activity
20182020
most citedMixing ADAM and SGD: a Combined Optimization Method

18 citations · 18 across the 6 of their papers we have counts for

collaborators

18 papers

cs.LG202018 cited

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…

cs.CV2020

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…

cs.CV2020

Learn Class Hierarchy using Convolutional Neural Networks

Riccardo La Grassa, Ignazio Gallo, Nicola Landro

A large amount of research on Convolutional Neural Networks has focused on flat Classification in the multi-class domain. In the real world, many problems are naturally expressed a…

cs.LG2020

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…

cs.LG2020

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…

cs.CV2020

Cross-modal Speaker Verification and Recognition: A Multilingual Perspective

Muhammad Saad Saeed, Shah Nawaz, Pietro Morerio +4

Recent years have seen a surge in finding association between faces and voices within a cross-modal biometric application along with speaker recognition. Inspired from this, we int…