2 citations · 2 across the 2 of their papers we have counts for
3 papers
cs.LG2022
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…
cs.LG2019
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…
cs.LG2019★ 2 cited
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…