Sepp Hochreiter
- Artificial Intelligence top 0.01%
- Neural Networks and Applications 14
- Computer Vision and Pattern Recognition top 0.02%
- Signal Processing top 0.01%
- Blind Source Separation Techniques 8
- Environmental Engineering top 0.05%
- Hydrological Forecasting Using AI 12
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- Gene expression and cancer classification 17
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- Computational Drug Discovery Methods 17
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- Hydrology and Watershed Management Studies 13
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- Flood Risk Assessment and Management 12
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- Machine Learning in Materials Science 10
- Co-authors
- Jürgen SchmidhuberThomas UnterthinerMartin HeuselGünter KlambauerBernhard NesslerHubert RamsauerUlrich BodenhoferAndreas Mayr
- Partner nations
- AustriaGermanyUnited States
In The Last Decade
Sepp Hochreiter
104 papers receiving 66.8k citations
Hit Papers
Peers
Comparison fields: 5 of 233
- Artificial Intelligence 26.8k
- Computer Vision and Pattern Recognition 14.2k
- Signal Processing 6.8k
- Management Science and Operations Research 4.3k
- Environmental Engineering 4.8k
Countries citing papers authored by Sepp Hochreiter
This map shows the geographic impact of Sepp Hochreiter'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 Sepp Hochreiter with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sepp Hochreiter more than expected).
Fields of papers citing papers by Sepp Hochreiter
This network shows the impact of papers produced by Sepp Hochreiter. 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 Sepp Hochreiter. The network helps show where Sepp Hochreiter may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Sepp Hochreiter, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 2 | |
| 2 | 2024 | 5 | |
| 3 | 2024 | 5 | |
| 4 | 2023 | 11 | |
| 5 | 2023 | 32 | |
| 6 | 2023 | 14 | |
| 7 | 2022 | 2 | |
| 8 | 2022 | 104 | |
| 9 | 2022 | 50 | |
| 10 | 2021 | 13 | |
| 11 | 2021 | 1 | |
| 12 | 2021 | 4 | |
| 13 | Fréchet ChemblNet Distance: A metric for generative models for molecules. | 2018 | 2 |
| 14 | Human-level Protein Localization with Convolutional Neural Networks | 2018 | 5 |
| 15 | Rectified factor networks | 2015 | 2 |
| 16 | Modeling Position Specificity in Sequence Kernels by Fuzzy Equivalence Relations | 2009 | 3 |
| 17 | Feature Selection and Classification on Matrix Data: From Large Margins to Small Covering Numbers | 2002 | 4 |
| 18 | Source Separation as a By-Product of Regularization | 1998 | 5 |
| 19 | LSTM can Solve Hard Long Time Lag Problems | 1996 | 481 |
| 20 | SIMPLIFYING NEURAL NETS BY DISCOVERING FLAT MINIMA | 1994 | 40 |
About Sepp Hochreiter
Sepp Hochreiter is a scholar working on Computational Theory and Mathematics, Artificial Intelligence and Water Science and Technology, having authored 109 papers that have together received 69.8k indexed citations. Recurring topics across this work include Gene expression and cancer classification (17 papers), Computational Drug Discovery Methods (17 papers), Neural Networks and Applications (14 papers), Hydrology and Watershed Management Studies (13 papers), Hydrological Forecasting Using AI (12 papers), Flood Risk Assessment and Management (12 papers), Machine Learning in Materials Science (10 papers) and Blind Source Separation Techniques (8 papers). The work is most often cited by research in Artificial Intelligence (26.8k citations), Computer Vision and Pattern Recognition (14.2k citations) and Signal Processing (6.8k citations). Sepp Hochreiter has collaborated with scholars based in Austria, Germany and United States. Frequent co-authors include Jürgen Schmidhuber, Thomas Unterthiner, Martin Heusel, Günter Klambauer, Bernhard Nessler, Hubert Ramsauer, Ulrich Bodenhofer, Andreas Mayr, Djork-Arné Clevert and Frederik Kratzert. Their work appears in journals such as Nucleic Acids Research, Nature Communications 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.