Search Results for author: Kun Yang

Found 52 papers, 8 papers with code

Towards Context-Aware Emotion Recognition Debiasing from a Causal Demystification Perspective via De-confounded Training

no code implementations6 Jul 2024 Dingkang Yang, Kun Yang, Haopeng Kuang, Zhaoyu Chen, Yuzheng Wang, Lihua Zhang

To address the issue, we embrace causal inference to disentangle the models from the impact of such bias, and formulate the causalities among variables in the CAER task via a customized causal graph.

Causal Inference Emotion Recognition +2

Asynchronous Multimodal Video Sequence Fusion via Learning Modality-Exclusive and -Agnostic Representations

no code implementations6 Jul 2024 Dingkang Yang, Mingcheng Li, Linhao Qu, Kun Yang, Peng Zhai, Song Wang, Lihua Zhang

To tackle these issues, we propose a Multimodal fusion approach for learning modality-Exclusive and modality-Agnostic representations (MEA) to refine multimodal features and leverage the complementarity across distinct modalities.

Timely Requesting for Time-Critical Content Users in Decentralized F-RANs

no code implementations3 Jul 2024 Xingran Chen, Kai Li, Kun Yang

We study two general classes of policies: (i) oblivious policies, where decision-making is independent of historical information, and (ii) non-oblivious policies, where decisions are influenced by historical information.

Defect Image Sample Generation With Diffusion Prior for Steel Surface Defect Recognition

no code implementations3 May 2024 Yichun Tai, Kun Yang, Tao Peng, Zhenzhen Huang, Zhijiang Zhang

To this end, we propose Stable Surface Defect Generation (StableSDG), which transfers the vast generation distribution embedded in Stable Diffusion model for steel surface defect image generation.

Image Generation

Correlation-Decoupled Knowledge Distillation for Multimodal Sentiment Analysis with Incomplete Modalities

no code implementations CVPR 2024 Mingcheng Li, Dingkang Yang, Xiao Zhao, Shuaibing Wang, Yan Wang, Kun Yang, Mingyang Sun, Dongliang Kou, Ziyun Qian, Lihua Zhang

Specifically, we present a sample-level contrastive distillation mechanism that transfers comprehensive knowledge containing cross-sample correlations to reconstruct missing semantics.

Disentanglement Knowledge Distillation +1

Robust Emotion Recognition in Context Debiasing

no code implementations CVPR 2024 Dingkang Yang, Kun Yang, Mingcheng Li, Shunli Wang, Shuaibing Wang, Lihua Zhang

Following the causal graph, CLEF introduces a non-invasive context branch to capture the adverse direct effect caused by the context bias.

counterfactual Emotion Recognition in Context

Age of Computing: A Metric of Computation Freshness in Communication and Computation Cooperative Networks

no code implementations8 Mar 2024 Xingran Chen, Yusha Liu, Yali Zheng, Kun Yang

In this letter, we introduce the novel concept, Age of Computing (AoC), to capture computation freshness in 3CNs.

Towards Multimodal Sentiment Analysis Debiasing via Bias Purification

no code implementations8 Mar 2024 Dingkang Yang, Mingcheng Li, Dongling Xiao, Yang Liu, Kun Yang, Zhaoyu Chen, Yuzheng Wang, Peng Zhai, Ke Li, Lihua Zhang

In the inference phase, given a factual multimodal input, MCIS imagines two counterfactual scenarios to purify and mitigate these biases.

counterfactual Counterfactual Inference +1

Efficient Prompt Optimization Through the Lens of Best Arm Identification

no code implementations15 Feb 2024 Chengshuai Shi, Kun Yang, Zihan Chen, Jundong Li, Jing Yang, Cong Shen

TRIPLE is built on a novel connection established between prompt optimization and fixed-budget best arm identification (BAI-FB) in multi-armed bandits (MAB); thus, it is capable of leveraging the rich toolbox from BAI-FB systematically and also incorporating unique characteristics of prompt optimization.

Instruction Following Multi-Armed Bandits

Harnessing the Power of Federated Learning in Federated Contextual Bandits

no code implementations26 Dec 2023 Chengshuai Shi, Ruida Zhou, Kun Yang, Cong Shen

Federated learning (FL) has demonstrated great potential in revolutionizing distributed machine learning, and tremendous efforts have been made to extend it beyond the original focus on supervised learning.

