18 citations · 35 across the 14 of their papers we have counts for
6 papers · 1 filter
Mixing ADAM and SGD: a Combined Optimization Method
Nicola Landro, Ignazio Gallo, Riccardo La Grassa
Optimization methods (optimizers) get special attention for the efficient training of neural networks in the field of deep learning. In literature there are many papers that compar…
Can a powerful neural network be a teacher for a weaker neural network?
Nicola Landro, Ignazio Gallo, Riccardo La Grassa
The transfer learning technique is widely used to learning in one context and applying it to another, i.e. the capacity to apply acquired knowledge and skills to new situations. Bu…
Dynamic Decision Boundary for One-class Classifiers applied to non-uniformly Sampled Data
Riccardo La Grassa, Ignazio Gallo, Nicola Landro
A typical issue in Pattern Recognition is the non-uniformly sampled data, which modifies the general performance and capability of machine learning algorithms to make accurate pred…
OCmst: One-class Novelty Detection using Convolutional Neural Network and Minimum Spanning Trees
Riccardo La Grassa, Ignazio Gallo, Nicola Landro
We present a novel model called One Class Minimum Spanning Tree (OCmst) for novelty detection problem that uses a Convolutional Neural Network (CNN) as deep feature extractor and g…
A Classification Methodology based on Subspace Graphs Learning
Riccardo La Grassa, Ignazio Gallo, Alessandro Calefati +1
In this paper, we propose a design methodology for one-class classifiers using an ensemble-of-classifiers approach. The objective is to select the best structures created during th…
Binary Classification using Pairs of Minimum Spanning Trees or N-ary Trees
Riccardo La Grassa, Ignazio Gallo, Alessandro Calefati +1
One-class classifiers are trained with target class only samples. Intuitively, their conservative modelling of the class description may benefit classical classification tasks wher…