Results 11 to 20 of about 115,093 (312)
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
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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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Efficient Learning for Undirected Topic Models [PDF]
, 2015 Replicated Softmax model, a well-known undirected topic model, is powerful in extracting semantic representations of documents. Traditional learning strategies such as Contrastive Divergence are very inefficient.
Gu, Jiatao, Li, Victor O. K.
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Hyperbolic Contrastive Learning
, 2023 Learning good image representations that are beneficial to downstream tasks is a challenging task in computer vision. As such, a wide variety of self-supervised learning approaches have been proposed. Among them, contrastive learning has shown competitive performance on several benchmark datasets.
Yue, Yun +3 more
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Self-supervised Dynamic Graph Representation Learning Approach Based on Contrastive Prediction [PDF]
Jisuanji kexue, 2023 In recent years,graph self-supervised learning represented by graph contrastive learning has become a hot research to-pic in the field of graph learning.This learning paradigm does not depend on node labels and has good generalization ability.However ...
JIANG Linpu, CHEN Kejia
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Stochastic Contrastive Learning
, 2021 While state-of-the-art contrastive Self-Supervised Learning (SSL) models produce results competitive with their supervised counterparts, they lack the ability to infer latent variables. In contrast, prescribed latent variable (LV) models enable attributing uncertainty, inducing task specific compression, and in general allow for more interpretable ...
Ramapuram, Jason +3 more
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SC-FGCL: Self-Adaptive Cluster-Based Federal Graph Contrastive Learning
IEEE Open Journal of the Computer Society, 2023 As a self-supervised learning method, the graph contrastive learning achieve admirable performance in graph pre-training tasks, and can be fine-tuned for multiple downstream tasks such as protein structure prediction, social recommendation, etc.
Tingqi Wang +4 more
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Equivariant Contrastive Learning
, 2021 In state-of-the-art self-supervised learning (SSL) pre-training produces semantically good representations by encouraging them to be invariant under meaningful transformations prescribed from human knowledge. In fact, the property of invariance is a trivial instance of a broader class called equivariance, which can be intuitively understood as the ...
Dangovski, Rumen +7 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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Signal Contrastive Enhanced Graph Collaborative Filtering for Recommendation
Data Science and Engineering, 2023 Graph collaborative filtering methods have shown great performance improvements compared with deep neural network-based models. However, these methods suffer from data sparsity and data noise problems.
Zhi-Yuan Li +3 more
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