Decision Making Federated Learning +1

Advancing RAN Slicing with Offline Reinforcement Learning

no code implementations16 Dec 2023 Kun Yang, Shu-ping Yeh, Menglei Zhang, Jerry Sydir, Jing Yang, Cong Shen

Dynamic radio resource management (RRM) in wireless networks presents significant challenges, particularly in the context of Radio Access Network (RAN) slicing.

Management Offline RL +2

Large Language Model Enhanced Multi-Agent Systems for 6G Communications

no code implementations13 Dec 2023 Feibo Jiang, Li Dong, Yubo Peng, Kezhi Wang, Kun Yang, Cunhua Pan, Dusit Niyato, Octavia A. Dobre

The rapid development of the Large Language Model (LLM) presents huge opportunities for 6G communications, e. g., network optimization and management by allowing users to input task requirements to LLMs by nature language.

Language Modelling Large Language Model +3

A brief introduction to a framework named Multilevel Guidance-Exploration Network

1 code implementation7 Dec 2023 Guoqing Yang, Zhiming Luo, Jianzhe Gao, Yingxin Lai, Kun Yang, Yifan He, Shaozi Li

Human behavior anomaly detection aims to identify unusual human actions, playing a crucial role in intelligent surveillance and other areas.

Anomaly Detection

Offline Reinforcement Learning for Wireless Network Optimization with Mixture Datasets

no code implementations19 Nov 2023 Kun Yang, Cong Shen, Jing Yang, Shu-ping Yeh, Jerry Sydir

We observe that the performance of offline RL for the RRM problem depends critically on the behavior policy used for data collection, and further propose a novel offline RL solution that leverages heterogeneous datasets collected by different behavior policies.

Management Offline RL +4

Massive Wireless Energy Transfer without Channel State Information via Imperfect Intelligent Reflecting Surfaces

no code implementations15 Nov 2023 Cheng Luo, Jie Hu, Luping Xiang, Kun Yang, Kai-Kit Wong

Intelligent Reflecting Surface (IRS) utilizes low-cost, passive reflecting elements to enhance the passive beam gain, improve Wireless Energy Transfer (WET) efficiency, and enable its deployment for numerous Internet of Things (IoT) devices.

Green Beamforming Design for Integrated Sensing and Communication Systems: A Practical Approach Using Beam-Matching Error Metrics

no code implementations21 Oct 2023 Luping Xiang, Ke Xu, Jie Hu, Kun Yang

In this paper, we propose a green beamforming design for the integrated sensing and communication (ISAC) system, using beam-matching error to assess radar performance.

Robust NOMA-assisted OTFS-ISAC Network Design with 3D Motion Prediction Topology

no code implementations21 Oct 2023 Luping Xiang, Ke Xu, Jie Hu, Christos Masouros, Kun Yang

This paper proposes a novel non-orthogonal multiple access (NOMA)-assisted orthogonal time-frequency space (OTFS)-integrated sensing and communication (ISAC) network, which uses unmanned aerial vehicles (UAVs) as air base stations to support multiple users.

Fairness motion prediction +1

Large AI Model Empowered Multimodal Semantic Communications

no code implementations3 Sep 2023 Feibo Jiang, Yubo Peng, Li Dong, Kezhi Wang, Kun Yang, Cunhua Pan, Xiaohu You

To this end, we propose a Large AI Model-based Multimodal SC (LAM-MSC) framework, in which we first present the MLM-based Multimodal Alignment (MMA) that utilizes the MLM to enable the transformation between multimodal and unimodal data while preserving semantic consistency.

Language Modelling Large Language Model +1

LAMBO: Large Language Model Empowered Edge Intelligence

no code implementations29 Aug 2023 Li Dong, Feibo Jiang, Yubo Peng, Kezhi Wang, Kun Yang, Cunhua Pan, Robert Schober

Next-generation edge intelligence is anticipated to bring huge benefits to various applications, e. g., offloading systems.

Active Learning Decision Making +4

Spatio-Temporal Domain Awareness for Multi-Agent Collaborative Perception

1 code implementation ICCV 2023 Kun Yang, Dingkang Yang, Jingyu Zhang, Mingcheng Li, Yang Liu, Jing Liu, Hanqi Wang, Peng Sun, Liang Song

In this paper, we propose SCOPE, a novel collaborative perception framework that aggregates the spatio-temporal awareness characteristics across on-road agents in an end-to-end manner.

