Zobrazeno 1 - 10
of 23 080
pro vyhledávání: '"An, Jiming"'
Autor:
Yuan, Quan, Zhang, Zhikun, Du, Linkang, Chen, Min, Sun, Mingyang, Gao, Yunjun, Backes, Michael, He, Shibo, Chen, Jiming
Streaming graphs are ubiquitous in daily life, such as evolving social networks and dynamic communication systems. Due to the sensitive information contained in the graph, directly sharing the streaming graphs poses significant privacy risks. Differe
Externí odkaz:
http://arxiv.org/abs/2412.11369
This paper studies a class of distributed bilevel optimization (DBO) problems with a coupled inner-level subproblem. Existing approaches typically rely on hypergradient estimations involving computationally expensive Hessian information. To address t
Externí odkaz:
http://arxiv.org/abs/2412.11218
Implicit neural representations and 3D Gaussian splatting (3DGS) have shown great potential for scene reconstruction. Recent studies have expanded their applications in autonomous reconstruction through task assignment methods. However, these methods
Externí odkaz:
http://arxiv.org/abs/2412.02249
Autor:
Wang, Tianyi, Wang, Zichen, Wang, Cong, Shu, Yuanchao, Deng, Ruilong, Cheng, Peng, Chen, Jiming
Object detection is a fundamental enabler for many real-time downstream applications such as autonomous driving, augmented reality and supply chain management. However, the algorithmic backbone of neural networks is brittle to imperceptible perturbat
Externí odkaz:
http://arxiv.org/abs/2412.02171
Condensing large datasets into smaller synthetic counterparts has demonstrated its promise for image classification. However, previous research has overlooked a crucial concern in image recognition: ensuring that models trained on condensed datasets
Externí odkaz:
http://arxiv.org/abs/2411.19623
We propose a random-effects approach to missing values for linear mixed model (LMM) analysis. The method converts a LMM with missing covariates to another LMM without missing covariates. The standard LMM analysis tools for longitudinal data then appl
Externí odkaz:
http://arxiv.org/abs/2411.14548
Differential privacy (DP) has recently been introduced into episodic reinforcement learning (RL) to formally address user privacy concerns in personalized services. Previous work mainly focuses on two trust models of DP: the central model, where a ce
Externí odkaz:
http://arxiv.org/abs/2411.11647
This paper studies privacy-preserving resilient vector consensus in multi-agent systems against faulty agents, where normal agents can achieve consensus within the convex hull of their initial states while protecting state vectors from being disclose
Externí odkaz:
http://arxiv.org/abs/2411.03633
Multi-modal fusion is imperative to the implementation of reliable object detection and tracking in complex environments. Exploiting the synergy of heterogeneous modal information endows perception systems the ability to achieve more comprehensive, r
Externí odkaz:
http://arxiv.org/abs/2410.19872
Autor:
Du, Linkang, Zhou, Xuanru, Chen, Min, Zhang, Chusong, Su, Zhou, Cheng, Peng, Chen, Jiming, Zhang, Zhikun
As the implementation of machine learning (ML) systems becomes more widespread, especially with the introduction of larger ML models, we perceive a spring demand for massive data. However, it inevitably causes infringement and misuse problems with th
Externí odkaz:
http://arxiv.org/abs/2410.16618