David Balduzzi

5.4k total citations · 2 hit papers
26 papers, 1.4k citations indexed

About

David Balduzzi is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Cognitive Neuroscience. According to data from OpenAlex, David Balduzzi has authored 26 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Artificial Intelligence, 7 papers in Computer Vision and Pattern Recognition and 7 papers in Cognitive Neuroscience. Recurrent topics in David Balduzzi's work include Neural dynamics and brain function (6 papers), Domain Adaptation and Few-Shot Learning (5 papers) and Advanced Memory and Neural Computing (3 papers). David Balduzzi is often cited by papers focused on Neural dynamics and brain function (6 papers), Domain Adaptation and Few-Shot Learning (5 papers) and Advanced Memory and Neural Computing (3 papers). David Balduzzi collaborates with scholars based in United States, New Zealand and Germany. David Balduzzi's co-authors include Giulio Tononi, Muhammad Ghifary, W. Bastiaan Kleijn, Mengjie Zhang, Manuel Gomez-Rodriguez, Jure Leskovec, Bernhard Schölkopf, Brian McWilliams, Paolo Delvino and Gino Roberto Corazza and has published in prestigious journals such as PLoS ONE, IEEE Transactions on Pattern Analysis and Machine Intelligence and PLoS Computational Biology.

In The Last Decade

David Balduzzi

26 papers receiving 1.3k citations

Hit Papers

Domain Generalization for Object Recognition with Multi-t... 2015 2026 2018 2022 2015 2016 100 200 300

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
David Balduzzi United States 11 705 438 338 132 67 26 1.4k
Alexander Lerchner United States 8 711 1.0× 616 1.4× 160 0.5× 107 0.8× 66 1.0× 15 1.4k
Ben Poole United States 13 664 0.9× 706 1.6× 201 0.6× 53 0.4× 76 1.1× 25 1.5k
Irina Higgins United Kingdom 8 741 1.1× 621 1.4× 138 0.4× 78 0.6× 54 0.8× 15 1.4k
Löıc Matthey United States 7 706 1.0× 613 1.4× 108 0.3× 56 0.4× 38 0.6× 11 1.4k
Thomas Navin Lal Germany 7 1.5k 2.1× 1.3k 3.1× 341 1.0× 235 1.8× 116 1.7× 10 2.9k
Carlo Baldassi Italy 17 480 0.7× 156 0.4× 193 0.6× 104 0.8× 111 1.7× 35 971
Raman Arora United States 17 1.0k 1.5× 1.0k 2.4× 120 0.4× 69 0.5× 70 1.0× 72 2.1k
Seungjin Choi South Korea 19 376 0.5× 346 0.8× 316 0.9× 88 0.7× 136 2.0× 89 1.5k
Lijuan Duan China 19 250 0.4× 420 1.0× 321 0.9× 104 0.8× 120 1.8× 95 1.1k
Mohammad Sadegh Helfroush Iran 20 415 0.6× 609 1.4× 161 0.5× 49 0.4× 26 0.4× 110 1.2k

Countries citing papers authored by David Balduzzi

Since Specialization
Citations

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

Fields of papers citing papers by David Balduzzi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of David Balduzzi

