196 citations · 408 across the 16 of their papers we have counts for
17 papers
Topological degree for singular -mean field equations
Mohameden Ahmedou, Zhongwei Tang, Heming Wang
We determine the Leray--Schauder degree of the singular Dirichlet -mean field equation on a smooth bounded domain in , , for every positive nonresonant par…
Distributed Activity Detection for Cell-Free Hybrid Near-Far Field Communications
Jingreng Lei, Yang Li, Zeyi Ren +4
A great amount of endeavor has recently been devoted to activity detection for massive machine-type communications in cell-free massive MIMO. However, in practice, as the number of…
Covariance-Based Device Activity Detection with Massive MIMO for Near-Field Correlated Channels
Ziyue Wang, Yang Li, Ya-Feng Liu +1
This paper studies the device activity detection problem in a massive multiple-input multiple-output (MIMO) system for near-field communications (NFC). In this system, active devic…
Covariance-Based Activity Detection in Cooperative Multi-Cell Massive MIMO: Scaling Law and Efficient Algorithms
Ziyue Wang, Ya-Feng Liu, Zhaorui Wang +1
This paper focuses on the covariance-based activity detection problem in a multi-cell massive multiple-input multiple-output (MIMO) system. In this system, active devices transmit…
Universal and Transferable Adversarial Attacks on Aligned Language Models
Andy Zou, Zifan Wang, Nicholas Carlini +3
Because "out-of-the-box" large language models are capable of generating a great deal of objectionable content, recent work has focused on aligning these models in an attempt to pr…
Device Activity Detection in mMTC with Low-Resolution ADC: A New Protocol
Zhaorui Wang, Ya-Feng Liu, Ziyue Wang +3
This paper investigates the effect of low-resolution analog-to-digital converters (ADCs) on device activity detection in massive machine-type communications (mMTC). The low-resolut…
Unlocking Deterministic Robustness Certification on ImageNet
Kai Hu, Andy Zou, Zifan Wang +2
Despite the promise of Lipschitz-based methods for provably-robust deep learning with deterministic guarantees, current state-of-the-art results are limited to feed-forward Convolu…
Imperceptible Adversarial Attack via Invertible Neural Networks
Zihan Chen, Ziyue Wang, Junjie Huang +3
Adding perturbations via utilizing auxiliary gradient information or discarding existing details of the benign images are two common approaches for generating adversarial examples.…
Scaling Law Analysis for Covariance Based Activity Detection in Cooperative Multi-Cell Massive MIMO
Ziyue Wang, Ya-Feng Liu, Zhaorui Wang +1
This paper studies the covariance based activity detection problem in a multi-cell massive multiple-input multiple-output (MIMO) system, where the active devices transmit their sig…
On the Perils of Cascading Robust Classifiers
Ravi Mangal, Zifan Wang, Chi Zhang +3
Ensembling certifiably robust neural networks is a promising approach for improving the \emph{certified robust accuracy} of neural models. Black-box ensembles that assume only quer…
Faithful Explanations for Deep Graph Models
Zifan Wang, Yuhang Yao, Chaoran Zhang +5
This paper studies faithful explanations for Graph Neural Networks (GNNs). First, we provide a new and general method for formally characterizing the faithfulness of explanations f…
On Optimizing Shared-ride Mobility Services with Walking Legs
Zifan Wang, Michael F Hyland, Younghun Bahk +1
Shared-ride mobility services that incorporate traveler walking legs aim to reduce vehicle-kilometers-travelled (VKT), vehicle-hours-travelled (VHT), request rejections, fleet size…
Globally-Robust Neural Networks
Klas Leino, Zifan Wang, Matt Fredrikson
The threat of adversarial examples has motivated work on training certifiably robust neural networks to facilitate efficient verification of local robustness at inference time. We…
Smoothed Geometry for Robust Attribution
Zifan Wang, Haofan Wang, Shakul Ramkumar +3
Feature attributions are a popular tool for explaining the behavior of Deep Neural Networks (DNNs), but have recently been shown to be vulnerable to attacks that produce divergent…
Towards Frequency-Based Explanation for Robust CNN
Zifan Wang, Yilin Yang, Ankit Shrivastava +2
Current explanation techniques towards a transparent Convolutional Neural Network (CNN) mainly focuses on building connections between the human-understandable input features with…
Interpreting Interpretations: Organizing Attribution Methods by Criteria
Zifan Wang, Piotr Mardziel, Anupam Datta +1
Motivated by distinct, though related, criteria, a growing number of attribution methods have been developed tointerprete deep learning. While each relies on the interpretability o…
Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks
Haofan Wang, Zifan Wang, Mengnan Du +5
Recently, increasing attention has been drawn to the internal mechanisms of convolutional neural networks, and the reason why the network makes specific decisions. In this paper, w…