2 citations · 2 across the 2 of their papers we have counts for
4 papers · 1 filter
Identifiability of a statistical model with two latent vectors: Importance of the dimensionality relation and application to graph embedding
Hiroaki Sasaki
Identifiability of statistical models is a key notion in unsupervised representation learning. Recent work of nonlinear independent component analysis (ICA) employs auxiliary data…
Robust modal regression with direct log-density derivative estimation
Hiroaki Sasaki, Tomoya Sakai, Takafumi Kanamori
Modal regression is aimed at estimating the global mode (i.e., global maximum) of the conditional density function of the output variable given input variables, and has led to regr…
Neural-Kernelized Conditional Density Estimation
Hiroaki Sasaki, Aapo Hyvärinen
Conditional density estimation is a general framework for solving various problems in machine learning. Among existing methods, non-parametric and/or kernel-based methods are often…
Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning
Aapo Hyvarinen, Hiroaki Sasaki, Richard E. Turner
Nonlinear ICA is a fundamental problem for unsupervised representation learning, emphasizing the capacity to recover the underlying latent variables generating the data (i.e., iden…