100 citations · 101 across the 7 of their papers we have counts for
9 papers
DL101 Neural Network Outputs and Loss Functions
Fernando Berzal
The loss function used to train a neural network is strongly connected to its output layer from a statistical point of view. This technical report analyzes common activation functi…
Differential Privacy Regularization: Protecting Training Data Through Loss Function Regularization
Francisco Aguilera-Martínez, Fernando Berzal
Training machine learning models based on neural networks requires large datasets, which may contain sensitive information. The models, however, should not expose private informati…
Enhancing Community Detection in Networks: A Comparative Analysis of Local Metrics and Hierarchical Algorithms
Julio-Omar Palacio-Niño, Fernando Berzal
The analysis and detection of communities in network structures are becoming increasingly relevant for understanding social behavior. One of the principal challenges in this field…
Beyond Trend Following: Deep Learning for Market Trend Prediction
Fernando Berzal, Alberto Garcia
Trend following and momentum investing are common strategies employed by asset managers. Even though they can be helpful in the proper situations, they are limited in the sense tha…
On the use of local structural properties for improving the efficiency of hierarchical community detection methods
Julio-Omar Palacio-Niño, Fernando Berzal
Community detection is a fundamental problem in the analysis of complex networks. It is the analogue of clustering in network data mining. Within community detection methods, hiera…
Evaluation Metrics for Unsupervised Learning Algorithms
Julio-Omar Palacio-Niño, Fernando Berzal
Determining the quality of the results obtained by clustering techniques is a key issue in unsupervised machine learning. Many authors have discussed the desirable features of good…