Results 1 to 10 of about 654,785 (260)
Learning Structures Through Reinforcement
, 2018 How the brain uses reinforcement feedback to make simple choices that lead to reward is well understood. However, this ability is often considered insufficient to account for the flexibility and efficiency of human decision-making. In this chapter, we show that the computations of model-free reinforcement learning (RL) can in fact account for complex ...
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Human-Guided Learning for Probabilistic Logic Models
Frontiers in Robotics and AI, 2018 Advice-giving has been long explored in the artificial intelligence community to build robust learning algorithms when the data is noisy, incorrect or even insufficient. While logic based systems were effectively used in building expert systems, the role
Phillip Odom, Sriraam Natarajan
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IEEE Access, 2017 The learning ability of neural networks (NNs) enables them to solve time series prediction problems. Off-line training can be applied to design the structure and weights of NNs when sufficient training data are available.
Shih-Hung Yang +3 more
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, 2020 Very recently, the unexpected combination of data structures and machine learning has led to the development of a new area of research, called learned data structures. Their distinguishing trait is the ability to reveal and exploit patterns and trends in the input data for achieving more efficiency in time and space, compared to previously known data ...
Paolo Ferragina, Giorgio Vinciguerra
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Learning dynamic cognitive map with autonomous navigation
Frontiers in Computational NeuroscienceInspired by animal navigation strategies, we introduce a novel computational model to navigate and map a space rooted in biologically inspired principles.
Daria de Tinguy +2 more
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EntropyIn uncertain battlefield environments, rapid and accurate detection, identification of hostile targets, and assessment of threat levels are crucial for supporting effective decision-making.
Zuoxin Zeng, Jinye Peng, Qi Feng
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Structure learning for gene regulatory networks. [PDF]
PLoS Comput Biol, 2023 Federico A, Kern J, Varelas X, Monti S.
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Kernel-Based Independence Tests for Causal Structure Learning on Functional Data. [PDF]
Entropy (Basel), 2023 Laumann F +4 more
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SAMBA: Structure-Learning of Aquaculture Microbiomes Using a Bayesian Approach. [PDF]
Genes (Basel), 2023 Soriano B +9 more
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Causal Structure Learning: A Combinatorial Perspective. [PDF]
Found Comut Math, 2022 Squires C, Uhler C.
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