3D Object Detection Autonomous Vehicles +1

Training Latency Minimization for Model-Splitting Allowed Federated Edge Learning

no code implementations21 Jul 2023 Yao Wen, Guopeng Zhang, Kezhi Wang, Kun Yang

To alleviate the shortage of computing power faced by clients in training deep neural networks (DNNs) using federated learning (FL), we leverage the edge computing and split learning to propose a model-splitting allowed FL (SFL) framework, with the aim to minimize the training latency without loss of test accuracy.

Edge-computing Federated Learning

Large AI Model-Based Semantic Communications

no code implementations7 Jul 2023 Feibo Jiang, Yubo Peng, Li Dong, Kezhi Wang, Kun Yang, Cunhua Pan, Xiaohu You

Semantic communication (SC) is an emerging intelligent paradigm, offering solutions for various future applications like metaverse, mixed-reality, and the Internet of everything.

Mixed Reality Semantic Communication +1

Adversarially robust clustering with optimality guarantees

no code implementations16 Jun 2023 Soham Jana, Kun Yang, Sanjeev Kulkarni

In the absence of outliers, in fixed dimensions, our theoretical guarantees are similar to that of the Lloyd algorithm.


Byzantine-Robust Clustered Federated Learning

1 code implementation1 Jun 2023 Zhixu Tao, Kun Yang, Sanjeev R. Kulkarni

This paper focuses on the problem of adversarial attacks from Byzantine machines in a Federated Learning setting where non-Byzantine machines can be partitioned into disjoint clusters.

Clustering Federated Learning

Accelerating Hybrid Federated Learning Convergence under Partial Participation

no code implementations10 Apr 2023 Jieming Bian, Lei Wang, Kun Yang, Cong Shen, Jie Xu

In this paper, we provide theoretical analysis of hybrid FL under clients' partial participation to validate that partial participation is the key constraint on convergence speed.

Federated Learning

DMSA: Dynamic Multi-scale Unsupervised Semantic Segmentation Based on Adaptive Affinity

1 code implementation1 Mar 2023 Kun Yang, Jun Lu

The proposed method in this paper proposes an end-to-end unsupervised semantic segmentation architecture DMSA based on four loss functions.

Unsupervised Semantic Segmentation

A novel efficient Multi-view traffic-related object detection framework

no code implementations23 Feb 2023 Kun Yang, Jing Liu, Dingkang Yang, Hanqi Wang, Peng Sun, Yanni Zhang, Yan Liu, Liang Song

With the rapid development of intelligent transportation system applications, a tremendous amount of multi-view video data has emerged to enhance vehicle perception.

Model Selection object-detection +1

Orthogonal-Time-Frequency-Space Signal Design for Integrated Data and Energy Transfer: Benefits from Doppler Offsets

no code implementations3 Feb 2023 Jie Hu, Ke Xu, Luping Xiang, Kun Yang

Integrated data and energy transfer (IDET) is an advanced technology for enabling energy sustainability for massively deployed low-power electronic consumption components.

SISO-OFDM and MISO-OFDM Counterparts for "Wideband Waveforming for Integrated Data and Energy Transfer: Creating Extra Gain Beyond Multiple Antennas and Multiple Carriers"

no code implementations8 Dec 2022 Zhonglun Wang, Jie Hu, Kun Yang

In this article, we proposethe SISO-OFDM and MISO-OFDM based IDET systems, which are the counterparts of our optimal wideband waveforming strategy in [1].

Content-Noise Complementary Learning for Medical Image Denoising

2 code implementations IEEE Transactions on Medical Imaging 2022 Mufeng Geng, Xiangxi Meng, Jiangyuan Yu, Lei Zhu, Lujia Jin, Zhe Jiang, Bin Qiu, Hui Li, Hanjing Kong, Jianmin Yuan, Kun Yang, Hongming Shan, Hongbin Han, Zhi Yang, Qiushi Ren, Yanye Lu

In this study, we propose a simple yet effective strategy, the content-noise complementary learning (CNCL) strategy, in which two deep learning predictors are used to learn the respective content and noise of the image dataset complementarily.

Generative Adversarial Network Image Denoising +1

QuatDE: Dynamic Quaternion Embedding for Knowledge Graph Completion

1 code implementation19 May 2021 Haipeng Gao, Kun Yang, Yuxue Yang, Rufai Yusuf Zakari, Jim Wilson Owusu, Ke Qin

Knowledge graph embedding has been an active research topic for knowledge base completion (KGC), with progressive improvement from the initial TransE, TransH, RotatE et al to the current state-of-the-art QuatE.

