Zobrazeno 1 - 10
of 74 136
pro vyhledávání: '"At, Vo"'
This article presents the Labeled Random Finite Set (LRFS) framework for multi-object systems-systems in which the number of objects and their states are unknown and vary randomly with time. In particular, we focus on state and trajectory estimation
Externí odkaz:
http://arxiv.org/abs/2409.18531
Selective state space models (SSM), such as Mamba, have gained prominence for their effectiveness in modeling sequential data. Despite their outstanding empirical performance, a comprehensive theoretical understanding of deep selective SSM remains el
Externí odkaz:
http://arxiv.org/abs/2410.03292
Autor:
Vo, James
Second-order optimization methods offer notable advantages in training deep neural networks by utilizing curvature information to achieve faster convergence. However, traditional second-order techniques are computationally prohibitive, primarily due
Externí odkaz:
http://arxiv.org/abs/2410.02293
Recent research has focused on the risks associated with poor sitting posture and the impact of sitting on biological parameters, such as heart rate because prolonged sitting is common across all ages and professions. In this work, we propose a novel
Externí odkaz:
http://arxiv.org/abs/2410.01459
Automatic polyp segmentation is crucial for effective diagnosis and treatment in colonoscopy images. Traditional methods encounter significant challenges in accurately delineating polyps due to limitations in feature representation and the handling o
Externí odkaz:
http://arxiv.org/abs/2410.01210
Autor:
Luong, Vinh, Dinh, Sang, Raghavan, Shruti, Nguyen, William, Nguyen, Zooey, Le, Quynh, Vo, Hung, Maegaito, Kentaro, Nguyen, Loc, Nguyen, Thao, Ha, Anh Hai, Nguyen, Christopher
Large Language Models (LLMs) have shown remarkable capabilities, but their inherent probabilistic nature often leads to inconsistency and inaccuracy in complex problem-solving tasks. This paper introduces DANA (Domain-Aware Neurosymbolic Agent), an a
Externí odkaz:
http://arxiv.org/abs/2410.02823
Amodal Instance Segmentation (AIS) presents an intriguing challenge, including the segmentation prediction of both visible and occluded parts of objects within images. Previous methods have often relied on shape prior information gleaned from trainin
Externí odkaz:
http://arxiv.org/abs/2409.18256
We prove an injectivity theorem for the cohomology of the Du Bois complexes of varieties with isolated singularities. We use this to deduce vanishing statements for the cohomologies of higher Du Bois complexes of such varieties. Besides some extensio
Externí odkaz:
http://arxiv.org/abs/2409.18019
Vision language models have played a key role in extracting meaningful features for various robotic applications. Among these, Contrastive Language-Image Pretraining (CLIP) is widely used in robotic tasks that require both vision and natural language
Externí odkaz:
http://arxiv.org/abs/2409.17727
Retrieval Augmented Generation (RAG) is a common method for integrating external knowledge into pretrained Large Language Models (LLMs) to enhance accuracy and relevancy in question answering (QA) tasks. However, prompt engineering and resource effic
Externí odkaz:
http://arxiv.org/abs/2409.17648