Bayesian optimization is a sample-efficient method for solving the optimization of a black-box function. In particular, it successfully shows its effectiveness in diverse applications such as hyperparameter optimization, automated machine learning, material design, sequential assembly, and chemical reaction optimization. In this paper we present an easy-to-use Bayesian optimization framework, referred to as BayesO, which is written in Python and licensed under the MIT license. To briefly introduce our software, we describe the functionality of BayesO and various components for software development.

Detecting and analyzing Alzheimer's disease (AD) in its early stages is a crucial and significant challenge. Speech data from AD patients can aid in diagnosing AD since the speech features have common patterns independent of race and spoken language. However, previous models for diagnosing AD from speech data have often focused on the characteristics of a single language, with no guarantee of scalability to other languages. In this study, we used the same method to extract acoustic features from two language datasets to diagnose AD. Using the Korean and English speech datasets, we used ten models capable of real-time AD and healthy control classification, regardless of language type. Four machine learning models were based on hand-crafted features, while the remaining six deep learning models utilized non-explainable features. The highest accuracy achieved by the machine learning models was 0.73 and 0.69 for the Korean and English speech datasets, respectively. The deep learning models' maximum achievable accuracy reached 0.75 and 0.78, with their minimum classification time of 0.01s and 0.02s. These findings reveal the models' robustness regardless of Korean and English and real-time diagnosis of AD through a 30-s voice sample. Non-explainable deep learning models that directly acquire voice representations surpassed machine learning models utilizing hand-crafted features in AD diagnosis. In addition, these AI models could confirm the possibility of extending to a language-agnostic AD diagnosis.

For high-dimensional data such as images, learning an encoder that can output a compact yet informative representation is a key task on its own, in addition to facilitating subsequent processing of data. We present a model that produces discrete infomax codes (DIMCO); we train a probabilistic encoder that yields k-way d-dimensional codes associated with input data. Our model maximizes the mutual information between codes and ground-truth class labels, with a regularization which encourages entries of a codeword to be statistically independent. In this context, we show that the infomax principle also justifies existing loss functions, such as cross-entropy as its special cases. Our analysis also shows that using shorter codes reduces overfitting in the context of few-shot classification, and our various experiments show this implicit task-level regularization effect of DIMCO. Furthermore, we show that the codes learned by DIMCO are efficient in terms of both memory and retrieval time compared to prior methods.

This paper addresses local stability analysis for the flexible independent component analysis (ICA) algorithm where the generalized Gaussian density model was employed for blind separation of mixtures of sub- and super-Gaussian sources. In the flexible ICA algorithm, the shape of nonlinear function in the learning algorithm varies depending on the Gaussian exponent which is properly selected according to the kurtosis of estimated source. In the framework of the natural gradient in Stiefel manifold, the flexible ICA algorithm is revisited and some new results about its local stability analysis are presented.