Francis Bach
- Computer Vision and Pattern Recognition top 0.1%
- Computational Mechanics top 0.5%
- Media Technology top 0.2%
- Artificial Intelligence top 1%
- Signal Processing top 0.5%
- Co-authors
- Jean PonceJulien MairalGuillermo SapiroAndrew ZissermanYann LeCunY-Lan BoureauOlivier DuchenneIn-So Kweon
- Topics
- Sparse and Compressive Sensing Techniques (13 papers)Image and Signal Denoising Methods (5 papers)Blind Source Separation Techniques (5 papers)
- Partner nations
- FranceUnited StatesSouth Korea
In The Last Decade
Francis Bach
22 papers receiving 5.8k citations
Hit Papers
Peers
Comparison fields: 5 of 139
- Computer Vision and Pattern Recognition 3.9k
- Computational Mechanics 1.8k
- Media Technology 1.3k
- Artificial Intelligence 1.2k
- Signal Processing 867
Countries citing papers authored by Francis Bach
This map shows the geographic impact of Francis Bach'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 Francis Bach with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Francis Bach more than expected).
Fields of papers citing papers by Francis Bach
This network shows the impact of papers produced by Francis Bach. 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 Francis Bach. The network helps show where Francis Bach may publish in the future.
Co-authorship network of co-authors of Francis Bach
This figure shows the co-authorship network connecting the top 25 collaborators of Francis Bach. A scholar is included among the top collaborators of Francis Bach 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 Francis Bach. Francis Bach is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 4 | |
| 2 | 1 | |
| 3 | 14 | |
| 4 | Learning Hierarchical and Topographic Dictionaries with Structured Sparsity | 7 |
| 5 | 47 | |
| 6 | Learning to Learn for Structured Sparsity | 1 |
| 7 | 8 | |
| 8 | 51 | |
| 9 | 19 | |
| 10 | 225 | |
| 11 | Online Learning for Matrix Factorization and Sparse Codingbreakdown → | 1597 |
| 12 | Learning mid-level features for recognitionbreakdown → | 702 |
| 13 | 76 | |
| 14 | Non-local sparse models for image restorationbreakdown → | 1199 |
| 15 | 20 | |
| 16 | Online dictionary learning for sparse codingbreakdown → | 1414 |
| 17 | 141 | |
| 18 | 33 | |
| 19 | 1 | |
| 20 | 4 |
About Francis Bach
Francis Bach is a scholar working on Computational Mathematics, Computer Vision and Pattern Recognition and Computational Mechanics, having authored 23 papers that have together received 6.1k indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (13 papers), Image and Signal Denoising Methods (5 papers) and Blind Source Separation Techniques (5 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (3.9k citations), Computational Mathematics (88 citations) and Media Technology (1.3k citations). Francis Bach has collaborated with scholars based in France, United States and South Korea. Frequent co-authors include Jean Ponce, Julien Mairal, Guillermo Sapiro, Andrew Zisserman, Yann LeCun, Y-Lan Boureau, Olivier Duchenne, In-So Kweon, Alexandre d’Aspremont and Laurent El Ghaoui. Their work appears in journals such as The Lancet, Bioinformatics and IEEE Transactions on Pattern Analysis and Machine Intelligence.
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.