Search Results for author:
Found 9 papers, 5 papers with code
Application of the representative measure approach to assess the reliability of decision trees in dealing with unseen vehicle collision data
Machine learning algorithms are fundamental components of novel data-informed Artificial Intelligence architecture.
An In-Depth Analysis of Data Reduction Methods for Sustainable Deep Learning
Finally, we experimentally compare how these data reduction methods affect the representativeness of the reduced dataset, the energy consumption and the predictive performance of the model.
SIMAP: A simplicial-map layer for neural networks
In this paper, we present SIMAP, a novel layer integrated into deep learning models, aimed at enhancing the interpretability of the output.
A Topological Approach to Measuring Training Data Quality
Data quality is crucial for the successful training, generalization and performance of artificial intelligence models.
Trainable and Explainable Simplicial Map Neural Networks
In this paper, we overcome these issues by proposing an SMNN training procedure based on a support subset of the given dataset and replacing the construction of the convex polytope by a method based on projections to a hypersphere.
Emotion recognition in talking-face videos using persistent entropy and neural networks
The automatic recognition of a person's emotional state has become a very active research field that involves scientists specialized in different areas such as artificial intelligence, computer vision or psychology, among others.
Summary and Distance between Sets of Texts based on Topological Data Analysis
In this paper, we use topological data analysis (TDA) tools such as persistent homology, persistent entropy and bottleneck distance, to provide a {\it TDA-based summary} of any given set of texts and a general method for computing a distance between any two literary styles, authors or periods.
Two-hidden-layer Feedforward Neural Networks are Universal Approximators: A Constructive Approach
This approach is based on an approximation of continuous functions by simplicial maps.
Topology-based Representative Datasets to Reduce Neural Network Training Resources
We prove that the accuracy of the learning process of a neural network on a representative dataset is "similar" to the accuracy on the original dataset when the neural network architecture is a perceptron and the loss function is the mean squared error.
