Results 21 to 30 of about 7,883,935 (303)
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
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
core +1 more source
Deep boundary‑aware clustering by jointly optimizing unsupervised representation learning [PDF]
, 2022 Deep clustering obtains feature representation generally and then performs clustering for high dimension real-world data. However, conventional solutions are two-stage embedding learning-based methods and these two processes are separate and independent,
Li, Lin +4 more
core +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
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
openaire +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
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
openaire +7 more sources
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.
openaire +3 more sources
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
doaj +1 more source
Unnes Journal of Mathematics Education, 2023 This study aims to analyze whether the Cooperative Learning type of Reciprocal Peer Tutoring (RPT) is effective in enhancing students' mathematical representation abilities, whether it is more effective than PBL in enhancing students' mathematical ...
Fifi Suryani, Mashuri Mashuri
doaj +1 more source