244 citations · 964 across the 44 of their papers we have counts for
5 papers · 1 filter
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…
A Unified Probabilistic Model for Learning Latent Factors and Their Connectivities from High-Dimensional Data
Ricardo Pio Monti, Aapo Hyvärinen
Connectivity estimation is challenging in the context of high-dimensional data. A useful preprocessing step is to group variables into clusters, however, it is not always clear how…
Deep Energy Estimator Networks
Saeed Saremi, Arash Mehrjou, Bernhard Schölkopf +1
Density estimation is a fundamental problem in statistical learning. This problem is especially challenging for complex high-dimensional data due to the curse of dimensionality. A…
Estimation of Non-Normalized Mixture Models and Clustering Using Deep Representation
Takeru Matsuda, Aapo Hyvarinen
We develop a general method for estimating a finite mixture of non-normalized models. Here, a non-normalized model is defined to be a parametric distribution with an intractable no…
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…