Zobrazeno 1 - 10
of 14 645
pro vyhledávání: '"Thuan AT"'
Multimodal LLMs have advanced vision-language tasks but still struggle with understanding video scenes. To bridge this gap, Video Scene Graph Generation (VidSGG) has emerged to capture multi-object relationships across video frames. However, prior me
Externí odkaz:
http://arxiv.org/abs/2411.18042
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
Autor:
Phamh, Thuan, Li, Xingpeng
Power system networks are often modeled as homogeneous graphs, which limits the ability of graph neural network (GNN) to capture individual generator features at the same nodes. By introducing the proposed virtual node-splitting strategy, generator-l
Externí odkaz:
http://arxiv.org/abs/2411.06268
Autor:
Pham, Thuan, Li, Xingpeng
Optimal power flow (OPF) has been used for real-time grid operations. Prior efforts demonstrated that utilizing flexibility from dynamic topologies will improve grid efficiency. However, this will convert the linear OPF into a mixed-integer linear pr
Externí odkaz:
http://arxiv.org/abs/2410.17460
This paper presents a novel approach to designing a Hedge Algebra Controller named Hedge Algebra Controller with Recursive Semantic Values (RS-HAC). This approach incorporates several newly introduced concepts, including Semantically Quantifying Simp
Externí odkaz:
http://arxiv.org/abs/2410.15058
Motion planning is an essential process for the navigation of unmanned aerial vehicles (UAVs) where they need to adapt to obstacles and different structures of their operating environment to reach the goal. This paper presents an optimal motion plann
Externí odkaz:
http://arxiv.org/abs/2410.09799
Autor:
Bender, Christian, Thuan, Nguyen Tran
In our recent work [3] we introduced the grid-sampling SDE as a proxy for modeling exploration in continuous-time reinforcement learning. In this note, we provide further motivation for the use of this SDE and discuss its wellposedness in the presenc
Externí odkaz:
http://arxiv.org/abs/2410.07778
Grasping a variety of objects remains a key challenge in the development of versatile robotic systems. The human hand is remarkably dexterous, capable of grasping and manipulating objects with diverse shapes, mechanical properties, and textures. Insp
Externí odkaz:
http://arxiv.org/abs/2410.05789
Autor:
Dong, Zijian, Li, Ruilin, Wu, Yilei, Nguyen, Thuan Tinh, Chong, Joanna Su Xian, Ji, Fang, Tong, Nathanael Ren Jie, Chen, Christopher Li Hsian, Zhou, Juan Helen
We introduce Brain-JEPA, a brain dynamics foundation model with the Joint-Embedding Predictive Architecture (JEPA). This pioneering model achieves state-of-the-art performance in demographic prediction, disease diagnosis/prognosis, and trait predicti
Externí odkaz:
http://arxiv.org/abs/2409.19407
Autor:
Bender, Christian, Thuan, Nguyen Tran
We present a random measure approach for modeling exploration, i.e., the execution of measure-valued controls, in continuous-time reinforcement learning (RL) with controlled diffusion and jumps. First, we consider the case when sampling the randomize
Externí odkaz:
http://arxiv.org/abs/2409.17200