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Found 6 papers, 4 papers with code
Empirical Evaluation and Theoretical Analysis for Representation Learning: A Survey
Representation learning enables us to automatically extract generic feature representations from a dataset to solve another machine learning task.
On the Surrogate Gap between Contrastive and Supervised Losses
Recent theoretical studies have attempted to explain the benefit of the large negative sample size by upper-bounding the downstream classification loss with the contrastive loss.
Understanding Negative Samples in Instance Discriminative Self-supervised Representation Learning
Instance discriminative self-supervised representation learning has been attracted attention thanks to its unsupervised nature and informative feature representation for downstream tasks.
PAC-Bayesian Contrastive Unsupervised Representation Learning
Contrastive unsupervised representation learning (CURL) is the state-of-the-art technique to learn representations (as a set of features) from unlabelled data.
PAC-Bayes Analysis of Sentence Representation
Learning sentence vectors from an unlabeled corpus has attracted attention because such vectors can represent sentences in a lower dimensional and continuous space.
Node Centralities and Classification Performance for Characterizing Node Embedding Algorithms
Embedding graph nodes into a vector space can allow the use of machine learning to e. g. predict node classes, but the study of node embedding algorithms is immature compared to the natural language processing field because of a diverse nature of graphs.
