226 citations · 1.6k across the 138 of their papers we have counts for
19 papers · 1 filter
Prediction of High-Performance Computing Input/Output Variability and Its Application to Optimization for System Configurations
Li Xu, Thomas Lux, Tyler Chang +6
Performance variability is an important measure for a reliable high performance computing (HPC) system. Performance variability is affected by complicated interactions between nume…
Longitudinal diffusion MRI analysis using Segis-Net: a single-step deep-learning framework for simultaneous segmentation and registration
Bo Li, Wiro J. Niessen, Stefan Klein +4
This work presents a single-step deep-learning framework for longitudinal image analysis, coined Segis-Net. To optimally exploit information available in longitudinal data, this me…
Learning unbiased group-wise registration (LUGR) and joint segmentation: evaluation on longitudinal diffusion MRI
Bo Li, Wiro J. Niessen, Stefan Klein +3
Analysis of longitudinal changes in imaging studies often involves both segmentation of structures of interest and registration of multiple timeframes. The accuracy of such analysi…
Impact of presymptomatic transmission on epidemic spreading in contact networks: A dynamic message-passing analysis
Bo Li, David Saad
Infectious diseases that incorporate pre-symptomatic transmission are challenging to monitor, model, predict and contain. We address this scenario by studying a variant of a stocha…
Relative Pose Estimation of Calibrated Cameras with Known Invariants
Bo Li, Evgeniy Martyushev, Gim Hee Lee
The invariants of a pose include its rotation angle and screw translation. In this paper, we present a complete comprehensive study of the relative pose estimation…
Rethinking Distributional Matching Based Domain Adaptation
Bo Li, Yezhen Wang, Tong Che +6
Domain adaptation (DA) is a technique that transfers predictive models trained on a labeled source domain to an unlabeled target domain, with the core difficulty of resolving distr…
QEBA: Query-Efficient Boundary-Based Blackbox Attack
Huichen Li, Xiaojun Xu, Xiaolu Zhang +2
Machine learning (ML), especially deep neural networks (DNNs) have been widely used in various applications, including several safety-critical ones (e.g. autonomous driving). As a…
GAPS: Generator for Automatic Polynomial Solvers
Bo Li, Viktor Larsson
Minimal problems in computer vision raise the demand of generating efficient automatic solvers for polynomial equation systems. Given a polynomial system repeated with different co…
Multi-Task Learning Enhanced Single Image De-Raining
Yulong Fan, Rong Chen, Bo Li
Rain removal in images is an important task in computer vision filed and attracting attentions of more and more people. In this paper, we address a non-trivial issue of removing vi…
Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations
Huan Zhang, Hongge Chen, Chaowei Xiao +4
A deep reinforcement learning (DRL) agent observes its states through observations, which may contain natural measurement errors or adversarial noises. Since the observations devia…
RAB: Provable Robustness Against Backdoor Attacks
Maurice Weber, Xiaojun Xu, Bojan Karlaš +2
Recent studies have shown that deep neural networks (DNNs) are vulnerable to adversarial attacks, including evasion and backdoor (poisoning) attacks. On the defense side, there hav…
Communication-Efficient Distributed Deep Learning: A Comprehensive Survey
Zhenheng Tang, Shaohuai Shi, Wei Wang +2
Distributed deep learning (DL) has become prevalent in recent years to reduce training time by leveraging multiple computing devices (e.g., GPUs/TPUs) due to larger models and data…
MADAN: Multi-source Adversarial Domain Aggregation Network for Domain Adaptation
Sicheng Zhao, Bo Li, Xiangyu Yue +2
Domain adaptation aims to learn a transferable model to bridge the domain shift between one labeled source domain and another sparsely labeled or unlabeled target domain. Since the…
Micro/Nano Motor Navigation and Localization via Deep Reinforcement Learning
Yuguang Yang, Michael A. Bevan, Bo Li
Efficient navigation and precise localization of Brownian micro/nano self-propelled motor particles within complex landscapes could enable future high-tech applications involving f…
TSS: Transformation-Specific Smoothing for Robustness Certification
Linyi Li, Maurice Weber, Xiaojun Xu +5
As machine learning (ML) systems become pervasive, safeguarding their security is critical. However, recently it has been demonstrated that motivated adversaries are able to mislea…
Distributed Algorithms that Solve Boolean Equations with Local and Differential Privacies
Hongsheng Qi, Bo Li, Rui-Juan Jing +3
In this paper, we propose distributed algorithms that solve a system of Boolean equations over a network, where each node in the network possesses only one Boolean equation from th…
AI-GAN: Attack-Inspired Generation of Adversarial Examples
Tao Bai, Jun Zhao, Jinlin Zhu +4
Deep neural networks (DNNs) are vulnerable to adversarial examples, which are crafted by adding imperceptible perturbations to inputs. Recently different attacks and strategies hav…
Stable Prediction with Model Misspecification and Agnostic Distribution Shift
Kun Kuang, Ruoxuan Xiong, Peng Cui +2
For many machine learning algorithms, two main assumptions are required to guarantee performance. One is that the test data are drawn from the same distribution as the training dat…
Sparse Black-box Video Attack with Reinforcement Learning
Xingxing Wei, Huanqian Yan, Bo Li
Adversarial attacks on video recognition models have been explored recently. However, most existing works treat each video frame equally and ignore their temporal interactions. To…