Laurent Sifre
- Artificial Intelligence top 0.05%
- Computer Vision and Pattern Recognition top 0.2%
- Electrical and Electronic Engineering top 1%
- Molecular Biology top 5%
- Control and Systems Engineering top 0.5%
- Co-authors
- David SilverDemis HassabisArthur GuezIoannis AntonoglouJulian SchrittwieserTimothy LillicrapThore GraepelAja Huang
- Topics
- Artificial Intelligence in Games (3 papers)Image Retrieval and Classification Techniques (3 papers)Reinforcement Learning in Robotics (3 papers)
- Partner nations
- United KingdomFranceUnited States
In The Last Decade
Laurent Sifre
9 papers receiving 17.6k citations
Hit Papers
Peers
Comparison fields: 5 of 216
- Artificial Intelligence 8.6k
- Computer Vision and Pattern Recognition 2.6k
- Electrical and Electronic Engineering 2.5k
- Molecular Biology 2.0k
- Control and Systems Engineering 1.9k
Countries citing papers authored by Laurent Sifre
This map shows the geographic impact of Laurent Sifre'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 Laurent Sifre with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Laurent Sifre more than expected).
Fields of papers citing papers by Laurent Sifre
This network shows the impact of papers produced by Laurent Sifre. 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 Laurent Sifre. The network helps show where Laurent Sifre may publish in the future.
Co-authorship network of co-authors of Laurent Sifre
This figure shows the co-authorship network connecting the top 25 collaborators of Laurent Sifre. A scholar is included among the top collaborators of Laurent Sifre 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 Laurent Sifre. Laurent Sifre is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Improved protein structure prediction using potentials from deep learningbreakdown → | 2026 |
| 2 | Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13)breakdown → | 208 |
| 3 | A general reinforcement learning algorithm that masters chess, shogi, and Go through self-playbreakdown → | 1827 |
| 4 | Mastering the game of Go without human knowledgebreakdown → | 5038 |
| 5 | Mastering the game of Go with deep neural networks and tree searchbreakdown → | 8793 |
| 6 | 173 | |
| 7 | Generic Deep Networks with Wavelet Scattering | 5 |
| 8 | 229 | |
| 9 | Combined scattering for rotation invariant texture analysis. | 30 |
About Laurent Sifre
Laurent Sifre is a scholar working on Computer Vision and Pattern Recognition, Developmental and Educational Psychology and Artificial Intelligence, having authored 9 papers that have together received 18.3k indexed citations. Recurring topics across this work include Artificial Intelligence in Games (3 papers), Image Retrieval and Classification Techniques (3 papers) and Reinforcement Learning in Robotics (3 papers). The work is most often cited by research in Artificial Intelligence (8.6k citations), Health Informatics (265 citations) and Computer Vision and Pattern Recognition (2.6k citations). Laurent Sifre has collaborated with scholars based in United Kingdom, France and United States. Frequent co-authors include David Silver, Demis Hassabis, Arthur Guez, Ioannis Antonoglou, Julian Schrittwieser, Timothy Lillicrap, Thore Graepel, Aja Huang, George van den Driessche and Marc Lanctot. Their work appears in journals such as Nature, Science and Proteins Structure Function and Bioinformatics.
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.