In machine learning, the model is not as complicated as possible. Good generalization
ability means that the model not only performs well on the training data set, but also can …

Bayesian optimization has emerged at the forefront of expensive black-box optimization due
to its data efficiency. Recent years have witnessed a proliferation of studies on the …

The imputation of missing values in time series has many applications in healthcare and
finance. While autoregressive models are natural candidates for time series imputation …

Large-scale pre-trained models (PTMs) such as BERT and GPT have recently achieved
great success and become a milestone in the field of artificial intelligence (AI). Owing to …

Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides
opportunities for the discovery and development of innovative drugs. Various machine …

Despite advances in scalable models, the inference tools used for Gaussian processes
(GPs) have yet to fully capitalize on developments in computing hardware. We present an …

The vast quantity of information brought by big data as well as the evolving computer
hardware encourages success stories in the machine learning community. In the …

Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to
leverage useful information contained in multiple related tasks to help improve the …

Functionalization of C–H bonds is a key challenge in medicinal chemistry, particularly for
fragment-based drug discovery (FBDD) where such transformations require execution in the …

As a promising area in machine learning, multi-task learning (MTL) aims to improve the
performance of multiple related learning tasks by leveraging useful information among them …