20 citations · 57 across the 17 of their papers we have counts for
18 papers
HyperCut: Fast Inter-Layer Scheduling via Directed Hypergraph and Early Filtering
Ziang Wei, Zirui Xu, Sufeng Guo +4
As deep neural networks (DNNs) continue to scale, inter-layer scheduling, which orchestrates the spatial allocation of compute resources and the temporal execution order across lay…
Unlocking Zero-Shot Geospatial Reasoning via Indirect Rewards
Chenhui Xu, Fuxun Yu, Michael J. Bianco +15
Training robust reasoning vision-language models (VLMs) in rare domains (such as geospatial) is fundamentally constrained by supervision scarcity. While raw geospatial imagery is a…
Geospatial Foundational Embedder: Top-1 Winning Solution on EarthVision Embed2Scale Challenge (CVPR 2025)
Zirui Xu, Raphael Tang, Mike Bianco +4
EarthVision Embed2Scale challenge (CVPR 2025) aims to develop foundational geospatial models to embed SSL4EO-S12 hyperspectral geospatial data cubes into embedding vectors that fac…
Infinite-Dimensional Feature Interaction
Chenhui Xu, Fuxun Yu, Maoliang Li +4
The past neural network design has largely focused on feature representation space dimension and its capacity scaling (e.g., width, depth), but overlooked the feature interaction s…
Out-of-Distribution Detection via Deep Multi-Comprehension Ensemble
Chenhui Xu, Fuxun Yu, Zirui Xu +2
Recent research underscores the pivotal role of the Out-of-Distribution (OOD) feature representation field scale in determining the efficacy of models in OOD detection. Consequentl…
QuadraNet: Improving High-Order Neural Interaction Efficiency with Hardware-Aware Quadratic Neural Networks
Chenhui Xu, Fuxun Yu, Zirui Xu +3
Recent progress in computer vision-oriented neural network designs is mostly driven by capturing high-order neural interactions among inputs and features. And there emerged a varie…
Leveraging Untrustworthy Commands for Multi-Robot Coordination in Unpredictable Environments: A Bandit Submodular Maximization Approach
Zirui Xu, Xiaofeng Lin, Vasileios Tzoumas
We study the problem of multi-agent coordination in unpredictable and partially-observable environments with untrustworthy external commands. The commands are actions suggested to…
Online Submodular Coordination with Bounded Tracking Regret: Theory, Algorithm, and Applications to Multi-Robot Coordination
Zirui Xu, Hongyu Zhou, Vasileios Tzoumas
We enable efficient and effective coordination in unpredictable environments, i.e., in environments whose future evolution is unknown a priori and even adversarial. We are motivate…
Resource-Aware Distributed Submodular Maximization: A Paradigm for Multi-Robot Decision-Making
Zirui Xu, Vasileios Tzoumas
Multi-robot decision-making is the process where multiple robots coordinate actions. In this paper, we aim for efficient and effective multi-robot decision-making despite the robot…
QuadraLib: A Performant Quadratic Neural Network Library for Architecture Optimization and Design Exploration
Zirui Xu, Fuxun Yu, Jinjun Xiong +1
The significant success of Deep Neural Networks (DNNs) is highly promoted by the multiple sophisticated DNN libraries. On the contrary, although some work have proved that Quadrati…
Towards Latency-aware DNN Optimization with GPU Runtime Analysis and Tail Effect Elimination
Fuxun Yu, Zirui Xu, Tong Shen +12
Despite the superb performance of State-Of-The-Art (SOTA) DNNs, the increasing computational cost makes them very challenging to meet real-time latency and accuracy requirements. A…
Efficient Neural Network Implementation with Quadratic Neuron
Zirui Xu, Jinjun Xiong, Fuxun Yu +1
Previous works proved that the combination of the linear neuron network with nonlinear activation functions (e.g. ReLu) can achieve nonlinear function approximation. However, simpl…
Heterogeneous Federated Learning
Fuxun Yu, Weishan Zhang, Zhuwei Qin +5
Federated learning learns from scattered data by fusing collaborative models from local nodes. However, due to chaotic information distribution, the model fusion may suffer from st…
Helios: Heterogeneity-Aware Federated Learning with Dynamically Balanced Collaboration
Zirui Xu, Fuxun Yu, Jinjun Xiong +1
In this paper, we propose Helios, a heterogeneity-aware FL framework to tackle the straggler issue. Helios identifies individual devices' heterogeneous training capability, and the…
LanCe: A Comprehensive and Lightweight CNN Defense Methodology against Physical Adversarial Attacks on Embedded Multimedia Applications
Zirui Xu, Fuxun Yu, Xiang Chen
Recently, adversarial attacks can be applied to the physical world, causing practical issues to various Convolutional Neural Networks (CNNs) powered applications. Most existing phy…
DoPa: A Comprehensive CNN Detection Methodology against Physical Adversarial Attacks
Zirui Xu, Fuxun Yu, Xiang Chen
Recently, Convolutional Neural Networks (CNNs) demonstrate a considerable vulnerability to adversarial attacks, which can be easily misled by adversarial perturbations. With more a…
HASP: A High-Performance Adaptive Mobile Security Enhancement Against Malicious Speech Recognition
Zirui Xu, Fuxun Yu, Chenchen Liu +1
Nowadays, machine learning based Automatic Speech Recognition (ASR) technique has widely spread in smartphones, home devices, and public facilities. As convenient as this technolog…
Towards Robust Training of Neural Networks by Regularizing Adversarial Gradients
Fuxun Yu, Zirui Xu, Yanzhi Wang +2
In recent years, neural networks have demonstrated outstanding effectiveness in a large amount of applications.However, recent works have shown that neural networks are susceptible…