A rigorous and fair evaluation of Deep Graph Networks
Python implementation by F. Errica, M. Podda
Related publications:
F. Errica,
M. Podda,
D. Bacciu,
and
A. Micheli
A Fair Comparison of Graph Neural Networks for Graph Classification
Proceedings of the 8th International Conference on Learning Representations (ICLR),
2020
A Deep Generative Model for Fragment-Based Molecule Generation
Python implementation by M. Podda
Related publications:
M. Podda,
D. Bacciu,
and
A. Micheli
A Deep Generative Model for Fragment-Based Molecule Generation
Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics (AISTATS),
PMLR,
2020
Python for Deep Graph Networks
Python implementation by F. Errica and M. Podda
Related publications:
D. Bacciu,
F. Errica,
A. Micheli,
and
M. Podda
A Gentle Introduction to Deep Learning for Graphs
Neural Networks,
129,
203-221,
2020
Python implementation by D. Di Sarli
Related publications:
D. Di Sarli,
C. Gallicchio,
and
A. Micheli
Question Classification with Untrained Recurrent Embeddings
International Conference of the Italian Association for Artificial Intelligence,
2019
Contextual Graph Markov Model
Python implementation by F. Errica
Related publications:
D. Bacciu,
F. Errica,
and
A. Micheli
Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing
Proceedings of the 35th International Conference on Machine Learning (ICML),
2018,
ISBN 9781510867963
Deep Echo State Network
Python implementation by L. Pedrelli
Related publications:
C. Gallicchio,
A. Micheli,
and
L. Pedrelli
Design of deep echo state networks
Neural Networks,
108,
33-47,
2018
Preterm Infants Survival Assessment (PISA) predictor
Python implementation by M. Podda
Related publications:
M. Podda,
D. Bacciu,
A. Micheli,
R. Bellù,
G. Placidi,
and
L. Gagliardi
A machine learning approach to estimating preterm infants survival: development of the Preterm Infants Survival Assessment (PISA) predictor
Scientific Reports,
8,
2018
Deep Echo State Network
Matlab implementation by C. Gallicchio
Related publications:
C. Gallicchio,
A. Micheli,
and
L. Pedrelli
Deep Reservoir Computing: A Critical Experimental Analysis
Neurocomputing,
268,
87-99,
2017
The dataset contains information on the balance abilities of elderly people, collected during a measurement campaign on 21 volunteer subjects.
Data Type: Sequences
Related publications:
D. Bacciu,
S. Chessa,
C. Gallicchio,
A. Micheli,
L. Pedrelli,
E. Ferro,
L. Fortunati,
D. L. Rosa,
F. Palumbo,
F. Vozzi,
and
O. Parodi
A learning system for automatic Berg Balance Scale score estimation
Engineering Applications of Artificial Intelligence,
66,
60-74,
2017
User Movement Prediction from RSS data Data Set
Data Type: Sequences
Related publications:
F. Palumbo,
C. Gallicchio,
R. Pucci,
and
A. Micheli
Human Activity Recognition using Multisensor Data Fusion based on Reservoir Computing
Ambient Intelligence and Smart Environments,
8,
87-107,
2016
F. Palumbo,
P. Barsocchi,
C. Gallicchio,
S. Chessa,
and
A. Micheli
Multisensor Data Fusion for Activity Recognition Based on Reservoir ComputingEvaluating AAL Systems Through Competitive Benchmarking
COMMUNICATIONS IN COMPUTER AND INFORMATION SCIENCE,
386,
24-35,
2013
A benchmark dataset for human activity recognition and ambient assisted living
Data Type: Sequences
Related publications:
G. Amato,
D. Bacciu,
S. Chessa,
M. Dragone,
C. Gallicchio,
C. Gennaro,
H. Lozano-Peiteado,
A. Micheli,
G. M. P. O'Hare,
A. Renteria,
and
C. F. Vairo
A benchmark dataset for human activity recognition and ambient assisted living
Ambient Intelligence- Software and Applications – 7th International Symposium on Ambient Intelligence (ISAmI 2016),
Springer Verlag,
2016,
ISBN 978-3-319-40114-0
A small piece of Matlab software used to generate artificial trees with continuous emissions.
Data Type: Trees
Related publications:
D. Bacciu,
A. Micheli,
and
A. Sperduti
Compositional Generative Mapping for Tree-Structured Data - Part II: Topographic Projection Model
IEEE Transactions on Neural Networks and Learning Systems,
24,
231-247,
2013
D. Bacciu,
A. Micheli,
and
A. Sperduti
Compositional Generative Mapping for Tree-Structured Data - Part I: Bottom-Up Probabilistic Modeling of Trees.
IEEE Transactions on Neural Networks and Learning Systems,
23,
1987-2002,
2012
D. Bacciu,
A. Micheli,
and
A. Sperduti
Compositional Generative Mapping of Structured Data
Proceedings of the International Joint Conference on Neural Networks (IJCNN '10),
IEEE,
2010,
ISBN 9781424481262
Activity Recognition system based on Multisensor data fusion
Data Type: Sequences
Related publications:
F. Palumbo,
P. Barsocchi,
C. Gallicchio,
S. Chessa,
and
A. Micheli
Multisensor Data Fusion for Activity Recognition Based on Reservoir ComputingEvaluating AAL Systems Through Competitive Benchmarking
COMMUNICATIONS IN COMPUTER AND INFORMATION SCIENCE,
386,
24-35,
2013
A collection of alkanes with their structures and the boiling point temperatures.
Data Type: Trees
Related publications:
A. Micheli
Neural Network for Graphs: A Contextual Constructive Approach
IEEE Transactions on Neural Networks,
20,
498-511,
2009
A. M. Bianucci,
A. Micheli,
A. Sperduti,
and
A. Starita
Application of Cascade Correlation Networks for Structures to Chemistry
Applied Intelligence,
12,
117-146,
2000
Alessio Micheli
Tel: +39 050 2212798
Email: micheli@di.unipi.it
Dipartimento di Informatica
Largo B. Pontecorvo, 3
56127 Pisa, Italy