Knowledge Base Completion Knowledge Graph Completion +3

Connecting AI Learning and Blockchain Mining in 6G Systems

no code implementations29 Apr 2021 Yunkai Wei, Zixian An, Supeng Leng, Kun Yang

The sixth generation (6G) systems are generally recognized to be established on ubiquitous Artificial Intelligence (AI) and distributed ledger such as blockchain.

An Efficient One-Class SVM for Anomaly Detection in the Internet of Things

no code implementations22 Apr 2021 Kun Yang, Samory Kpotufe, Nick Feamster

Insecure Internet of things (IoT) devices pose significant threats to critical infrastructure and the Internet at large; detecting anomalous behavior from these devices remains of critical importance, but fast, efficient, accurate anomaly detection (also called "novelty detection") for these classes of devices remains elusive.

Anomaly Detection Novelty Detection

End-to-End Jet Classification of Boosted Top Quarks with the CMS Open Data

no code implementations19 Apr 2021 Michael Andrews, Bjorn Burkle, Yi-fan Chen, Davide DiCroce, Sergei Gleyzer, Ulrich Heintz, Meenakshi Narain, Manfred Paulini, Nikolas Pervan, Yusef Shafi, Wei Sun, Emanuele Usai, Kun Yang

We describe a novel application of the end-to-end deep learning technique to the task of discriminating top quark-initiated jets from those originating from the hadronization of a light quark or a gluon.

Exactly solvable model of Fermi arcs and pseudogap

no code implementations3 Nov 2020 Kun Yang

We introduce a very simple and exactly solvable model that supports Fermi arcs in its ground state and excitation spectrum.

Strongly Correlated Electrons Superconductivity

6G Cellular Networks and Connected Autonomous Vehicles

no code implementations2 Oct 2020 Jianhua He, Kun Yang, Hsiao-Hwa Chen

With 5G mobile communication systems been commercially rolled out, research discussions on next generation mobile systems, i. e., 6G, have started.

Autonomous Vehicles

Feature Extraction for Novelty Detection in Network Traffic

no code implementations30 Jun 2020 Kun Yang, Samory Kpotufe, Nick Feamster

To facilitate such exploration, we develop a systematic framework, open-source toolkit, and public Python library that makes it both possible and easy to extract and generate features from network traffic and perform and end-to-end evaluation of these representations across most prevalent modern novelty detection models.

Anomaly Detection BIG-bench Machine Learning +2

Distributed Resource Scheduling for Large-Scale MEC Systems: A Multi-Agent Ensemble Deep Reinforcement Learning with Imitation Acceleration

no code implementations21 May 2020 Feibo Jiang, Li Dong, Kezhi Wang, Kun Yang, Cunhua Pan

We consider the optimization of distributed resource scheduling to minimize the sum of task latency and energy consumption for all the Internet of things devices (IoTDs) in a large-scale mobile edge computing (MEC) system.

Decision Making Edge-computing +1

AI Driven Heterogeneous MEC System with UAV Assistance for Dynamic Environment -- Challenges and Solutions

no code implementations11 Feb 2020 Feibo Jiang, Kezhi Wang, Li Dong, Cunhua Pan, Wei Xu, Kun Yang

By taking full advantage of Computing, Communication and Caching (3C) resources at the network edge, Mobile Edge Computing (MEC) is envisioned as one of the key enablers for the next generation networks.

Decision Making Edge-computing +3

Stacked Auto Encoder Based Deep Reinforcement Learning for Online Resource Scheduling in Large-Scale MEC Networks

no code implementations24 Jan 2020 Feibo Jiang, Kezhi Wang, Li Dong, Cunhua Pan, Kun Yang

An online resource scheduling framework is proposed for minimizing the sum of weighted task latency for all the Internet of things (IoT) users, by optimizing offloading decision, transmission power and resource allocation in the large-scale mobile edge computing (MEC) system.

Data Compression Edge-computing +1

RL-Based User Association and Resource Allocation for Multi-UAV enabled MEC

no code implementations8 Apr 2019 Liang Wang, Peiqiu Huang, Kezhi Wang, Guopeng Zhang, Lei Zhang, Nauman Aslam, Kun Yang

In this paper, multi-unmanned aerial vehicle (UAV) enabled mobile edge computing (MEC), i. e., UAVE is studied, where several UAVs are deployed as flying MEC platform to provide computing resource to ground user equipments (UEs).

