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D-Index & Metrics

Computer Science

D-Index
56
Citations
142099
World Ranking
3923
National Ranking
24

Overview

Sepp Hochreiter is affiliated with Johannes Kepler University of Linz in Austria. Their research primarily focuses on computer science, with a significant emphasis on artificial intelligence. The body of work spans various subfields, including molecular biology, computer vision and pattern recognition, computational theory and mathematics, and environmental engineering.

The scientist's research covers diverse topics that include computational drug discovery methods, hydrological forecasting using AI, flood risk assessment and management, hydrology and watershed management studies, domain adaptation and few-shot learning, machine learning in materials science, and multimodal machine learning applications.

Sepp Hochreiter has a substantial publication record, frequently collaborating with several researchers. Notable coauthors include:

  • Günter Klambauer (34 publications)
  • J. Brandstetter (22 publications)
  • Daniel Klotz (17 publications)
  • Philipp Seidl (14 publications)
  • Frederik Kratzert (12 publications)

Their works have appeared in various publication venues, with a concentration in:

  • arXiv (Cornell University) - 63 publications
  • Zenodo (CERN European Organization for Nuclear Research) - 7 publications
  • bioRxiv (Cold Spring Harbor Laboratory) - 6 publications
  • Hydrology and earth system sciences - 3 publications
  • Journal of Chemical Information and Modeling - 2 publications

Recent papers authored by Sepp Hochreiter include:

  • "Uncertainty estimation with deep learning for rainfall-runoff modeling" (2022, Hydrology and earth system sciences)
  • "A note on leveraging synergy in multiple meteorological data sets with deep learning for rainfall-runoff modeling" (2021, Hydrology and earth system sciences)
  • "In silico proof of principle of machine learning-based antibody design at unconstrained scale" (2022, mAbs)
  • "xLSTM: Extended Long Short-Term Memory" (2024, arXiv (Cornell University))
  • "Improving Few- and Zero-Shot Reaction Template Prediction Using Modern Hopfield Networks" (2022, Journal of Chemical Information and Modeling)

Best Publications

  • Long short-term memory

    Sepp Hochreiter;Jürgen Schmidhuber

  • Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)

    Djork-Arné Clevert;Thomas Unterthiner;Sepp Hochreiter

  • GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium

    Martin Heusel;Hubert Ramsauer;Thomas Unterthiner;Bernhard Nessler

  • The vanishing gradient problem during learning recurrent neural nets and problem solutions

    Sepp Hochreiter

  • Self-Normalizing Neural Networks

    Günter Klambauer;Thomas Unterthiner;Andreas Mayr;Sepp Hochreiter

  • Gradient Flow in Recurrent Nets: the Difficulty of Learning Long-Term Dependencies

    Sepp Hochreiter;Yoshua Bengio

  • DeepTox: Toxicity Prediction using Deep Learning

    Andreas Mayr;Günter Klambauer;Thomas Unterthiner;Sepp Hochreiter

  • A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium

    Zhenqiang Su;Paweł P. Łabaj;Sheng Li;Jean Thierry-Mieg

  • Untersuchungen zu dynamischen neuronalen Netzen

    Sepp Hochreiter

  • LSTM can Solve Hard Long Time Lag Problems

    Sepp Hochreiter;Jürgen Schmidhuber

  • Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning

    Frederik Kratzert;Daniel Klotz;Mathew Herrnegger;Alden Keefe Sampson

  • msa: an R package for multiple sequence alignment.

    Ulrich Bodenhofer;Enrico Bonatesta;Christoph Horejš-Kainrath;Sepp Hochreiter

  • Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets

    Frederik Kratzert;Daniel Klotz;Guy Shalev;Günter Klambauer

  • Flat minima

    Sepp Hochreiter;Jürgen Schmidhuber

  • Learning to Learn Using Gradient Descent

    Sepp Hochreiter;A. Steven Younger;Peter R. Conwell

  • DeepSynergy: predicting anti-cancer drug synergy with Deep Learning.

    Kristina Preuer;Richard P I Lewis;Sepp Hochreiter;Andreas Bender

  • Large-scale comparison of machine learning methods for drug target prediction on ChEMBL

    Andreas Mayr;Günter Klambauer;Thomas Unterthiner;Marvin Steijaert

  • APCluster: an R package for affinity propagation clustering.

    Ulrich Bodenhofer;Andreas Kothmeier;Sepp Hochreiter

  • cn.MOPS: mixture of Poissons for discovering copy number variations in next-generation sequencing data with a low false discovery rate

    Günter Klambauer;Karin Schwarzbauer;Andreas Mayr;Djork-Arné Clevert

  • FABIA: factor analysis for bicluster acquisition

    Sepp Hochreiter;Ulrich Bodenhofer;Martin Heusel;Andreas Mayr

  • DeepTox: Toxicity prediction using deep learning

    Günter Klambauer;Thomas Unterthiner;Andreas Mayr;Sepp Hochreiter

Frequent Co-Authors

Günter Klambauer
Günter Klambauer Johannes Kepler University of Linz
Klaus Obermayer
Klaus Obermayer Technical University of Berlin
Jürgen Schmidhuber
Jürgen Schmidhuber King Abdullah University of Science and Technology
Michael C. Mozer
Michael C. Mozer Google (United States)
Klaus-Robert Müller
Klaus-Robert Müller Technical University of Berlin
Andreas Bender
Andreas Bender University of Cambridge
Thomas Villmann
Thomas Villmann Hochschule Mittweida
Barbara Hammer
Barbara Hammer Bielefeld University
Joaquín Dopazo
Joaquín Dopazo Institute of Biomedicine of Seville
Christopher E. Mason
Christopher E. Mason Cornell University

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