Zobrazeno 1 - 10
of 232 693
pro vyhledávání: '"Background Knowledge"'
Automated planning is a form of declarative problem solving which has recently drawn attention from the machine learning (ML) community. ML has been applied to planning either as a way to test `reasoning capabilities' of architectures, or more pragma
Externí odkaz:
http://arxiv.org/abs/2410.07923
Causality plays a pivotal role in various fields of study. Based on the framework of causal graphical models, previous works have proposed identifying whether a variable is a cause or non-cause of a target in every Markov equivalent graph solely by l
Externí odkaz:
http://arxiv.org/abs/2408.07890
Low sample efficiency is an enduring challenge of reinforcement learning (RL). With the advent of versatile large language models (LLMs), recent works impart common-sense knowledge to accelerate policy learning for RL processes. However, we note that
Externí odkaz:
http://arxiv.org/abs/2407.03964
Identifying causal relations is crucial for a variety of downstream tasks. In additional to observational data, background knowledge (BK), which could be attained from human expertise or experiments, is usually introduced for uncovering causal relati
Externí odkaz:
http://arxiv.org/abs/2407.15259
Autor:
Buliga, Andrei, Di Francescomarino, Chiara, Ghidini, Chiara, Donadello, Ivan, Maggi, Fabrizio Maria
Counterfactual explanations suggest what should be different in the input instance to change the outcome of an AI system. When dealing with counterfactual explanations in the field of Predictive Process Monitoring, however, control flow relationships
Externí odkaz:
http://arxiv.org/abs/2403.11642
LAKE-RED: Camouflaged Images Generation by Latent Background Knowledge Retrieval-Augmented Diffusion
Autor:
Zhao, Pancheng, Xu, Peng, Qin, Pengda, Fan, Deng-Ping, Zhang, Zhicheng, Jia, Guoli, Zhou, Bowen, Yang, Jufeng
Camouflaged vision perception is an important vision task with numerous practical applications. Due to the expensive collection and labeling costs, this community struggles with a major bottleneck that the species category of its datasets is limited
Externí odkaz:
http://arxiv.org/abs/2404.00292
Neurosymbolic AI is a growing field of research aiming to combine neural networks learning capabilities with the reasoning abilities of symbolic systems. This hybridization can take many shapes. In this paper, we propose a new formalism for supervise
Externí odkaz:
http://arxiv.org/abs/2402.13019
Publikováno v:
Application Research of Computers / Jisuanji Yingyong Yanjiu. Oct2024, Vol. 41 Issue 10, p2993-2999. 7p.