Edge-computing Reinforcement Learning (RL)

Machine Learning in High Energy Physics Community White Paper

no code implementations8 Jul 2018 Kim Albertsson, Piero Altoe, Dustin Anderson, John Anderson, Michael Andrews, Juan Pedro Araque Espinosa, Adam Aurisano, Laurent Basara, Adrian Bevan, Wahid Bhimji, Daniele Bonacorsi, Bjorn Burkle, Paolo Calafiura, Mario Campanelli, Louis Capps, Federico Carminati, Stefano Carrazza, Yi-fan Chen, Taylor Childers, Yann Coadou, Elias Coniavitis, Kyle Cranmer, Claire David, Douglas Davis, Andrea De Simone, Javier Duarte, Martin Erdmann, Jonas Eschle, Amir Farbin, Matthew Feickert, Nuno Filipe Castro, Conor Fitzpatrick, Michele Floris, Alessandra Forti, Jordi Garra-Tico, Jochen Gemmler, Maria Girone, Paul Glaysher, Sergei Gleyzer, Vladimir Gligorov, Tobias Golling, Jonas Graw, Lindsey Gray, Dick Greenwood, Thomas Hacker, John Harvey, Benedikt Hegner, Lukas Heinrich, Ulrich Heintz, Ben Hooberman, Johannes Junggeburth, Michael Kagan, Meghan Kane, Konstantin Kanishchev, Przemysław Karpiński, Zahari Kassabov, Gaurav Kaul, Dorian Kcira, Thomas Keck, Alexei Klimentov, Jim Kowalkowski, Luke Kreczko, Alexander Kurepin, Rob Kutschke, Valentin Kuznetsov, Nicolas Köhler, Igor Lakomov, Kevin Lannon, Mario Lassnig, Antonio Limosani, Gilles Louppe, Aashrita Mangu, Pere Mato, Narain Meenakshi, Helge Meinhard, Dario Menasce, Lorenzo Moneta, Seth Moortgat, Mark Neubauer, Harvey Newman, Sydney Otten, Hans Pabst, Michela Paganini, Manfred Paulini, Gabriel Perdue, Uzziel Perez, Attilio Picazio, Jim Pivarski, Harrison Prosper, Fernanda Psihas, Alexander Radovic, Ryan Reece, Aurelius Rinkevicius, Eduardo Rodrigues, Jamal Rorie, David Rousseau, Aaron Sauers, Steven Schramm, Ariel Schwartzman, Horst Severini, Paul Seyfert, Filip Siroky, Konstantin Skazytkin, Mike Sokoloff, Graeme Stewart, Bob Stienen, Ian Stockdale, Giles Strong, Wei Sun, Savannah Thais, Karen Tomko, Eli Upfal, Emanuele Usai, Andrey Ustyuzhanin, Martin Vala, Justin Vasel, Sofia Vallecorsa, Mauro Verzetti, Xavier Vilasís-Cardona, Jean-Roch Vlimant, Ilija Vukotic, Sean-Jiun Wang, Gordon Watts, Michael Williams, Wenjing Wu, Stefan Wunsch, Kun Yang, Omar Zapata

In this document we discuss promising future research and development areas for machine learning in particle physics.

BIG-bench Machine Learning Vocal Bursts Intensity Prediction

Point Set Registration With Global-Local Correspondence and Transformation Estimation

no code implementations ICCV 2017 Su Zhang, Yang Yang, Kun Yang, Yi Luo, Sim-Heng Ong

We present a new point set registration method with global-local correspondence and transformation estimation (GL-CATE).

Density Estimation via Discrepancy

no code implementations23 Sep 2015 Kun Yang, Hao Su, Wing Hung Wang

Given i. i. d samples from some unknown continuous density on hyper-rectangle $[0, 1]^d$, we attempt to learn a piecewise constant function that approximates this underlying density non-parametrically.

Density Estimation

Density Estimation via Discrepancy Based Adaptive Sequential Partition

no code implementations NeurIPS 2016 Dangna Li, Kun Yang, Wing Hung Wong

Given $iid$ observations from an unknown absolute continuous distribution defined on some domain $\Omega$, we propose a nonparametric method to learn a piecewise constant function to approximate the underlying probability density function.

Density Estimation

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