86 citations · 112 across the 10 of their papers we have counts for
5 papers · 1 filter
Semantic-Inductive Attribute Selection for Zero-Shot Learning
Juan Jose Herrera-Aranda, Guillermo Gomez-Trenado, Francisco Herrera +1
Zero-Shot Learning is an important paradigm within General-Purpose Artificial Intelligence Systems, particularly in those that operate in open-world scenarios where systems must ad…
The Energy Prediction Smart-Meter Dataset: Analysis of Previous Competitions and Beyond
Direnc Pekaslan, Jose Maria Alonso-Moral, Kasun Bandara +17
This paper presents the real-world smart-meter dataset and offers an analysis of solutions derived from the Energy Prediction Technical Challenges, focusing primarily on two key co…
AutoEn: An AutoML method based on ensembles of predefined Machine Learning pipelines for supervised Traffic Forecasting
Juan S. Angarita-Zapata, Antonio D. Masegosa, Isaac Triguero
Intelligent Transportation Systems are producing tons of hardly manageable traffic data, which motivates the use of Machine Learning (ML) for data-driven applications, such as Traf…
Local-Global Methods for Generalised Solar Irradiance Forecasting
Timothy Cargan, Dario Landa-Silva, Isaac Triguero
As the use of solar power increases, having accurate and timely forecasts will be essential for smooth grid operators. There are many proposed methods for forecasting solar irradia…
L2AE-D: Learning to Aggregate Embeddings for Few-shot Learning with Meta-level Dropout
Heda Song, Mercedes Torres Torres, Ender Özcan +1
Few-shot learning focuses on learning a new visual concept with very limited labelled examples. A successful approach to tackle this problem is to compare the similarity between ex…