The remarkable performance of deep Convolutional neural networks (CNNs) is generally
attributed to their deeper and wider architectures, which can come with significant …

Uniform manifold approximation and projection is a nonlinear dimension reduction method
often used for visualizing data and as pre-processing for further machine-learning tasks …

Computer vision models suffer from a phenomenon known as catastrophic forgetting when
learning novel concepts from continuously shifting training data. Typical solutions for this …

Bayesian Optimization (BO), the application of Bayesian function approximation to finding
optima of expensive functions, has exploded in popularity in recent years. In particular, much …

As the big data generated by the development of modern experiments and computing
technology becomes more and more accessible, the material design method based on …

The evolutionary algorithms (EAs) have been shown favorable performance for feature
selection. However, a large number of evaluations are required through the EAs. Thus, they …

Recently, Optimal Transport has been proposed as a probabilistic framework in Machine
Learning for comparing and manipulating probability distributions. This is rooted in its rich …

In recent years, feature selection has emerged as a major challenge in machine learning. In
this paper, considering the promising performance of metaheuristics on different types of …

The rapid expansion of global cloud wide-area networks (WANs) has posed a challenge for
commercial optimization engines to efficiently solve network traffic engineering (TE) …

Mechanistic models have been used for centuries to describe complex interconnected
processes, including biological ones. As the scope of these models has widened, so have …