b'Marco Gori'

research

∙ 09/13/2023

Collectionless Artificial Intelligence

By and large, the professional handling of huge data collections is rega...

0 Marco Gori, et al. ∙

research

∙ 06/05/2023

Continual Learning with Pretrained Backbones by Tuning in the Input Space

The intrinsic difficulty in adapting deep learning models to non-station...

0 Simone Marullo, et al. ∙

research

∙ 10/17/2022

PARTIME: Scalable and Parallel Processing Over Time with Deep Neural Networks

In this paper, we present PARTIME, a software library written in Python ...

16 Enrico Meloni, et al. ∙

research

∙ 08/08/2022

Learning to Identify Drilling Defects in Turbine Blades with Single Stage Detectors

Nondestructive testing (NDT) is widely applied to defect identification ...

0 Andrea Panizza, et al. ∙

research

∙ 06/30/2022

Deep Learning to See: Towards New Foundations of Computer Vision

The remarkable progress in computer vision over the last few years is, b...

0 Alessandro Betti, et al. ∙

research

∙ 12/18/2021

Being Friends Instead of Adversaries: Deep Networks Learn from Data Simplified by Other Networks

Amongst a variety of approaches aimed at making the learning procedure o...

5 Simone Marullo, et al. ∙

research

∙ 10/15/2021

Knowledge-driven Active Learning

In the last few years, Deep Learning models have become increasingly pop...

0 Gabriele Ciravegna, et al. ∙

research

∙ 10/13/2021

Logic Constraints to Feature Importances

In recent years, Artificial Intelligence (AI) algorithms have been prove...

0 Nicola Picchiotti, et al. ∙

research

∙ 10/13/2021

Clustering-Based Interpretation of Deep ReLU Network

Amongst others, the adoption of Rectified Linear Units (ReLUs) is regard...

0 Nicola Picchiotti, et al. ∙

research

∙ 10/12/2021

Can machines learn to see without visual databases?

This paper sustains the position that the time has come for thinking of ...

0 Alessandro Betti, et al. ∙

research

∙ 09/21/2021

Graph Neural Networks for Graph Drawing

Graph Drawing techniques have been developed in the last few years with ...

5 Matteo Tiezzi, et al. ∙

research

∙ 09/17/2021

Messing Up 3D Virtual Environments: Transferable Adversarial 3D Objects

In the last few years, the scientific community showed a remarkable and ...

9 Enrico Meloni, et al. ∙

research

∙ 08/11/2021

Logic Explained Networks

The large and still increasing popularity of deep learning clashes with ...

12 Gabriele Ciravegna, et al. ∙

research

∙ 06/21/2021

Friendly Training: Neural Networks Can Adapt Data To Make Learning Easier

In the last decade, motivated by the success of Deep Learning, the scien...

16 Simone Marullo, et al. ∙

research

∙ 06/12/2021

Entropy-based Logic Explanations of Neural Networks

Explainable artificial intelligence has rapidly emerged since lawmakers ...

0 Pietro Barbiero, et al. ∙

research

∙ 10/28/2020

An Optimal Control Approach to Learning in SIDARTHE Epidemic model

The COVID-19 outbreak has stimulated the interest in the proposal of nov...

8 Andrea Zugarini, et al. ∙

research

∙ 09/15/2020

Gravitational Models Explain Shifts on Human Visual Attention

Visual attention refers to the human brain's ability to select relevant ...

0 Dario Zanca, et al. ∙

research

∙ 09/01/2020

Developing Constrained Neural Units Over Time

In this paper we present a foundational study on a constrained method th...

0 Alessandro Betti, et al. ∙

research

∙ 08/14/2020

An Overview on the Web of Clinical Data

In the last few years there has been an impressive growth of connections...

0 Marco Gori, et al. ∙

research

∙ 07/16/2020

SAILenv: Learning in Virtual Visual Environments Made Simple

Recently, researchers in Machine Learning algorithms, Computer Vision sc...

10 Enrico Meloni, et al. ∙

research

∙ 06/19/2020

Wave Propagation of Visual Stimuli in Focus of Attention

Fast reactions to changes in the surrounding visual environment require ...

0 Lapo Faggi, et al. ∙

research

∙ 06/16/2020

Focus of Attention Improves Information Transfer in Visual Features

Unsupervised learning from continuous visual streams is a challenging pr...

10 Matteo Tiezzi, et al. ∙

research

∙ 06/06/2020

Can Domain Knowledge Alleviate Adversarial Attacks in Multi-Label Classifiers?

Adversarial attacks on machine learning-based classifiers, along with de...

16 Stefano Melacci, et al. ∙

research

∙ 02/29/2020

Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective

Neural-symbolic computing has now become the subject of interest of both...

69 Luis Lamb, et al. ∙

research

∙ 02/18/2020

Local Propagation in Constraint-based Neural Network

In this paper we study a constraint-based representation of neural netwo...