This figure shows the co-authorship network connecting the top 25 collaborators of David Balduzzi. A scholar is included among the top collaborators of David Balduzzi 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 David Balduzzi. David Balduzzi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Balduzzi, David, Wojciech Marian Czarnecki, Edward Hughes, et al.. (2020). Smooth markets: A basic mechanism for organizing gradient-based learners. arXiv (Cornell University). 2 indexed citations
2.
Balduzzi, David, et al.. (2020). From Chaos to Order: Symmetry and Conservation Laws in Game Dynamics. 1. 7186–7196. 2 indexed citations
3.
Balduzzi, David, Marta Garnelo, Yoram Bachrach, et al.. (2019). Open-ended learning in symmetric zero-sum games. UCL Discovery (University College London). 434–443. 2 indexed citations
4.
Balduzzi, David, Sébastien Racanière, James Martens, et al.. (2019). Differentiable Game Mechanics. Journal of Machine Learning Research. 20(84). 1–40. 6 indexed citations
5.
Balduzzi, David, Sébastien Racanière, James Martens, et al.. (2018). The Mechanics of n-Player Differentiable Games. UCL Discovery (University College London). 354–363. 13 indexed citations
6.
Balduzzi, David, Karl Tuyls, Julien Pérolat, & Thore Graepel. (2018). Re-evaluating evaluation. UCL Discovery (University College London). 31. 3268–3279. 3 indexed citations
7.
Ghifary, Muhammad, David Balduzzi, W. Bastiaan Kleijn, & Mengjie Zhang. (2016). Scatter Component Analysis: A Unified Framework for Domain Adaptation and Domain Generalization. IEEE Transactions on Pattern Analysis and Machine Intelligence. 39(7). 1414–1430. 323 indexed citations breakdown →
8.
Balduzzi, David. (2016). Grammars for Games: A Gradient-Based, Game-Theoretic Framework for Optimization in Deep Learning. Frontiers in Robotics and AI. 2. 2 indexed citations
9.
Biagi, Federico, David Balduzzi, Paolo Delvino, et al.. (2015). Prevalence of Whipple's disease in north-western Italy. European Journal of Clinical Microbiology & Infectious Diseases. 34(7). 1347–1348. 43 indexed citations
10.
Ghifary, Muhammad, W. Bastiaan Kleijn, Mengjie Zhang, & David Balduzzi. (2015). Domain Generalization for Object Recognition with Multi-task Autoencoders. 2551–2559. 339 indexed citations breakdown →
11.
Gomez-Rodriguez, Manuel, Jure Leskovec, David Balduzzi, & Bernhard Schölkopf. (2014). Uncovering the structure and temporal dynamics of information propagation. Network Science. 2(1). 26–65. 89 indexed citations
12.
Balduzzi, David, Pedro A. Ortega, & Michel Besserve. (2013). METABOLIC COST AS AN ORGANIZING PRINCIPLE FOR COOPERATIVE LEARNING. Advances in Complex Systems. 16(02n03). 1350012–1350012. 5 indexed citations
13.
McWilliams, Brian, David Balduzzi, & Joachim M. Buhmann. (2013). Correlated random features for fast semi-supervised learning. arXiv (Cornell University). 26. 440–448. 10 indexed citations
14.
Olcese, Umberto, et al.. (2012). A Neuromorphic Architecture for Object Recognition and Motion Anticipation Using Burst-STDP. PLoS ONE. 7(5). e36958–e36958. 20 indexed citations
15.
Balduzzi, David & Giulio Tononi. (2012). What can neurons do for their brain? Communicate selectivity with bursts. Theory in Biosciences. 132(1). 27–39. 19 indexed citations
16.
Balduzzi, David. (2011). Information, learning and falsification. PhilSci-Archive (University of Pittsburgh). 1–4. 2 indexed citations
17.
Balduzzi, David. (2011). Estimating integrated information with TMS pulses during wakefulness, sleep, and under anesthesia. PubMed. 34. 4717–4720. 3 indexed citations
18.
Balduzzi, David & Giulio Tononi. (2009). Qualia: The Geometry of Integrated Information. PLoS Computational Biology. 5(8). e1000462–e1000462. 151 indexed citations
19.
Balduzzi, David & Giulio Tononi. (2008). Integrated Information in Discrete Dynamical Systems: Motivation and Theoretical Framework. PLoS Computational Biology. 4(6). e1000091–e1000091. 235 indexed citations
20.
Balduzzi, David. (2006). Donagi-Markman cubic for Hitchin systems. Mathematical Research Letters. 13(6). 923–933. 7 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.

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