Zobrazeno 1 - 10
of 13 015
pro vyhledávání: '"P, Pappas"'
Autor:
Mishra, Anup, Pappas, Nikolaos, Stefanović, Čedomir, Ayan, Onur, An, Xueli, Wu, Yiqun, Popovski, Petar, Leyva-Mayorga, Israel
Achieving a flexible and efficient sharing of wireless resources among a wide range of novel applications and services is one of the major goals of the sixth-generation of mobile systems (6G). Accordingly, this work investigates the performance of a
Externí odkaz:
http://arxiv.org/abs/2411.13192
We consider a real-time state reconstruction system for industrial metaverse. The time-varying physical process states in real space are captured by multiple sensors via wireless links, and then reconstructed in virtual space. In this paper, we use t
Externí odkaz:
http://arxiv.org/abs/2411.08413
In this paper, we derive a new Kalman filter with probabilistic data association between measurements and states. We formulate a variational inference problem to approximate the posterior density of the state conditioned on the measurement data. We v
Externí odkaz:
http://arxiv.org/abs/2411.06378
Conformal prediction (CP) is a distribution-free framework for achieving probabilistic guarantees on black-box models. CP is generally applied to a model post-training. Recent research efforts, on the other hand, have focused on optimizing CP efficie
Externí odkaz:
http://arxiv.org/abs/2411.01696
Autor:
Shahriar, Sadat, Qi, Zheng, Pappas, Nikolaos, Doss, Srikanth, Sunkara, Monica, Halder, Kishaloy, Mager, Manuel, Benajiba, Yassine
Aligning Large Language Models (LLM) to address subjectivity and nuanced preference levels requires adequate flexibility and control, which can be a resource-intensive and time-consuming procedure. Existing training-time alignment methods require ful
Externí odkaz:
http://arxiv.org/abs/2410.19206
The recent introduction of large language models (LLMs) has revolutionized the field of robotics by enabling contextual reasoning and intuitive human-robot interaction in domains as varied as manipulation, locomotion, and self-driving vehicles. When
Externí odkaz:
http://arxiv.org/abs/2410.13691
We study the real-time remote tracking of a two-state Markov process by an energy harvesting source. The source decides whether to transmit over an unreliable channel based on the state. We formulate this scenario as a Markov decision process (MDP) t
Externí odkaz:
http://arxiv.org/abs/2410.11521
A driving force behind the diverse applicability of modern machine learning is the ability to extract meaningful features across many sources. However, many practical domains involve data that are non-identically distributed across sources, and stati
Externí odkaz:
http://arxiv.org/abs/2410.11227
Flying quadrotors in tight formations is a challenging problem. It is known that in the near-field airflow of a quadrotor, the aerodynamic effects induced by the propellers are complex and difficult to characterize. Although machine learning tools ca
Externí odkaz:
http://arxiv.org/abs/2410.09727
Autor:
Liu, Qin, Shang, Chao, Liu, Ling, Pappas, Nikolaos, Ma, Jie, John, Neha Anna, Doss, Srikanth, Marquez, Lluis, Ballesteros, Miguel, Benajiba, Yassine
The safety alignment ability of Vision-Language Models (VLMs) is prone to be degraded by the integration of the vision module compared to its LLM backbone. We investigate this phenomenon, dubbed as ''safety alignment degradation'' in this paper, and
Externí odkaz:
http://arxiv.org/abs/2410.09047