Results 21 to 30 of about 351,687 (262)
Hebbian Continual Representation Learning
Proceedings of the Annual Hawaii International Conference on System Sciences, 2023 Continual Learning aims to bring machine learning into a more realistic scenario, where tasks are learned sequentially and the i.i.d. assumption is not preserved. Although this setting is natural for biological systems, it proves very difficult for machine learning models such as artificial neural networks.
Morawiecki, Pawel +3 more
openaire +4 more sources
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 +6 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
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
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source
Learning More Universal Representations for Transfer-Learning [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020 Submitted to IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
Tamaazousti, Youssef +4 more
openaire +4 more sources
Review of Visual Representation Learning [PDF]
Jisuanji kexueRepresentation learning is an important step of artificial intelligence algorithm,where well designed representation can boost downstream tasks.With the development of deep learning in computer vision,visual representation learning has become ...
WANG Shuaiwei, LEI Jie, FENG Zunlei, LIANG Ronghua
doaj +1 more source
A survey of information network representation learning
Journal of Hebei University of Science and Technology, 2020 The network representation learning algorithm represents the information network as a low-dimensional dense real vector carrying the characteristic information of network nodes, and is applied to the input of downstream machine learning tasks.
Junhao LU, Yunfeng XU
doaj +1 more source