The problem of determining the proper size of an artificial neural network is recognized to be crucial, especially for its practical implications in such important issues as learning and generalization. One popular approach for tackling this problem is commonly known as pruning and it consists of training a larger than necessary network and then removing unnecessary weights/nodes. In this paper, a new pruning method is developed, based on the idea of iteratively eliminating units and adjusting the remaining weights in such a way that the network performance does not worsen over the entire training set. The pruning problem is formulated in terms of solving a system of linear equations, and a very efficient conjugate gradient algorithm is used for solving it, in the least-squares sense. The algorithm also provides a simple criterion for choosing the units to be removed, which has proved to work well in practice. The results obtained over various test problems demonstrate the effectiveness of the proposed approach.

Recent advances in deep learning and imaging technologies have revolutionized automated medical image analysis, especially in diagnosing Alzheimer’s disease through neuroimaging. Despite the availability of various imaging modalities for the same patient, the development of multi-modal models leveraging these modalities remains underexplored. This paper addresses this gap by proposing and evaluating classification models using 2D and 3D MRI images and amyloid PET scans in uni-modal and multi-modal frameworks. Our findings demonstrate that models using volumetric data learn more effective representations than those using only 2D images. Furthermore, integrating multiple modalities enhances model performance over single-modality approaches significantly. We achieved state-of-the-art performance on the OASIS-3 cohort. Additionally, explainability analyses with Grad-CAM indicate that our model focuses on crucial AD-related regions for its predictions, underscoring its potential to aid in understanding the disease’s causes.

This paper provides an overview of some of the most relevant deep learning approaches to pattern extraction and recognition in visual arts, particularly painting and drawing. Recent advances in deep learning and computer vision, coupled with the growing availability of large digitized visual art collections, have opened new opportunities for computer science researchers to assist the art community with automatic tools to analyse and further understand visual arts. Among other benefits, a deeper understanding of visual arts has the potential to make them more accessible to a wider population, ultimately supporting the spread of culture.

The predictive business process monitoring is a family of online approaches to predict the unfolding of running traces basedon the knowledge learned from historical event logs. In this paper, we address the task of predicting the next trace activity from the completed events in a running trace. This is an important business capability as counting on accurate predictions of the future activities may allow companies to guarantee the higher utilization by acting proactively in anticipation. We propose a novel predictive process approach that couples multi-view learning and deep learning, in order to gain predictive accuracy by accounting for the variety ofinformation possibly recorded in event logs. Experiments with various benchmark event logs prove the effectiveness of the proposed approach compared to several recent state-of-the-art methods.