Results 21 to 30 of about 476,790 (313)
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
Exploratory State Representation Learning
Frontiers in Robotics and AI, 2022 Not having access to compact and meaningful representations is known to significantly increase the complexity of reinforcement learning (RL). For this reason, it can be useful to perform state representation learning (SRL) before tackling RL tasks ...
Astrid Merckling +3 more
doaj +1 more source
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
doaj +1 more source
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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Involvement of NRF2 in Breast Cancer and Possible Therapeutical Role of Polyphenols and Melatonin
Molecules, 2021 Oxidative stress is defined as a disturbance in the prooxidant/antioxidant balance in favor of the former and a loss of control over redox signaling processes, leading to potential biomolecular damage.
Alev Tascioglu Aliyev +4 more
doaj +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
doaj +1 more source
Learning Graph Representations [PDF]
, 2021 Social and information networks are gaining huge popularity recently due to their various applications. Knowledge representation through graphs in the form of nodes and edges should preserve as many characteristics of the original data as possible.
Rucha Bhalchandra Joshi +1 more
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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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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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Toward Causal Representation Learning [PDF]
Proceedings of the IEEE, 2021 ISSN:1558 ...
Bernhard Schölkopf +6 more
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