Results 31 to 40 of about 476,790 (313)
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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Temporal Knowledge Graph Representation Learning [PDF]
Jisuanji kexue, 2022 As a structured form of human knowledge,knowledge graphs have played a great supportive role in supporting the semantic intercommunication of massive,multi-source,heterogeneous data,and effectively support tasks such as data analysis,attracting the ...
XU Yong-xin, ZHAO Jun-feng, WANG Ya-sha, XIE Bing, YANG Kai
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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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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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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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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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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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Role-Based Network Representation Learning Method [PDF]
Jisuanji gongcheng, 2021 Network representation learning is widely used to obtain the characteristics and semantics of network nodes. The existing network representation learning methods mainly study the adjacency matrix or the power of the adjacency matrix,making a node in the ...
XU You, WANG Xiaoping, XIONG Yun
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Autonomous Learning of Representations [PDF]
KI - Künstliche Intelligenz, 2015 Besides the core learning algorithm itself, one major question in machine learning is how to best encode given training data such that the learning technology can efficiently learn based thereon and generalize to novel data. While classical approaches often rely on a hand coded data representation, the topic of autonomous representation or feature ...
Oliver Walter +4 more
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