Recent advances in Information Retrieval utilise energy-intensive hardware to produce state-
of-the-art results. In areas of research highly related to Information Retrieval, such as Natural …

The increasing energy consumption and carbon footprint of deep learning (DL) due to
growing compute requirements has become a cause of concern. In this work, we focus on …

State-of-the-art machine learning (ML) systems show exceptional qualitative performance,
but can also have a negative impact on society. With regard to global climate change, the …

With the rapid increase in the research, development, and application of neural networks in
the current era, there is a proportional increase in the energy needed to train and use …

Since the creation of the Web, recommender systems (RSs) have been an indispensable
mechanism in information filtering. State-of-the-art RSs primarily depend on categorical …

This study presents a model for predicting photovoltaic power generation based on
meteorological, temporal and geographical variables, without using irradiance values, which …

The massive use of machine learning models, particularly neural networks, has raised
serious concerns about their environmental impact. Indeed, over the last few years we have …

Abstract Artificial Intelligence (AI), the Internet of Things (IoT) and digitization are very
influential topics in current times, changing many areas in which they are applied. The …

Abstract ConformalLayers are sequential Convolutional Neural Networks (CNNs) that use
activation functions defined as geometric operations in the conformal model for Euclidean …

Automated machine learning (AutoML) streamlines the creation of ML models, but few
specialized methods have approached the challenging domain of time series forecasting …