Mainstream statistical methodology is generally applicable to data observed in Euclidean
space. There are, however, numerous contexts of considerable scientific interest in which …

This paper aims at proposing an unsupervised hierarchical nonparametric Bayesian
framework for modeling axial data (ie, observations are axes of direction) that can be …

From the celebrated Gaussian mixture, model averaging estimators to the cutting-edge multi-
Bernoulli mixture of various forms, finite mixture models offer a fundamental and flexible …

For the purpose of enhancing the discriminability of convolutional neural networks (CNNs)
and facilitating the optimization, we investigate the activation function for a neural network …

In this paper, we develop a novel variational Bayesian learning method for the Dirichlet
process (DP) mixture of the inverted Dirichlet distributions, which has been shown to be very …

In this paper, we propose novel strategies for neutral vector variable decorrelation. Two
fundamental invertible transformations, namely, serial nonlinear transformation and parallel …

In recent years, clustering methods based on deep generative models have received great
attention in various unsupervised applications, due to their capabilities for learning …

For most of the non-Gaussian statistical models, the data being modeled represent strongly
structured properties, such as scalar data with bounded support (eg, beta distribution) …

Self-distillation methods using Siamese networks are popular for self-supervised pre-
training. DINO is one such method based on a cross-entropy loss between $ K …

Unsupervised learning and meta-learning share a common goal of enhancing learning
efficiency compared to starting from scratch. However, meta-learning methods are …