Demo Data from EDBT 2026 / reviewers in the wild / expert
Angelo Di Garbo
dblp:55/6329
· DBLP profile ↗
7ranked-venue papers
4as first author
1since 2021 · last 2026
0009-0000-9065-5163ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 6 · 3 first-author · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Deep learning architectures and training · 50% Image recognition and object detection · 50% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Deep learning architectures and training › recurrent neural network
reservoir computing |
1.0 | 1 | 2026 | Neuro-Inspired Visual Pattern Recognition via Biological Reservoir Computing · Int. J. Comput. Vis. 2026 |
Computer vision › Image recognition and object detection › object recognition
visual pattern recognition |
1.0 | 1 | 2026 | Neuro-Inspired Visual Pattern Recognition via Biological Reservoir Computing · Int. J. Comput. Vis. 2026 |
Methods — techniques the papers use, named apart from their topics
reservoir computing · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Neuro-Inspired Visual Pattern Recognition via Biological Reservoir Computing
Luca Ciampi, Ludovico Iannello, Fabrizio Tonelli, Gabriele Lagani, Angelo Di Garbo, Federico Cremisi, Giuseppe Amato 0001 |
Int. J. Comput. Vis. | 5 |
| 2015 | Thalamic inputs modulate cortical activity: Possibility to control the generation and the termination of seizure-like behaviour
Fabio Vallone, Andrea Cintio, Santi Chillemi, Angelo Di Garbo |
Neurocomputing | 4 |
| 2013 | The role of glutamatercic and GABAergic synapses on the dynamics of neural networks: How they impact the transition to seizure?
Angelo Di Garbo, Michele Barbi, Santi Chillemi |
Neurocomputing | 1 |
| 2011 | A network of pyramidal neurons is sensitive to the timing of its excitatory inputs
Santi Chillemi, Michele Barbi, Angelo Di Garbo |
Neurocomputing | 3 |
| 2007 | The kinetics of the IPSC, the heterogeneity and the noise affect the firing coherence of a population of inhibitory interneurons
Angelo Di Garbo, Alessandro Panarese, Michele Barbi, Santi Chillemi |
Neurocomputing | 1 |
| 2004 | Phase locking states between fast-spiking interneurons coupled by electrical and chemical synapses
Angelo Di Garbo, Alessandro Panarese, Santi Chillemi |
Neurocomputing | 1 |
| 1997 | For Nonlinearity in the Heartbeat Time SeriesabstractHearbeat time series obtained by Holter monitoring of 10 healthy subjects are studied using a nonlinear predictor S map . The normalized prediction error was calculated as a function of the model control parameter in order to get information on the amount of nonlinearity in the data. Moreover, to search for possible chaotic behavior, the linear correlation coefficient between predicted and real values was calculated as a function of the prediction time. The results of this analysis reveal no clear evidence of chaoticness or nonlinearity in the data. Moreover, for 8 subjects out of 10, the predictability during sleep is better than during the daytime. Angelo Di Garbo, Michele Barbi, Santi Chillemi, Rita Balocchi, Claudio Michelassi, Clara Carpeggiani |
Cybern. Syst. | 1 |