18 citations · 18 across the 3 of their papers we have counts for
3 papers
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…
R Loss: a Weighted Loss by Multiplicative Factors using Sigmoidal Functions
Riccardo La Grassa, Ignazio Gallo, Nicola Landro
In neural networks, the loss function represents the core of the learning process that leads the optimizer to an approximation of the optimal convergence error. Convolutional neura…
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…