Results 21 to 30 of about 375,534 (168)
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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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
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Representation and learning of invariance
Proceedings of 1st International Conference on Image Processing, 2002 Invariance is a very important property of features that are useful for vision. A great deal of research on this subject is going on at different labs. While invariance mechanisms can be prescribed for certain descriptors, it is our firm belief that this is not feasible for descriptors of higher level properties in general.
Nordberg, Klas +2 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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Matryoshka Representation Learning
Advances in Neural Information Processing Systems 35, 2022 Learned representations are a central component in modern ML systems, serving a multitude of downstream tasks. When training such representations, it is often the case that computational and statistical constraints for each downstream task are unknown.
Aditya Kusupati +10 more
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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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Analysis of David Kolb's Learning Style According to Mathematical Representation Ability
Journal of Medives: Journal of Mathematics Education IKIP Veteran Semarang, 2021 The purpose of this study was to describe David Kolb's learning style according to the mathematical representation of students. This research is qualitative. The subjects of this study were students of class VIII SMP Agus Salim Semarang.
Umi Hajaro +2 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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Multi-Task Network Representation Learning
Frontiers in Neuroscience, 2020 Networks, such as social networks, biochemical networks, and protein-protein interaction networks are ubiquitous in the real world. Network representation learning aims to embed nodes in a network as low-dimensional, dense, real-valued vectors, and ...
Yu Xie +4 more
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A Review of Disentangled Representation Learning for Remote Sensing Data
CAAI Artificial Intelligence Research, 2022 Representation learning is one of the core problems in machine learning research. The transition of input representations for machine learning algorithms from handcraft features, which dominated in the past, to the potential representations learned ...
Mi Wang +3 more
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