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.
Assessing gender bias in machine translation: a case study with Google Translate
This map shows the geographic impact of Luís C. Lamb'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 Luís C. Lamb with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Luís C. Lamb more than expected).
This network shows the impact of papers produced by Luís C. Lamb. 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 Luís C. Lamb. The network helps show where Luís C. Lamb may publish in the future.
Co-authorship network of co-authors of Luís C. Lamb
This figure shows the co-authorship network connecting the top 25 collaborators of Luís C. Lamb.
A scholar is included among the top collaborators of Luís C. Lamb 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 Luís C. Lamb. Luís C. Lamb 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.
Garcez, Artur d’Avila & Luís C. Lamb. (2023). Neurosymbolic AI: the 3rd wave. Artificial Intelligence Review. 56(11). 12387–12406.102 indexed citations
Lamb, Luís C., et al.. (2008). Memetic networks: analyzing the effects of network properties in multi-agent performance. National Conference on Artificial Intelligence. 3–8.11 indexed citations
12.
Lamb, Luís C., et al.. (2007). An information-theoretic analysis of memory bounds in a distributed resource allocation mechanism. International Joint Conference on Artificial Intelligence. 212–217.4 indexed citations
13.
Lamb, Luís C., et al.. (2007). A connectionist cognitive model for temporal synchronisation and learning. National Conference on Artificial Intelligence. 827–832.19 indexed citations
Lamb, Luís C., Artur d’Avila Garcez, & Michael Gabbay. (2005). Metalevel priorities and neural networks. City Research Online (City University London).1 indexed citations
18.
Garcez, Artur d’Avila, Luís C. Lamb, & Dov M. Gabbay. (2003). Neural-symbolic intuitionistic reasoning. 399–408.7 indexed citations
19.
Garcez, Artur d’Avila, Luís C. Lamb, Krysia Broda, & Dov M. Gabbay. (2003). Distributed Knowledge Representation in Neural-Symbolic Learning Systems: A Case Study. The Florida AI Research Society. 271–275.4 indexed citations
20.
Garcez, Artur d’Avila & Luís C. Lamb. (2003). Reasoning about Time and Knowledge in Neural Symbolic Learning Systems. Neural Information Processing Systems. 16. 921–928.15 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.