Results 21 to 30 of about 8,190,264 (303)
Roy-lab/graph-representation-learning: v1.1
, 2023 Source Code and Supplementary Materials for Paper "Benchmarking graph representation learning algorithms for detecting modules in molecular networks"
zsong96wisc, Sushmita Roy
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IEEE Access, 2021 Machine learning has been widely applied in the fields of biomedicine, computational biology, bioinformatics, image processing, and so on. The performance of machine learning methods mainly relies on feature representation that is the mapping from ...
Feifei Cui +5 more
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On learning with imperfect representations [PDF]
2011 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL), 2011 In this paper we present a perspective on the relationship between learning and representation in sequential decision making tasks. We undertake a brief survey of existing real-world applications, which demonstrates that the classical “tabular” representation seldom applies in practice.
Shivaram Kalyanakrishnan +1 more
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Roy-lab/graph-representation-learning: v1.3
, 2023 Source Code and Supplementary Materials for Paper "Benchmarking graph representation learning algorithms for detecting modules in molecular networks"
zsong96wisc, Sushmita Roy
core +1 more source
Journal of Medives: Journal of Mathematics Education IKIP Veteran Semarang, 2022 This research is a descriptive study that aims to explain students' mathematical representation abilities in solving AKM numeracy questions after problem based learning is implemented, to explain the implementation of the problem based learning learning ...
Karenina Rizka Alifa +3 more
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A Manifold Learning Perspective on Representation Learning: Learning Decoder and Representations without an Encoder [PDF]
Entropy, 2021 Autoencoders are commonly used in representation learning. They consist of an encoder and a decoder, which provide a straightforward method to map n-dimensional data in input space to a lower m-dimensional representation space and back. The decoder itself defines an m-dimensional manifold in input space.
Viktoria Schuster, Anders Krogh
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Learning internal representations [PDF]
Proceedings of the eighth annual conference on Computational learning theory - COLT '95, 1995 Probably the most important problem in machine learning is the preliminary biasing of a learner's hypothesis space so that it is small enough to ensure good generalisation from reasonable training sets, yet large enough that it contains a good solution to the problem being learnt.
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ESANN 2023 proceesdings, 2023 In a broad range of real-world machine learning applications, representing examples as graphs is crucial to avoid a loss of information. For this reason, in the last few years, the definition of machine learning methods, particularly neural networks, for graph-structured inputs has been gaining increasing attention.
Davide Bacciu +6 more
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Knowledge representation issues in control knowledge learning [PDF]
, 2000 Seventeenth International Conference on Machine Learning. Stanford, CA, USA, 29 June-2 July, 2000Knowledge representation is a key issue for any machine learning task.
Aler, Ricardo +2 more
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Triplet Loss Network for Unsupervised Domain Adaptation
Algorithms, 2019 Domain adaptation is a sub-field of transfer learning that aims at bridging the dissimilarity gap between different domains by transferring and re-using the knowledge obtained in the source domain to the target domain.
Imad Eddine Ibrahim Bekkouch +4 more
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