Results 21 to 30 of about 26,557 (311)
Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning [PDF]
, 2021 Anomaly detection on attributed networks attracts considerable research interests due to wide applications of attributed networks in modeling a wide range of complex systems.
Karypis, George +5 more
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Al-Takhlil al-Taqabuly fi Ta'lim al-Lughah al-'Arabiyyah
Jurnal Al Bayan: Jurnal Jurusan Pendidikan Bahasa Arab, 2020 This article explains systematically the nature of contrastive analysis in language learning. Throughout this article the writer investigates the development of contrastive analysis in the field of language learning, its main objectives, some hypotheses ...
Ahmad Bukhari Muslim
doaj +1 more source
Grouped Contrastive Learning of Self-Supervised Sentence Representation
Applied Sciences, 2023 This paper proposes a method called Grouped Contrastive Learning of self-supervised Sentence Representation (GCLSR), which can learn an effective and meaningful representation of sentences. Previous works maximize the similarity between two vectors to be
Qian Wang +3 more
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Decoupled Contrastive Learning
, 2022 Contrastive learning (CL) is one of the most successful paradigms for self-supervised learning (SSL). In a principled way, it considers two augmented "views" of the same image as positive to be pulled closer, and all other images as negative to be pushed further apart.
Chun-Hsiao Yeh +5 more
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A Contrastive Rule for Meta-Learning
Advances in Neural Information Processing Systems 35, 2022 Meta-learning algorithms leverage regularities that are present on a set of tasks to speed up and improve the performance of a subsidiary learning process. Recent work on deep neural networks has shown that prior gradient-based learning of meta-parameters can greatly improve the efficiency of subsequent learning.
Zucchet, Nicolas +4 more
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Attraction and Repulsion: Unsupervised Domain Adaptive Graph Contrastive Learning Network [PDF]
, 2022 Graph convolutional networks (GCNs) are important techniques for analytics tasks related to graph data. To date, most GCNs are designed for a single graph domain. They are incapable of transferring knowledge from/to different domains (graphs), due to the
Wu, M +5 more
core +1 more source
Supervised Contrastive Learning
CoRR, 2020 Contrastive learning applied to self-supervised representation learning has seen a resurgence in recent years, leading to state of the art performance in the unsupervised training of deep image models. Modern batch contrastive approaches subsume or significantly outperform traditional contrastive losses such as triplet, max-margin and the N-pairs loss.
Prannay Khosla +8 more
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Enriched music representations with multiple cross-modal contrastive learning [PDF]
, 2021 Modeling various aspects that make a music piece unique is a challenging task, requiring the combination of multiple sources of information. Deep learning is commonly used to obtain representations using various sources of information, such as the audio,
Xavier Favory +9 more
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Contrastive Fairness in Machine Learning [PDF]
IEEE Letters of the Computer Society, 2020 Was it fair that Harry was hired but not Barry? Was it fair that Pam was fired instead of Sam? How can one ensure fairness when an intelligent algorithm takes these decisions instead of a human? How can one ensure that the decisions were taken based on merit and not on protected attributes like race or sex? These are the questions that must be answered
Tapabrata Chakraborti +2 more
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CoRR, 2020 A prominent technique for self-supervised representation learning has been to contrast semantically similar and dissimilar pairs of samples. Without access to labels, dissimilar (negative) points are typically taken to be randomly sampled datapoints, implicitly accepting that these points may, in reality, actually have the same label.
Ching-Yao Chuang +4 more
openaire +3 more sources