Jörg Lücke
- Artificial Intelligence top 5%
- Cognitive Neuroscience top 10%
- Computer Vision and Pattern Recognition top 10%
- Neurology top 10%
- Signal Processing top 10%
- Co-authors
- Lars Schmidt-ThiemeFrank HutterNoritoshi AraiUlf ZiemannChristoph von der MalsburgManeesh SahaniJulian EggertCristina Savin
- Topics
- Neural dynamics and brain function (16 papers)Neural Networks and Applications (13 papers)Blind Source Separation Techniques (11 papers)
- Partner nations
- GermanyUnited KingdomUnited States
In The Last Decade
Jörg Lücke
41 papers receiving 591 citations
Peers
Comparison fields: 5 of 118
- Artificial Intelligence 210
- Cognitive Neuroscience 207
- Computer Vision and Pattern Recognition 101
- Neurology 93
- Signal Processing 86
Countries citing papers authored by Jörg Lücke
This map shows the geographic impact of Jörg Lücke'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 Jörg Lücke with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jörg Lücke more than expected).
Fields of papers citing papers by Jörg Lücke
This network shows the impact of papers produced by Jörg Lücke. 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 Jörg Lücke. The network helps show where Jörg Lücke may publish in the future.
Co-authorship network of co-authors of Jörg Lücke
This figure shows the co-authorship network connecting the top 25 collaborators of Jörg Lücke. A scholar is included among the top collaborators of Jörg Lücke 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 Jörg Lücke. Jörg Lücke 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 | 3 | |
| 3 | 4 | |
| 4 | 6 | |
| 5 | Can clustering scale sublinearly with its clusters? A variational EM acceleration of GMMs and k-means | 1 |
| 6 | 8 | |
| 7 | Select-and-Sample for Spike-and-Slab Sparse Coding | 2 |
| 8 | Neurons Equipped with Intrinsic Plasticity Learn Stimulus Intensity Statistics | 3 |
| 9 | 9 | |
| 10 | 123 | |
| 11 | 19 | |
| 12 | What Are the Invariant Occlusive Components of Image Patches? A Probabilistic Generative Approach | 5 |
| 13 | 14 | |
| 14 | 22 | |
| 15 | A Closed-Form EM Algorithm for Sparse Coding | 4 |
| 16 | Expectation Truncation and the Benefits of Preselection In Training Generative Models | 28 |
| 17 | Occlusive Components Analysis | 10 |
| 18 | 100 | |
| 19 | Maximal Causes for Non-linear Component Extraction | 37 |
| 20 | 10 |
About Jörg Lücke
Jörg Lücke is a scholar working on Signal Processing, Cognitive Neuroscience and Artificial Intelligence, having authored 43 papers that have together received 609 indexed citations. Recurring topics across this work include Neural dynamics and brain function (16 papers), Neural Networks and Applications (13 papers) and Blind Source Separation Techniques (11 papers). The work is most often cited by research in Neurology (93 citations), Cognitive Neuroscience (207 citations) and Signal Processing (86 citations). Jörg Lücke has collaborated with scholars based in Germany, United Kingdom and United States. Frequent co-authors include Lars Schmidt-Thieme, Frank Hutter, Noritoshi Arai, Ulf Ziemann, Christoph von der Malsburg, Maneesh Sahani, Julian Eggert, Cristina Savin, Zhenwen Dai and Carl‐Magnus Svensson. Their work appears in journals such as PLoS ONE, IEEE Transactions on Pattern Analysis and Machine Intelligence and NeuroImage.
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.