8 Giuseppe Marra, et al. ∙

research

∙ 02/18/2020

A Lagrangian Approach to Information Propagation in Graph Neural Networks

In many real world applications, data are characterized by a complex str...

11 Matteo Tiezzi, et al. ∙

research

∙ 02/11/2020

Toward Improving the Evaluation of Visual Attention Models: a Crowdsourcing Approach

Human visual attention is a complex phenomenon. A computational modeling...

0 Dario Zanca, et al. ∙

research

∙ 02/06/2020

Relational Neural Machines

Deep learning has been shown to achieve impressive results in several ta...

58 Giuseppe Marra, et al. ∙

research

∙ 12/10/2019

Backprop Diffusion is Biologically Plausible

The Backpropagation algorithm relies on the abstraction of using a neura...

0 Alessandro Betti, et al. ∙

research

∙ 11/21/2019

Discrete and Continuous Deep Residual Learning Over Graphs

In this paper we propose the use of continuous residual modules for grap...

0 Pedro H. C. Avelar, et al. ∙

research

∙ 09/24/2019

Jointly Learning to Detect Emotions and Predict Facebook Reactions

The growing ubiquity of Social Media data offers an attractive perspecti...

0 Lisa Graziani, et al. ∙

research

∙ 09/01/2019

Learning Visual Features Under Motion Invariance

Humans are continuously exposed to a stream of visual data with a natura...

0 Alessandro Betti, et al. ∙

research

∙ 07/26/2019

Learning and T-Norms Theory

Deep learning has been shown to achieve impressive results in several do...

2 Giuseppe Marra, et al. ∙

research

∙ 07/18/2019

On the relation between Loss Functions and T-Norms

Deep learning has been shown to achieve impressive results in several do...

4 Francesco Giannini, et al. ∙

research

∙ 07/14/2019

On the Role of Time in Learning

By and large the process of learning concepts that are embedded in time ...

4 Alessandro Betti, et al. ∙

research

∙ 07/11/2019

Spatiotemporal Local Propagation

This paper proposes an in-depth re-thinking of neural computation that p...

0 Alessandro Betti, et al. ∙

research

∙ 07/04/2019

Least Action Principles and Well-Posed Learning Problems

Machine Learning algorithms are typically regarded as appropriate optimi...

0 Alessandro Betti, et al. ∙

research

∙ 05/15/2019

Neural-Symbolic Computing: An Effective Methodology for Principled Integration of Machine Learning and Reasoning

Current advances in Artificial Intelligence and machine learning in gene...

3 Artur d'Avila Garcez, et al. ∙

research

∙ 03/18/2019

LYRICS: a General Interface Layer to Integrate AI and Deep Learning

In spite of the amazing results obtained by deep learning in many applic...

0 Giuseppe Marra, et al. ∙

research

∙ 01/23/2019

Typed Graph Networks

Recently, the deep learning community has given growing attention to neu...

0 Pedro Henrique da Costa Avelar, et al. ∙

research

∙ 01/14/2019

Integrating Learning and Reasoning with Deep Logic Models

Deep learning is very effective at jointly learning feature representati...

0 Giuseppe Marra, et al. ∙

research

∙ 10/17/2018

Coherence Constraints in Facial Expression Recognition

Recognizing facial expressions from static images or video sequences is ...

0 Lisa Graziani, et al. ∙

research

∙ 09/18/2018

On a Convex Logic Fragment for Learning and Reasoning

In this paper we introduce the convex fragment of Łukasiewicz Logic and ...

0 Francesco Giannini, et al. ∙

research

∙ 08/28/2018

Cognitive Action Laws: The Case of Visual Features

This paper proposes a theory for understanding perceptual learning proce...

14 Alessandro Betti, et al. ∙

research

∙ 08/21/2018

Backpropagation and Biological Plausibility

By and large, Backpropagation (BP) is regarded as one of the most import...

0 Alessandro Betti, et al. ∙

research

∙ 07/17/2018

Learning Neuron Non-Linearities with Kernel-Based Deep Neural Networks

The effectiveness of deep neural architectures has been widely supported...

0 Giuseppe Marra, et al. ∙

research

∙ 07/16/2018

Constraint-Based Visual Generation

In the last few years the systematic adoption of deep learning to visual...

0 Giuseppe Marra, et al. ∙

research

∙ 07/14/2018

Motion Invariance in Visual Environments

The puzzle of computer vision might find new challenging solutions when ...

0 Alessandro Betti, et al. ∙

research

∙ 07/14/2018

Generalization in quasi-periodic environments

By and large the behavior of stochastic gradient is regarded as a challe...

0 Giovanni Bellettini, et al. ∙

research

∙ 07/11/2018

Trees in the Real Field

This paper proposes an algebraic view of trees which opens the doors to ...

0 Alessandro Betti, et al. ∙

Marco Gori

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