1 citations · 1 across the 7 of their papers we have counts for
8 papers
Is Spectral Self-Supervised Learning a Minimax Optimal Method of Density Ratio Estimation?
Hiroki Waida, Takafumi Kanamori
Spectral self-supervised learning is a method of density ratio estimation for the setting in which only samples drawn from the joint density of the numerator are available. The met…
Statistical learnability of smooth boundaries via pairwise binary classification with deep ReLU networks
Hiroki Waida, Takafumi Kanamori
The topic of nonparametric estimation of smooth boundaries is extensively studied in the conventional setting where pairs of single covariate and response variable are observed. Ho…
Scaling-based Data Augmentation for Generative Models and its Theoretical Extension
Yoshitaka Koike, Takumi Nakagawa, Hiroki Waida +1
This paper studies stable learning methods for generative models that enable high-quality data generation. Noise injection is commonly used to stabilize learning. However, selectin…
Robust VAEs via Generating Process of Noise Augmented Data
Hiroo Irobe, Wataru Aoki, Kimihiro Yamazaki +5
Advancing defensive mechanisms against adversarial attacks in generative models is a critical research topic in machine learning. Our study focuses on a specific type of generative…
Investigating Self-Supervised Image Denoising with Denaturation
Hiroki Waida, Kimihiro Yamazaki, Atsushi Tokuhisa +2
Self-supervised learning for image denoising problems in the presence of denaturation for noisy data is a crucial approach in machine learning. However, theoretical understanding o…
Denoising Cosine Similarity: A Theory-Driven Approach for Efficient Representation Learning
Takumi Nakagawa, Yutaro Sanada, Hiroki Waida +5
Representation learning has been increasing its impact on the research and practice of machine learning, since it enables to learn representations that can apply to various downstr…
Towards Understanding the Mechanism of Contrastive Learning via Similarity Structure: A Theoretical Analysis
Hiroki Waida, Yuichiro Wada, Léo Andéol +3
Contrastive learning is an efficient approach to self-supervised representation learning. Although recent studies have made progress in the theoretical understanding of contrastive…
Deep Clustering with a Constraint for Topological Invariance based on Symmetric InfoNCE
Yuhui Zhang, Yuichiro Wada, Hiroki Waida +3
We consider the scenario of deep clustering, in which the available prior knowledge is limited. In this scenario, few existing state-of-the-art deep clustering methods can perform…