Results 31 to 40 of about 375,534 (168)
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
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Attributed Bipartite Network Representation Learning
Jisuanji kexue yu tansuo, 2021 Existing network embedding models are mostly designed for homogeneous networks or heterogeneous networks, but ignore the special features of bipartite network which arise in recommender systems, search engines, question answering systems and so on ...
ZHAO Xueli, LU Guangyue, LV Shaoqing, ZHANG Pan
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On Representation Learning with Feedback
CoRR, 2022 This note complements the author's recent paper "Robust representation learning with feedback for single image deraining" by providing heuristically theoretical explanations on the mechanism of representation learning with feedback, namely an essential merit of the works presented in this recent article.
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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
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Motif-Aware Adversarial Graph Representation Learning
IEEE Access, 2022 Graph representation learning has been extensively studied in recent years. It has been proven effective in network analysis and mining tasks such as node classification and link prediction.
Ming Zhao +3 more
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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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ESANN 2023 proceesdings, 2023 In a broad range of real-world machine learning applications, representing examples as graphs is crucial to avoid a loss of information. For this reason, in the last few years, the definition of machine learning methods, particularly neural networks, for graph-structured inputs has been gaining increasing attention.
Davide Bacciu +6 more
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Socially Supervised Representation Learning: The Role of Subjectivity in Learning Efficient Representations [PDF]
International Joint Conference on Autonomous Agents and Multiagent Systems, 2022 Despite its rise as a prominent solution to the data inefficiency of today's machine learning models, self-supervised learning has yet to be studied from a purely multi-agent perspective. In this work, we propose that aligning internal subjective representations, which naturally arise in a multi-agent setup where agents receive partial observations of ...
Taylor, Julius +2 more
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Learning representations of learning representations
CoRRThe ICLR conference is unique among the top machine learning conferences in that all submitted papers are openly available. Here we present the ICLR dataset consisting of abstracts of all 24 thousand ICLR submissions from 2017-2024 with meta-data, decision scores, and custom keyword-based labels.
Rita González-Márquez, Dmitry Kobak
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Growing Representation Learning
CoRR, 2021 8 pages, 5 ...
Ryan King, Bobak Mortazavi
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