Jake Bouvrie
- Artificial Intelligence top 10%
- Signal Processing top 5%
- Computer Vision and Pattern Recognition top 5%
- Cognitive Neuroscience
- Media Technology top 10%
- Co-authors
- Tomaso PoggioTony EzzatMaxim RaginskyLorenzo RosascoPawan SinhaRyan RifkinJean-Jacques SlotineAndre Wibisono
- Topics
- Speech and Audio Processing (5 papers)Music and Audio Processing (4 papers)Blind Source Separation Techniques (4 papers)
- Journals
- Neural ComputationNeurocomputingDSpace@MIT (Massachusetts Institute of Technology)
- Partner nations
- United StatesLebanon
In The Last Decade
Jake Bouvrie
14 papers receiving 485 citations
Peers
Comparison fields: 5 of 86
- Artificial Intelligence 174
- Signal Processing 157
- Computer Vision and Pattern Recognition 151
- Cognitive Neuroscience 50
- Media Technology 35
Countries citing papers authored by Jake Bouvrie
This map shows the geographic impact of Jake Bouvrie'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 Jake Bouvrie with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jake Bouvrie more than expected).
Fields of papers citing papers by Jake Bouvrie
This network shows the impact of papers produced by Jake Bouvrie. 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 Jake Bouvrie. The network helps show where Jake Bouvrie may publish in the future.
Co-authorship network of co-authors of Jake Bouvrie
This figure shows the co-authorship network connecting the top 25 collaborators of Jake Bouvrie. A scholar is included among the top collaborators of Jake Bouvrie 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 Jake Bouvrie. Jake Bouvrie is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 23 | |
| 3 | 7 | |
| 4 | Learning and Invariance in a Family of Hierarchical Kernels | 4 |
| 5 | On Invariance in Hierarchical Models | 22 |
| 6 | 34 | |
| 7 | 12 | |
| 8 | 59 | |
| 9 | Phonetic Classification Using Hierarchical, Feed-forward, Spectro-temporal Patch-based Architectures | 8 |
| 10 | 18 | |
| 11 | 12 | |
| 12 | Notes on Convolutional Neural Networks | 305 |
| 13 | 6 | |
| 14 | 5 |
About Jake Bouvrie
Jake Bouvrie is a scholar working on Signal Processing, Media Technology and Computer Vision and Pattern Recognition, having authored 14 papers that have together received 517 indexed citations. Recurring topics across this work include Speech and Audio Processing (5 papers), Music and Audio Processing (4 papers) and Blind Source Separation Techniques (4 papers). The work is most often cited by research in Signal Processing (157 citations), Computer Vision and Pattern Recognition (151 citations) and Artificial Intelligence (174 citations). Jake Bouvrie has collaborated with scholars based in United States and Lebanon. Frequent co-authors include Tomaso Poggio, Tony Ezzat, Maxim Raginsky, Lorenzo Rosasco, Pawan Sinha, Ryan Rifkin, Jean-Jacques Slotine, Andre Wibisono, Mauro Maggioni and Sharat Chikkerur. Their work appears in journals such as Neural Computation, Neurocomputing and DSpace@MIT (Massachusetts Institute of Technology).
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.