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Found 5 papers, 2 papers with code
A singular Riemannian Geometry Approach to Deep Neural Networks III. Piecewise Differentiable Layers and Random Walks on $n$-dimensional Classes
Neural networks are playing a crucial role in everyday life, with the most modern generative models able to achieve impressive results.
Majorization-Minimization for sparse SVMs
Several decades ago, Support Vector Machines (SVMs) were introduced for performing binary classification tasks, under a supervised framework.
Unsupervised deep learning techniques for powdery mildew recognition based on multispectral imaging
We focus on unsupervised deep learning techniques applied to multispectral imaging data and we propose the use of autoencoder architectures to investigate two strategies for disease detection: i) clusterization of features in a compressed space; ii) anomaly detection.
A singular Riemannian geometry approach to Deep Neural Networks II. Reconstruction of 1-D equivalence classes
In this paper, we present an application of this framework, proposing a way to build the class of equivalence of an input point: such class is defined as the set of the points on the input manifold mapped to the same output by the neural network.
A singular Riemannian geometry approach to Deep Neural Networks I. Theoretical foundations
Motivated by some open problems, we study a particular sequence of maps between manifolds, with the last manifold of the sequence equipped with a Riemannian metric.
