Zobrazeno 1 - 10
of 19 170
pro vyhledávání: '"Guided learning"'
Autor:
Yu, Runlong, Qiu, Chonghao, Ladwig, Robert, Hanson, Paul C., Xie, Yiqun, Li, Yanhua, Jia, Xiaowei
This paper introduces a \textit{Process-Guided Learning (Pril)} framework that integrates physical models with recurrent neural networks (RNNs) to enhance the prediction of dissolved oxygen (DO) concentrations in lakes, which is crucial for sustainin
Externí odkaz:
http://arxiv.org/abs/2411.12973
Multi-stage decision-making is crucial in various real-world artificial intelligence applications, including recommendation systems, autonomous driving, and quantitative investment systems. In quantitative investment, for example, the process typical
Externí odkaz:
http://arxiv.org/abs/2411.10496
Autor:
Gunasekaran, Skye, Kembay, Assel, Ladret, Hugo, Zhu, Rui-Jie, Perrinet, Laurent, Kavehei, Omid, Eshraghian, Jason
Accurate time-series forecasting is essential across a multitude of scientific and industrial domains, yet deep learning models often struggle with challenges such as capturing long-term dependencies and adapting to drift in data distributions over t
Externí odkaz:
http://arxiv.org/abs/2410.15217
Explaining the decision-making processes of Artificial Intelligence (AI) models is crucial for addressing their "black box" nature, particularly in tasks like image classification. Traditional eXplainable AI (XAI) methods typically rely on unimodal e
Externí odkaz:
http://arxiv.org/abs/2411.13053
Spatial transcriptomics (ST) has emerged as an advanced technology that provides spatial context to gene expression. Recently, deep learning-based methods have shown the capability to predict gene expression from WSI data using ST data. Existing appr
Externí odkaz:
http://arxiv.org/abs/2412.04072
We address the decision-making capability within an end-to-end planning framework that focuses on motion prediction, decision-making, and trajectory planning. Specifically, we formulate decision-making and trajectory planning as a differentiable nonl
Externí odkaz:
http://arxiv.org/abs/2412.01234
Autor:
Jiang, Jin, Yan, Yuchen, Liu, Yang, Jin, Yonggang, Peng, Shuai, Zhang, Mengdi, Cai, Xunliang, Cao, Yixin, Gao, Liangcai, Tang, Zhi
In this paper, we present a novel approach, called LogicPro, to enhance Large Language Models (LLMs) complex Logical reasoning through Program Examples. We do this effectively by simply utilizing widely available algorithmic problems and their code s
Externí odkaz:
http://arxiv.org/abs/2409.12929
Autor:
Lu, Yang, Yao, Weijia, Xiao, Yongqian, Zhang, Xinglong, Xu, Xin, Wang, Yaonan, Xiao, Dingbang
In obstacle-dense scenarios, providing safe guidance for mobile robots is critical to improve the safe maneuvering capability. However, the guidance provided by standard guiding vector fields (GVFs) may limit the motion capability due to the improper
Externí odkaz:
http://arxiv.org/abs/2405.08283
In the task of Knowledge Graph Completion (KGC), the existing datasets and their inherent subtasks carry a wealth of shared knowledge that can be utilized to enhance the representation of knowledge triplets and overall performance. However, no curren
Externí odkaz:
http://arxiv.org/abs/2405.06696