Xiao Da

52 papers receiving 1.8k citations

Xiao Da's Hit Papers

An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks 2014 · 353 citations
3530+4+8Years since publication100200300

Peers

Xiao Da
Comparison fields: 5 of 128
  • Genetics 340
  • Radiology, Nuclear Medicine and Imaging 469
  • Neurology 172
  • Psychiatry and Mental health 278
  • Health Informatics 27
Replace Saima Rathore with:
Saima Rathore United States
Kelvin Wong United States
Evangelia I. Zacharaki Greece
Vasileios Megalooikonomou Greece
Manuel Gómez-Río Spain
Marco Lorenzi France
Diana M. Sima Belgium
Dong Liang China
Kourosh Jafari‐Khouzani United States
Francesca Gallivanone Italy
Xiao Da relative to Saima Rathore United States Saima Rathore's profile →
Citations per field
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Saima Rathore · 1×
Citations per year

Countries citing papers authored by Xiao Da

Since Specialization
Citations

This map shows the geographic impact of Xiao Da'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 Xiao Da with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xiao Da more than expected).

Fields of papers citing papers by Xiao Da

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Xiao Da. 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 Xiao Da. The network helps show where Xiao Da may publish in the future.

Co-authors

The 25 scholars most cited alongside Xiao Da, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Xiao Da Line = papers co-authored together Xiao Da links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 54 papers — load more, or switch the sort, to bring in the rest.

#Work
1
An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks
Hit paper breakdown →
2014353
2 2015236
3 2013138
4 2014131
5 2016115
6 2013106
7 201480
8 201568
9 201667
10 201466
11 201460
12 201651
13 201337
14 201532
15 201429
16 202128
17 201728
18 201821
19 202320
20 201717

About Xiao Da

Xiao Da is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Computer Networks and Communications, Information Systems and Genetics, having authored 54 papers that have together received 1.9k indexed citations. Recurring topics across this work include Glioma Diagnosis and Treatment (7 papers), Dementia and Cognitive Impairment Research (6 papers), Cryptography and Data Security (5 papers), Advanced SAR Imaging Techniques (5 papers), Advanced Malware Detection Techniques (5 papers), Cloud Data Security Solutions (5 papers), Software Testing and Debugging Techniques (4 papers) and Network Security and Intrusion Detection (4 papers). The work is most often cited by research in Genetics (340 citations), Radiology, Nuclear Medicine and Imaging (469 citations), Neurology (172 citations), Psychiatry and Mental health (278 citations) and Health Informatics (27 citations). Xiao Da has collaborated with scholars based in United States, China and Norway. Frequent co-authors include Christos Davatzikos, Aaron Courville, Ian Goodfellow, Hamed Akbari, Mehdi Mirza, Yoshua Bengio, Michel Bilello, Yangming Ou, Donald M. O’Rourke and Ronald L. Wolf. Their work appears in journals such as NeuroImage Clinical, Neurosurgery, Electronics Letters, Acta Neuropathologica Communications and Journal of Alzheimer s Disease.

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.

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