Results 171 to 180 of about 266,150 (211)
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The Web Conference, 2023
Markov logic network (MLN) is a powerful statistical modeling framework for probabilistic logic reasoning. Despite the elegancy and effectiveness of MLN, the inference of MLN is known to suffer from an efficiency issue.
Huang Fang +3 more
semanticscholar +1 more source
Markov logic network (MLN) is a powerful statistical modeling framework for probabilistic logic reasoning. Despite the elegancy and effectiveness of MLN, the inference of MLN is known to suffer from an efficiency issue.
Huang Fang +3 more
semanticscholar +1 more source
Complex Video Action Reasoning via Learnable Markov Logic Network
Computer Vision and Pattern Recognition, 2022Profiting from the advance of deep convolutional networks, current state-of-the-art video action recognition models have achieved remarkable progress.
Yang Jin, Linchao Zhu, Yadong Mu
semanticscholar +1 more source
Toward Semantic Communication Protocols: A Probabilistic Logic Perspective
IEEE Journal on Selected Areas in Communications, 2022Classical medium access control (MAC) protocols are interpretable, yet their task-agnostic control signaling messages (CMs) are ill-suited for emerging mission-critical applications.
Sejin Seo +5 more
semanticscholar +1 more source
Asymptotical Stability and Stabilization of Continuous-Time Probabilistic Logic Networks
IEEE Transactions on Automatic Control, 2022Discrete-time probabilistic logic networks (DT-PLNs), of which probabilistic Boolean networks (PBNs) are a special type, are an important qualitative model for gene regulatory networks (GRNs). Although a DT-PLN can predict the long-term behavior of a GRN,
Yuqian Guo, Zhitao Li, Yang Liu, W. Gui
semanticscholar +1 more source
Generating Fake Documents Using Probabilistic Logic Graphs
IEEE Transactions on Dependable and Secure Computing, 2021Past research has shown that over 8 months may elapse between the time when a network is compromised and the time the attack is discovered. During this long gap, attackers can steal valuable intellectual property from the victim. The recent FORGE system [
Qian Han +5 more
semanticscholar +1 more source
IEEE Sensors Journal, 2021
The vehicle accident prediction methods are designed to improve the vehicular safety and reduce the rescue response time in the case of an accident.
Muhammad Usman +4 more
semanticscholar +1 more source
The vehicle accident prediction methods are designed to improve the vehicular safety and reduce the rescue response time in the case of an accident.
Muhammad Usman +4 more
semanticscholar +1 more source
Asymptotical Feedback Controllability of Continuous-time Probabilistic Logic Control Networks
2020 IEEE 16th International Conference on Control & Automation (ICCA), 2020This paper investigates the controllability problem of continuous-time probabilistic logic control networks (CT- PLCNs) under sampled-data feedback controls. Firstly, we point out that it is nonsense to define finite-time controllability with probability
Zhitao Li, Yuqian Guo, W. Gui
semanticscholar +1 more source
Logic‐based probabilistic network model to detect and track faults in a process system
Process safety progress, 2019Process systems are becoming complex due to a higher dependency among operational variables and complex control loops. Principal component analysis (PCA) is widely used to reduce the dimensionality of the complex process systems, while Bayesian networks (
Amr Ibrahim Tahoon +3 more
semanticscholar +1 more source
Towards Probabilistic Inductive Logic Programming with Neurosymbolic Inference and Relaxation
Theory and Practice of Logic ProgrammingMany inductive logic programming (ILP) methods are incapable of learning programs from probabilistic background knowledge, for example, coming from sensory data or neural networks with probabilities.
Fieke Hillerström, G. Burghouts
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A probabilistic logic neuron network for associative learning
, 1989 W. Kan, I. Aleksander
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