Luc Lamontagne
About
In The Last Decade
Luc Lamontagne
39 papers receiving 203 citations
Peers
Comparison fields: 5 of 76
- Artificial Intelligence 78
- Information Systems 63
- Software 43
- Management Science and Operations Research 29
- Computer Vision and Pattern Recognition 25
Countries citing papers authored by Luc Lamontagne
This map shows the geographic impact of Luc Lamontagne'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 Luc Lamontagne with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Luc Lamontagne more than expected).
Fields of papers citing papers by Luc Lamontagne
This network shows the impact of papers produced by Luc Lamontagne. 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 Luc Lamontagne. The network helps show where Luc Lamontagne may publish in the future.
Co-authorship network of co-authors of Luc Lamontagne
This figure shows the co-authorship network connecting the top 25 collaborators of Luc Lamontagne. A scholar is included among the top collaborators of Luc Lamontagne 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 Luc Lamontagne. Luc Lamontagne is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 2 | |
| 3 | 1 | |
| 4 | 5 | |
| 5 | 1 | |
| 6 | 1 | |
| 7 | 5 | |
| 8 | 1 | |
| 9 | Encoding Neighbor Information into Geographical Embeddings Using Convolutional Neural Networks. | 2 |
| 10 | Building User Interest Profiles Using DBpedia in a Question Answering System. | 2 |
| 11 | Word embeddings and Global Preference for Contextual Suggestion. | 4 |
| 12 | Laval University and Lakehead University Experiments at TREC 2015 Contextual Suggestion Track | 1 |
| 13 | Optimizing Question-Answering Systems Using Genetic Algorithms | 0 |
| 14 | Learning Case Feature Weights from Relevance and Ranking Feedback | 1 |
| 15 | Towards a Unified Metrics Suite for JUnit Test Cases. | 4 |
| 16 | 15 | |
| 17 | The optimal searcher path problem with a visibility criterion in discrete time and space | 9 |
| 18 | Reinforcement of Local Pattern Cases for Playing Tetris. | 5 |
| 19 | Applying Case-Based Reasoning to Email Response. | 4 |
| 20 | Using Statistical Word Associations for the Retrieval of Strongly-Textual Cases. | 3 |
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.