Zobrazeno 1 - 10
of 421
pro vyhledávání: '"Shi Tianyu"'
Autor:
Shi Tianyu, Govindasamy Vimala AP
Publikováno v:
Applied Mathematics and Nonlinear Sciences, Vol 9, Iss 1 (2024)
This paper classifies the data of the original data set by random forest algorithm, selects the nodes in the attribute space for iteration, and gets the number of decision trees in the random forest. Based on the decision tree, the information gain r
Externí odkaz:
https://doaj.org/article/3afa46d278984b7299511bf19cb5f614
Large language models (LLMs) with long-context processing are still challenging because of their implementation complexity, training efficiency and data sparsity. To address this issue, a new paradigm named Online Long-context Processing (OLP) is pro
Externí odkaz:
http://arxiv.org/abs/2409.18014
Large language models (LLMs) have made significant advances in the field of natural language processing, but they still face challenges such as continuous decision-making. In this research, we propose a novel framework by integrating iterative feedba
Externí odkaz:
http://arxiv.org/abs/2409.00872
As large language models (LLMs) improve their capabilities in handling complex tasks, the issues of computational cost and efficiency due to long prompts are becoming increasingly prominent. To accelerate model inference and reduce costs, we propose
Externí odkaz:
http://arxiv.org/abs/2409.00855
Today's image generation systems are capable of producing realistic and high-quality images. However, user prompts often contain ambiguities, making it difficult for these systems to interpret users' potential intentions. Consequently, machines need
Externí odkaz:
http://arxiv.org/abs/2409.07464
Autor:
Yim, Yauwai, Chan, Chunkit, Shi, Tianyu, Deng, Zheye, Fan, Wei, Zheng, Tianshi, Song, Yangqiu
Large language models (LLMs) have shown success in handling simple games with imperfect information and enabling multi-agent coordination, but their ability to facilitate practical collaboration against other agents in complex, imperfect information
Externí odkaz:
http://arxiv.org/abs/2408.02559
Over the last decade, there has been increasing interest in autonomous driving systems. Reinforcement Learning (RL) shows great promise for training autonomous driving controllers, being able to directly optimize a combination of criteria such as eff
Externí odkaz:
http://arxiv.org/abs/2407.16857
Recent advancements in large language models (LLMs) have unlocked the potential for generating high-quality game commentary. However, producing insightful and engaging commentary for complex games with incomplete information remains a significant cha
Externí odkaz:
http://arxiv.org/abs/2406.17807
Automated code generation is a pivotal capability of large language models (LLMs). However, assessing this capability in real-world scenarios remains challenging. Previous methods focus more on low-level code generation, such as model loading, instea
Externí odkaz:
http://arxiv.org/abs/2406.04712
The task of industrial detection based on deep learning often involves solving two problems: (1) obtaining sufficient and effective data samples, (2) and using efficient and convenient model training methods. In this paper, we introduce a novel defec
Externí odkaz:
http://arxiv.org/abs/2407.03332