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arXiv:2409.09590v2 Announce Type: replace Abstract: This article investigates the influential role of flexible antenna array curvature on the performance of 6G communication systems with carrier...
arXiv:2409.09899v2 Announce Type: replace Abstract: This article presents a complete semantic scene understanding workflow using only a single 2D lidar. This fills the gap in...
arXiv:2409.12680v3 Announce Type: replace Abstract: In fine-grained road scene understanding, semantic segmentation plays a crucial role in enabling vehicles to perceive and comprehend their surroundings....
arXiv:2409.14506v4 Announce Type: replace Abstract: We introduce an interactive LLM-based framework designed to enhance the autonomy and robustness of domestic robots, targeting embodied intelligence. Our...
arXiv:2410.00664v3 Announce Type: replace Abstract: Segre-Veronese manifolds are smooth submanifolds of tensors comprising the partially symmetric rank-1 tensors. We investigate a one-parameter family of warped...
arXiv:2410.02296v3 Announce Type: replace Abstract: Language Models (LMs) are increasingly challenging the dominance of domain-specific models, such as Graph Neural Networks (GNNs) and Graph Transformers...
arXiv:2410.04663v4 Announce Type: replace Abstract: The evaluation of Large Language Models (LLMs) remains challenging due to inconsistency, bias, and the absence of transparent decision criteria...
arXiv:2410.05270v4 Announce Type: replace Abstract: We introduce ProLIP, a simple and architecture-agnostic method for adapting contrastively pretrained vision-language models, such as CLIP, to few-shot classification....
arXiv:2410.07582v3 Announce Type: replace Abstract: Membership inference attacks (MIAs) aim to determine whether a specific example was used to train a given language model. While...
arXiv:2410.08314v4 Announce Type: replace Abstract: In this paper we study the Spanning Tree Congestion problem, where we are given a graph $G=(V,E)$ and are asked...
arXiv:2410.12019v2 Announce Type: replace Abstract: Modern autonomous systems with machine learning components often use uncertainty quantification to help produce assurances about system operation. However, there...
arXiv:2410.14485v5 Announce Type: replace Abstract: Artificial Neural Networks (ANNs), including fully-connected networks and transformers, are highly flexible and powerful function approximators, widely applied in fields...
arXiv:2410.14486v2 Announce Type: replace Abstract: A numerical algorithm to decompose an exact low-rank skew-symmetric tensor into a sum of elementary (rank-$1$) skew-symmetric tensors is introduced....
arXiv:2410.24201v2 Announce Type: replace Abstract: We present LingGen, a controlled text generation model that allows fine-grained control over a large number of real-valued linguistic attributes....
arXiv:2411.02057v2 Announce Type: replace Abstract: In recent years, aerial object detection has been increasingly pivotal in various earth observation applications. However, current algorithms are limited...
arXiv:2411.02334v3 Announce Type: replace Abstract: Generative diffusion models (GDMs) have recently shown great success in synthesizing multimedia signals with high perceptual quality, enabling highly efficient...
arXiv:2411.05538v2 Announce Type: replace Abstract: We consider a class of stochastic gradient optimization schemes. Assuming that the objective function is strongly convex, we prove weak...
arXiv:2411.06112v3 Announce Type: replace Abstract: Recommendation model interpretation aims to reveal the relationships between inputs, model internal representations and outputs to enhance the transparency, interpretability,...
arXiv:2411.08835v2 Announce Type: replace Abstract: As one of the most promising technologies in industry, the Digital Twin (DT) facilitates real-time monitoring and predictive analysis for...
arXiv:2411.13687v3 Announce Type: replace Abstract: Assigning a set of labels to a given text is a classification problem with many real-world applications, such as recommender...
arXiv:2411.16170v2 Announce Type: replace Abstract: Recently, large efforts have been made to design efficient linear-complexity visual Transformers. However, current linear attention models are generally unsuitable...
arXiv:2411.17782v2 Announce Type: replace Abstract: The evolving landscape of edge computing envisions platforms operating as dynamic intermediaries between application providers and edge servers (ESs), where...
arXiv:2411.18028v3 Announce Type: replace Abstract: A central approach to algorithmic derandomization is to construct probability distributions with small support that "fool" randomized algorithms, often enabling...
arXiv:2411.18624v3 Announce Type: replace Abstract: Given a single in-the-wild human photo, it remains a challenging task to reconstruct a high-fidelity 3D human model. Existing methods...