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Found 7 papers, 2 papers with code
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.
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.
Dynamic Price of Parking Service based on Deep Learning
Major efforts from government bodies in this area include monitoring and forecasting systems, banning more pollutant motor vehicles, and traffic limitations during the periods of low-quality air.
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.
Logic Negation with Spiking Neural P Systems
Nowadays, the success of neural networks as reasoning systems is doubtless.
