, Raghu Mudumbai, Xiaodong Wu, Weiyu Xu
Recent works have demonstrated the existence of {\it adversarial examples} targeting a single machine learning system.
no code implementations • 25 May 2019 • Jirong Yi, Hui Xie, Leixin Zhou, Xiaodong Wu, Weiyu Xu, Raghuraman Mudumbai
In this paper, we present a simple hypothesis about a feature compression property of artificial intelligence (AI) classifiers and present theoretical arguments to show that this hypothesis successfully accounts for the observed fragility of AI classifiers to small adversarial perturbations.
1 code implementation • CVPR 2019 • Yang Yang, Wenye Ma, Yin Zheng, Jian-Feng Cai, Weiyu Xu
Removing undesired reflections from images taken through the glass is of great importance in computer vision.
no code implementations • 27 Jan 2019 • Hui Xie, Jirong Yi, Weiyu Xu, Raghu Mudumbai
We present a simple hypothesis about a compression property of artificial intelligence (AI) classifiers and present theoretical arguments to show that this hypothesis successfully accounts for the observed fragility of AI classifiers to small adversarial perturbations.
no code implementations • 31 Dec 2018 • Weiyu Xu, Lifeng Lai, Amin Khajehnejad
In this paper, we study the inaccuracies of opinion polls in the 2016 election through the lens of information theory.
no code implementations • 26 Oct 2018 • Jirong Yi, Anh Duc Le, Tianming Wang, Xiaodong Wu, Weiyu Xu
In this paper, we propose a generative model neural network approach for reconstructing the ground truth signals under sparse outliers.
no code implementations • 14 Feb 2018 • Jirong Yi, Weiyu Xu
In [12, 14, 15], the authors established the necessary and sufficient null space conditions for nuclear norm minimization to recover every possible low-rank matrix with rank at most r (the strong null space condition).
no code implementations • 4 Nov 2017 • Weiyu Xu, Jirong Yi, Soura Dasgupta, Jian-Feng Cai, Mathews Jacob, Myung Cho
However, it is known that in order for TV minimization and atomic norm minimization to recover the missing data or the frequencies, the underlying $R$ frequencies are required to be well-separated, even when the measurements are noiseless.
no code implementations • 14 Mar 2017 • Myung Cho, Lifeng Lai, Weiyu Xu
Additionally, we show that adapting number of local and global iterations to network communication delays in the distributed dual coordinated ascent algorithm can improve its convergence speed.
no code implementations • 15 Sep 2015 • Bingwen Zhang, Weiyu Xu, Jian-Feng Cai, Lifeng Lai
Characterizing the phase transitions of convex optimizations in recovering structured signals or data is of central importance in compressed sensing, machine learning and statistics.
no code implementations • 14 Jul 2015 • Jian-Feng Cai, Suhui Liu, Weiyu Xu
This paper considers reconstructing a spectrally sparse signal from a small number of randomly observed time-domain samples.
no code implementations • 10 Mar 2015 • Jian-Feng Cai, Xiaobo Qu, Weiyu Xu, Gui-Bo Ye
Our method can be applied to spectral compressed sensing where the signal of interest is a superposition of $R$ complex sinusoids.
no code implementations • 2 Dec 2013 • Weiyu Xu, Jian-Feng Cai, Kumar Vijay Mishra, Myung Cho, Anton Kruger
Recent research in off-the-grid compressed sensing (CS) has demonstrated that, under certain conditions, one can successfully recover a spectrally sparse signal from a few time-domain samples even though the dictionary is continuous.
no code implementations • 17 Jul 2013 • Weiyu Xu, Myung Cho
In this paper, we show that a polynomial-time algorithm can universally elevate the phase-transition performance of compressed sensing, compared with $\ell_1$ minimization, even for signals with constant-modulus nonzero elements.
no code implementations • 11 Jun 2013 • Myung Cho, Weiyu Xu
In this paper, we first propose a series of new polynomial-time algorithms to compute upper bounds on $\alpha_k$.
no code implementations • 28 Jan 2013 • Jian-Feng Cai, Weiyu Xu
In this paper, we consider using total variation minimization to recover signals whose gradients have a sparse support, from a small number of measurements.