14 citations · 22 across the 5 of their papers we have counts for
5 papers
Empirical Evaluation and Theoretical Analysis for Representation Learning: A Survey
Kento Nozawa, Issei Sato
Representation learning enables us to automatically extract generic feature representations from a dataset to solve another machine learning task. Recently, extracted feature repre…
On the Surrogate Gap between Contrastive and Supervised Losses
Han Bao, Yoshihiro Nagano, Kento Nozawa
Contrastive representation learning encourages data representation to make semantically similar pairs closer than randomly drawn negative samples, which has been successful in vari…
Understanding Negative Samples in Instance Discriminative Self-supervised Representation Learning
Kento Nozawa, Issei Sato
Instance discriminative self-supervised representation learning has been attracted attention thanks to its unsupervised nature and informative feature representation for downstream…
PAC-Bayesian Contrastive Unsupervised Representation Learning
Kento Nozawa, Pascal Germain, Benjamin Guedj
Contrastive unsupervised representation learning (CURL) is the state-of-the-art technique to learn representations (as a set of features) from unlabelled data. While CURL has colle…
PAC-Bayes Analysis of Sentence Representation
Kento Nozawa, Issei Sato
Learning sentence vectors from an unlabeled corpus has attracted attention because such vectors can represent sentences in a lower dimensional and continuous space. Simple heuristi…