Zobrazeno 1 - 10
of 153
pro vyhledávání: '"Chen, Yize"'
Growing concerns over climate change call for improved techniques for estimating and quantifying the greenhouse gas emissions associated with electricity generation and transmission. Among the emission metrics designated for power grids, locational m
Externí odkaz:
http://arxiv.org/abs/2411.12104
Autor:
Shi, Di, Zhang, Qiang, Hong, Mingguo, Wang, Fengyu, Maslennikov, Slava, Luo, Xiaochuan, Chen, Yize
Deep reinforcement learning (DRL) holds significant promise for managing voltage control challenges in simulated power grid environments. However, its real-world application in power system operations remains underexplored. This study rigorously eval
Externí odkaz:
http://arxiv.org/abs/2410.19880
Autor:
Jiang, Xun, Li, Feng, Zhao, Han, Wang, Jiaying, Shao, Jun, Xu, Shihao, Zhang, Shu, Chen, Weiling, Tang, Xavier, Chen, Yize, Wu, Mengyue, Ma, Weizhi, Wang, Mengdi, Chen, Tianqiao
Large language models (LLMs) like GPTs, trained on vast datasets, have demonstrated impressive capabilities in language understanding, reasoning, and planning, achieving human-level performance in various tasks. Most studies focus on enhancing these
Externí odkaz:
http://arxiv.org/abs/2410.15665
Detecting deepfakes has become an important task. Most existing detection methods provide only real/fake predictions without offering human-comprehensible explanations. Recent studies leveraging MLLMs for deepfake detection have shown improvements in
Externí odkaz:
http://arxiv.org/abs/2410.06126
Multi-objective reinforcement learning (MORL) excels at handling rapidly changing preferences in tasks that involve multiple criteria, even for unseen preferences. However, previous dominating MORL methods typically generate a fixed policy set or pre
Externí odkaz:
http://arxiv.org/abs/2410.02236
Hierarchical Imitation Learning (HIL) is a promising approach for tackling long-horizon decision-making tasks. While it is a challenging task due to the lack of detailed supervisory labels for sub-goal learning, and reliance on hundreds to thousands
Externí odkaz:
http://arxiv.org/abs/2410.02231
Forecasting faithful trajectories of multivariate time series from practical scopes is essential for reasonable decision-making. Recent methods majorly tailor generative conditional diffusion models to estimate the target temporal predictive distribu
Externí odkaz:
http://arxiv.org/abs/2410.02168
Object-centric surface reconstruction from multi-view images is crucial in creating editable digital assets for AR/VR. Due to the lack of geometric constraints, existing methods, e.g., NeuS necessitate annotating the object masks to reconstruct compa
Externí odkaz:
http://arxiv.org/abs/2409.13158
Recent breakthroughs of large language models (LLMs) have exhibited superior capability across major industries and stimulated multi-hundred-billion-dollar investment in AI-centric data centers in the next 3-5 years. This, in turn, bring the increasi
Externí odkaz:
http://arxiv.org/abs/2409.11416
Day-ahead unit commitment (UC) is a fundamental task for power system operators, where generator statuses and power dispatch are determined based on the forecasted nodal net demands. The uncertainty inherent in renewables and load forecasting require
Externí odkaz:
http://arxiv.org/abs/2408.05185