Zobrazeno 1 - 10
of 1 366
pro vyhledávání: '"Jin Yaochu"'
Multi-agent hierarchical reinforcement learning (MAHRL) has been studied as an effective means to solve intelligent decision problems in complex and large-scale environments. However, most current MAHRL algorithms follow the traditional way of using
Externí odkaz:
http://arxiv.org/abs/2411.01184
Multi-objective optimization has burgeoned as a potent methodology for informed decision-making in enhanced geothermal systems, aiming to concurrently maximize economic yield, ensure enduring geothermal energy provision, and curtail carbon emissions.
Externí odkaz:
http://arxiv.org/abs/2411.00504
Autor:
Hu, Xiang, Fu, Hongyu, Wang, Jinge, Wang, Yifeng, Li, Zhikun, Xu, Renjun, Lu, Yu, Jin, Yaochu, Pan, Lili, Lan, Zhenzhong
Scientific innovation is pivotal for humanity, and harnessing large language models (LLMs) to generate research ideas could transform discovery. However, existing LLMs often produce simplistic and repetitive suggestions due to their limited ability i
Externí odkaz:
http://arxiv.org/abs/2410.14255
Reinforcement learning methods have been used to optimize long-term user engagement in recommendation systems. However, existing reinforcement learning-based recommendation systems do not fully exploit the relevance of individual user behavior across
Externí odkaz:
http://arxiv.org/abs/2409.14872
Adversarial training (AT) has become an effective defense method against adversarial examples (AEs) and it is typically framed as a bi-level optimization problem. Among various AT methods, fast AT (FAT), which employs a single-step attack strategy to
Externí odkaz:
http://arxiv.org/abs/2407.12443
Deploying models across diverse devices demands tradeoffs among multiple objectives due to different resource constraints. Arguably, due to the small model trap problem in multi-objective neural architecture search (MO-NAS) based on a supernet, exist
Externí odkaz:
http://arxiv.org/abs/2407.13122
Multi-Task Evolutionary Optimization (MTEO), an important field focusing on addressing complex problems through optimizing multiple tasks simultaneously, has attracted much attention. While MTEO has been primarily focusing on task similarity, there r
Externí odkaz:
http://arxiv.org/abs/2406.14865
Graph neural networks (GNNs) have emerged as a powerful tool for solving combinatorial optimization problems (COPs), exhibiting state-of-the-art performance in both graph-structured and non-graph-structured domains. However, existing approaches lack
Externí odkaz:
http://arxiv.org/abs/2406.13125
Federated learning (FL) has emerged as a promising paradigm for training models on decentralized data while safeguarding data privacy. Most existing FL systems, however, assume that all machine learning models are of the same type, although it become
Externí odkaz:
http://arxiv.org/abs/2406.09680
Federated learning (FL) offers a privacy-preserving approach to machine learning for multiple collaborators without sharing raw data. However, the existence of non-independent and non-identically distributed (non-IID) datasets across different client
Externí odkaz:
http://arxiv.org/abs/2406.09682