Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Deep reservoir computing: A critical experimental analysis
2017322 citationsClaudio Gallicchio, Alessio Micheli et al.Neurocomputingprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
Countries citing papers authored by Claudio Gallicchio
Since
Specialization
Citations
This map shows the geographic impact of Claudio Gallicchio's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Claudio Gallicchio with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Claudio Gallicchio more than expected).
Fields of papers citing papers by Claudio Gallicchio
This network shows the impact of papers produced by Claudio Gallicchio. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Claudio Gallicchio. The network helps show where Claudio Gallicchio may publish in the future.
Co-authorship network of co-authors of Claudio Gallicchio
This figure shows the co-authorship network connecting the top 25 collaborators of Claudio Gallicchio.
A scholar is included among the top collaborators of Claudio Gallicchio based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Claudio Gallicchio. Claudio Gallicchio is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Gallicchio, Claudio & Alessio Micheli. (2020). Ring Reservoir Neural Networks for Graphs. CINECA IRIS Institutial research information system (University of Pisa).8 indexed citations
Bianchi, Filippo Maria, Claudio Gallicchio, & Alessio Micheli. (2018). Pyramidal Graph Echo State Networks. CINECA IRIS Institutial research information system (University of Pisa). 573–578.1 indexed citations
11.
Gallicchio, Claudio, Alessio Micheli, & Peter Tiňo. (2018). Randomized Recurrent Neural Networks. CINECA IRIS Institutial research information system (University of Pisa). 415–424.4 indexed citations
12.
Gallicchio, Claudio, Alessio Micheli, & Luca Silvestri. (2017). Local Lyapunov Exponents of Deep RNN. CINECA IRIS Institutial research information system (University of Pisa). 559–564.5 indexed citations
13.
Dragone, Mauro, et al.. (2016). RSS-based Robot Localization in Critical Environments using Reservoir Computing. CINECA IRIS Institutial research information system (University of Pisa). 71–76.4 indexed citations
14.
Gallicchio, Claudio & Alessio Micheli. (2016). A Reservoir Computing Approach for Human Gesture Recognition from Kinect Data.. CINECA IRIS Institutial research information system (University of Pisa). 1803. 33–42.3 indexed citations
15.
Bacciu, Davide, Claudio Gallicchio, & Alessio Micheli. (2016). A reservoir activation kernel for trees. CINECA IRIS Institutial research information system (University of Pisa). 29–34.2 indexed citations
16.
Gallicchio, Claudio & Alessio Micheli. (2016). Deep reservoir computing: A critical analysis. CINECA IRIS Institutial research information system (University of Pisa). 497–502.26 indexed citations
17.
Gallicchio, Claudio, Alessio Micheli, Luca Pedrelli, Federico Vozzi, & Oberdan Parodi. (2015). Preliminary experimental analysis of reservoir computing approach for balance assessment. CINECA IRIS Institutial research information system (University of Pisa). 1425. 51–56.1 indexed citations
18.
Gallicchio, Claudio, Alessio Micheli, Paolo Barsocchi, & Stefano Chessa. (2011). Reservoir Computing Forecasting of User Movements from RSS Mote-Class Sensors Measurement. UnipiEprints Open Archive (Università di Pisa).2 indexed citations
19.
Gallicchio, Claudio & Alessio Micheli. (2010). A Markovian characterization of redundancy in echo state networks by PCA.. The European Symposium on Artificial Neural Networks. 321–326.2 indexed citations
20.
Gallicchio, Claudio & Alessio Micheli. (2009). On the Predictive Effects of Markovian and Architectural Factors of Echo State Networks. UnipiEprints Open Archive (Università di Pisa). 1–43.2 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.