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.
On the Complexity of Neural Network Classifiers: A Comparison Between Shallow and Deep Architectures
Countries citing papers authored by Monica Bianchini
Since
Specialization
Citations
This map shows the geographic impact of Monica Bianchini'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 Monica Bianchini with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Monica Bianchini more than expected).
Fields of papers citing papers by Monica Bianchini
This network shows the impact of papers produced by Monica Bianchini. 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 Monica Bianchini. The network helps show where Monica Bianchini may publish in the future.
Co-authorship network of co-authors of Monica Bianchini
This figure shows the co-authorship network connecting the top 25 collaborators of Monica Bianchini.
A scholar is included among the top collaborators of Monica Bianchini 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 Monica Bianchini. Monica Bianchini is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Rossi, Alberto, Simone Bonechi, Paolo Andreini, et al.. (2020). Graph Neural Networks for the Prediction of Protein-Protein Interfaces.. Use Siena air (University of Siena). 127–132.13 indexed citations
Bianchini, Monica & Franco Scarselli. (2014). On the complexity of shallow and deep neural network classifiers. Use Siena air (University of Siena). 371–376.22 indexed citations
14.
Bianchini, Monica, et al.. (2006). A Cyclostationary Neural Network Model for the Prediction of the NO2 Concentration. The European Symposium on Artificial Neural Networks. 67–72.6 indexed citations
15.
Bianchini, Monica, Marco Gori, Lorenzo Sarti, & Franco Scarselli. (2006). Recursive Processing of Cyclic Graphs. IEEE Transactions on Neural Networks. 17(1). 10–18.12 indexed citations
16.
Bianchini, Monica, et al.. (2003). Face Spotting in Color Images using Recursive Neural Networks. Use Siena air (University of Siena). 76–81.14 indexed citations
17.
Bianchini, Monica, et al.. (1997). Solving Linear Systems by a Neural Network Canonical Form of Efficient Gradient Descent. International Conference on Neural Information Processing. 1. 531–534.2 indexed citations
Rigatelli, Marco, Monica Bianchini, & G. Pietri. (1992). The Psychiatric-psychosomatic consultations in Modena University Hospital Department of Dermatology. The Present state and the perspectives.. IRIS UNIMORE (University of Modena and Reggio Emilia).1 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.