Francis R. Bach
- Computer Vision and Pattern Recognition top 2%
- Artificial Intelligence top 5%
- Computational Mechanics top 5%
- Signal Processing top 5%
- Media Technology top 5%
- Co-authors
- Michael I. JordanJulien MairalGuillaume ObozinskiRodolphe JenattonCédric ArchambeauDominique JeulinA. CordAlexandre d’Aspremont
- Topics
- Sparse and Compressive Sensing Techniques (3 papers)Image and Signal Denoising Methods (2 papers)Speech and Audio Processing (2 papers)
- Journals
- Food ChemistryJournal of MicroscopyHAL (Le Centre pour la Communication Scientifique Directe)
- Partner nations
- United StatesFranceUnited Kingdom
In The Last Decade
Francis R. Bach
11 papers receiving 775 citations
Peers
Comparison fields: 5 of 93
- Computer Vision and Pattern Recognition 410
- Artificial Intelligence 319
- Computational Mechanics 219
- Signal Processing 166
- Media Technology 99
Countries citing papers authored by Francis R. Bach
This map shows the geographic impact of Francis R. 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 R. 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 R. Bach more than expected).
Fields of papers citing papers by Francis R. Bach
This network shows the impact of papers produced by Francis R. 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 R. Bach. The network helps show where Francis R. Bach may publish in the future.
Co-authorship network of co-authors of Francis R. Bach
This figure shows the co-authorship network connecting the top 25 collaborators of Francis R. Bach. A scholar is included among the top collaborators of Francis R. 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 R. Bach. Francis R. 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 | Learning with Differentiable Pertubed Optimizers | 20 |
| 2 | Dictionary Learning for Deblurring and Digital Zoom | 11 |
| 3 | 215 | |
| 4 | 29 | |
| 5 | Sparse probabilistic projections | 77 |
| 6 | Segmentation of random textures by morphological and linear operators. | 6 |
| 7 | 34 | |
| 8 | 19 | |
| 9 | Blind One-microphone Speech Separation: A Spectral Learning Approach | 61 |
| 10 | Computing regularization paths for learning multiple kernels | 64 |
| 11 | Learning Spectral Clustering | 288 |
About Francis R. Bach
Francis R. Bach is a scholar working on Signal Processing, Computer Vision and Pattern Recognition and Media Technology, having authored 11 papers that have together received 824 indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (3 papers), Image and Signal Denoising Methods (2 papers) and Speech and Audio Processing (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (410 citations), Signal Processing (166 citations) and Media Technology (99 citations). Francis R. Bach has collaborated with scholars based in United States, France and United Kingdom. Frequent co-authors include Michael I. Jordan, Julien Mairal, Guillaume Obozinski, Rodolphe Jenatton, Cédric Archambeau, Dominique Jeulin, A. Cord, Alexandre d’Aspremont, Laurent El Ghaoui and Quentin Berthet. Their work appears in journals such as Food Chemistry, Journal of Microscopy and HAL (Le Centre pour la Communication Scientifique Directe).
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.