Zobrazeno 1 - 10
of 13 857
pro vyhledávání: '"P Hau"'
Sleep staging is a challenging task, typically manually performed by sleep technologists based on electroencephalogram and other biosignals of patients taken during overnight sleep studies. Recent work aims to leverage automated algorithms to perform
Externí odkaz:
http://arxiv.org/abs/2411.07964
Publikováno v:
Carnegie Mellon Sports Analytics Conference 2024
Using data from professional bouldering competitions from 2008 to 2022, we train a logistic regression to predict climber results and measure climber skill. However, this approach is limited, as a single numeric coefficient per climber cannot adequat
Externí odkaz:
http://arxiv.org/abs/2411.02343
Autor:
Bu, Dake, Huang, Wei, Han, Andi, Nitanda, Atsushi, Suzuki, Taiji, Zhang, Qingfu, Wong, Hau-San
Transformer-based large language models (LLMs) have displayed remarkable creative prowess and emergence capabilities. Existing empirical studies have revealed a strong connection between these LLMs' impressive emergence abilities and their in-context
Externí odkaz:
http://arxiv.org/abs/2411.02199
We extend the shifted boundary method (SBM) to the simulation of incompressible fluid flow using immersed octree meshes. Previous work on SBM for fluid flow primarily utilized two- or three-dimensional unstructured tetrahedral grids. Recently, octree
Externí odkaz:
http://arxiv.org/abs/2411.00272
In Markov decision processes (MDPs), quantile risk measures such as Value-at-Risk are a standard metric for modeling RL agents' preferences for certain outcomes. This paper proposes a new Q-learning algorithm for quantile optimization in MDPs with st
Externí odkaz:
http://arxiv.org/abs/2410.24128
In this paper, we consider the application of exponential integrators to problems that are advection dominated, either on the entire or on a subset of the domain. In this context, we compare Leja and Krylov based methods to compute the action of expo
Externí odkaz:
http://arxiv.org/abs/2410.12765
Autor:
Aung, Aye Phyu Phyu, Wang, Xinrun, Wang, Ruiyu, Chan, Hau, An, Bo, Li, Xiaoli, Senthilnath, J.
In this paper, we propose a new approach to train deep learning models using game theory concepts including Generative Adversarial Networks (GANs) and Adversarial Training (AT) where we deploy a double-oracle framework using best response oracles. GA
Externí odkaz:
http://arxiv.org/abs/2410.04764
The anticipation of human behavior is a crucial capability for robots to interact with humans safely and efficiently. We employ a smart edge sensor network to provide global observations along with future predictions and goal information to integrate
Externí odkaz:
http://arxiv.org/abs/2410.05015
Optimization in the Bures-Wasserstein space has been gaining popularity in the machine learning community since it draws connections between variational inference and Wasserstein gradient flows. The variational inference objective function of Kullbac
Externí odkaz:
http://arxiv.org/abs/2410.02490
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