Monica Bianchini

Franco Scarselli, Marco Gori, Monica Bianchini, 2000, KES'2000. Fourth International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies. Proceedings (Cat. No.00TH8516).

Innovations in Neural Information Paradigms and Applications

Lakhmi C. Jain, Franco Scarselli, Marco Maggini, 2009, Innovations in Neural Information Paradigms and Applications.

ABLE: An Automated Bacterial Load Estimator for the Urinoculture Screening

Andrea Garzelli, Alessandro Mecocci, Monica Bianchini, 2016, ICPRAM.

The Decoupling Network Assumptions for Optimal Learning in Recurrent Neural Networks

Marco Gori, Marco Maggini, Monica Bianchini, 1994 .

Face Spotting in Color Images using Recursive Neural Networks

Franco Scarselli, Monica Bianchini, Lorenzo Sarti, 2003 .

Multi-Modal Siamese Network for Diagnostically Similar Lesion Retrieval in Prostate MRI

Franco Scarselli, Monica Bianchini, Alberto Rossi, 2020, IEEE Transactions on Medical Imaging.

Theoretical properties of recursive neural networks with linear neurons

Marco Gori, Monica Bianchini, M. Bianchini, 2001, IEEE Trans. Neural Networks.

A Comparative Study of Inductive and Transductive Learning with Feedforward Neural Networks

Franco Scarselli, Monica Bianchini, Anas Belahcen, 2016, AI*IA.

Recursive Neural Networks and Their Applications to Image Processing

Marco Maggini, Monica Bianchini, Lorenzo Sarti, 2006 .

Fusion of Visual and Anamnestic Data for the Classification of Skin Lesions with Deep Learning

Monica Bianchini, Alberto Rossi, Paolo Andreini, 2019, ICIAP Workshops.

Handbook on Neural Information Processing

Lakhmi C. Jain, Marco Maggini, Monica Bianchini, 2013, Handbook on Neural Information Processing.

Learning in multilayered networks used as autoassociators

Paolo Frasconi, Marco Gori, Monica Bianchini, 1995, IEEE Trans. Neural Networks.

Processing directed acyclic graphs with recursive neural networks

Franco Scarselli, Marco Gori, Monica Bianchini, 2001, IEEE Trans. Neural Networks.

Web Spam Detection Using Transductive(Inductive Graph Neural Networks

Franco Scarselli, Monica Bianchini, Anas Belahcen, 2015, Advances in Neural Networks.

Solving Linear Systems by a Neural Network Canonical Form of Efficient Gradient Descent

Marco Protasi, Marco Gori, Monica Bianchini, 1997, ICONIP.

Optimal learning in artificial neural networks: A review of theoretical results

Marco Gori, Monica Bianchini, M. Bianchini, 1996, Neurocomputing.

Recursive Neural Networks and Graphs: Dealing with Cycles

Franco Scarselli, Marco Gori, Monica Bianchini, 2005, WIRN/NAIS.

Inductive-Transductive Learning with Graph Neural Networks

Franco Scarselli, Giovanna Maria Dimitri, Marco Maggini, 2018, ANNPR.

Editorial: RNA-Seq Analysis: Methods, Applications and Challenges

Indrajit Saha, Monica Bianchini, Filippo Geraci, 2020, Frontiers in Genetics.

Does Terminal Attractor Guarantee Global Convergence

Marco Gori, Marco Maggini, Monica Bianchini, 1994 .

Recursive Networks: An Overview of Theoretical Results

Franco Scarselli, Marco Gori, Monica Bianchini, 1999 .

Optimal Learning and the Class of Unimodal Error Surface Loading Problems

Marco Gori, Monica Bianchini, S. Fanelli, 1995 .

Generating Bounding Box Supervision for Semantic Segmentation with Deep Learning

Franco Scarselli, Monica Bianchini, Paolo Andreini, 2018, ANNPR.

Recursive networks for processing graphs with labelled edges

Franco Scarselli, Marco Maggini, Monica Bianchini, 2004, The European Symposium on Artificial Neural Networks.

Deep Neural Networks for Structured Data

Franco Scarselli, Giovanna Maria Dimitri, Marco Maggini, 2018 .

Recursive processing of cyclic graphs

Franco Scarselli, Marco Gori, Monica Bianchini, 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

Recursive neural networks learn to localize faces

Franco Scarselli, Marco Maggini, Monica Bianchini, 2005, Pattern Recognit. Lett..

AKUImg: A database of cartilage images of Alkaptonuria patients

Monica Bianchini, Andrea Bernini, Silvia Galderisi, 2020, Comput. Biol. Medicine.

COCO_TS Dataset: Pixel-level Annotations Based on Weak Supervision for Scene Text Segmentation

Franco Scarselli, Monica Bianchini, Paolo Andreini, 2019, ICANN.

Graph Neural Networks for the Prediction of Protein-Protein Interfaces

Franco Scarselli, Monica Bianchini, Alberto Rossi, 2020, ESANN.

A unifying point of view on expressive power of GNNs pdf

Franco Scarselli, Monica Bianchini, Maria Lucia Sampoli, 2021, ArXiv.

Segmentation of Aorta 3D CT Images Based on 2D Convolutional Neural Networks

Franco Scarselli, Alessandro Mecocci, Giovanna Maria Dimitri, 2021, Electronics.

A multi-stage GAN for multi-organ chest X-ray image generation and segmentation pdf

Franco Scarselli, Monica Bianchini, Paolo Andreini, 2021, Mathematics.

Smart gravimetric system based on Deep Learning for enhanced safety of accesses to public places

Franco Scarselli, Tommaso Addabbo, Ada Fort, 2021, 2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC).

Complex Data: Learning Trustworthily, Automatically, and with Guarantees

Alessio Micheli, Franco Scarselli, Alessandro Sperduti, 2021, ESANN.

Molecular generative Graph Neural Networks for Drug Discovery

Franco Scarselli, Monica Bianchini, Pietro Bongini, 2020, Neurocomputing.

Prediction of Traffic Movement for Autonomous Vehicles

Monica Bianchini, Rabindra Nath Shaw, Ankush Ghosh, 2021, Machine Learning for Robotics Applications.

Does Terminal Attractor Backpropagation Guarantee Global Optimization

Marco Gori, Marco Maggini, Monica Bianchini, 1994 .

Non-suspiciousness: a generalisation of convexity in the frame of foundations of numerical analysis and learning

Marco Gori, Monica Bianchini, S. Fanelli, 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).

Weak Supervision for Generating Pixel-Level Annotations in Scene Text Segmentation pdf

Franco Scarselli, Monica Bianchini, Paolo Andreini, 2020, Pattern Recognit. Lett..