8 citations · 14 across the 7 of their papers we have counts for
7 papers
TULiP: Test-time Uncertainty Estimation via Linearization and Weight Perturbation
Yuhui Zhang, Dongshen Wu, Yuichiro Wada +1
A reliable uncertainty estimation method is the foundation of many modern out-of-distribution (OOD) detectors, which are critical for safe deployments of deep learning models in th…
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…
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…
Fast and Multi-aspect Mining of Complex Time-stamped Event Streams
Kota Nakamura, Yasuko Matsubara, Koki Kawabata +3
Given a huge, online stream of time-evolving events with multiple attributes, such as online shopping logs: (item, price, brand, time), and local mobility activities: (pick-up and…
Learning Domain Invariant Representations by Joint Wasserstein Distance Minimization
Léo Andeol, Yusei Kawakami, Yuichiro Wada +3
Domain shifts in the training data are common in practical applications of machine learning; they occur for instance when the data is coming from different sources. Ideally, a ML m…