Sepp Hochreiter

Sepp Hochreiter

Johannes Kepler Universität Linz

H-index: 58

Europe-Austria

About Sepp Hochreiter

Sepp Hochreiter, With an exceptional h-index of 58 and a recent h-index of 54 (since 2020), a distinguished researcher at Johannes Kepler Universität Linz, specializes in the field of Machine Learning, Deep Learning, Artificial Intelligence, Neural Networks, Bioinformatics.

His recent articles reflect a diverse array of research interests and contributions to the field:

Contrastive tuning: A little help to make masked autoencoders forget

HyperPCM: Robust Task-Conditioned Modeling of Drug–Target Interactions

Variational annealing on graphs for combinatorial optimization

A data-centric perspective on the information needed for hydrological uncertainty predictions

A community effort in SARS‐CoV‐2 drug discovery

Semantic HELM: A Human-Readable Memory for Reinforcement Learning

Potential Predictors for Deterioration of Renal Function After Transfusion

Conformal prediction for time series with Modern Hopfield Networks

Sepp Hochreiter Information

University

Johannes Kepler Universität Linz

Position

Institute for Machine Learning

Citations(all)

143605

Citations(since 2020)

116183

Cited By

67616

hIndex(all)

58

hIndex(since 2020)

54

i10Index(all)

143

i10Index(since 2020)

119

Email

University Profile Page

Johannes Kepler Universität Linz

Sepp Hochreiter Skills & Research Interests

Machine Learning

Deep Learning

Artificial Intelligence

Neural Networks

Bioinformatics

Top articles of Sepp Hochreiter

Title

Journal

Author(s)

Publication Date

Contrastive tuning: A little help to make masked autoencoders forget

Proceedings of the AAAI Conference on Artificial Intelligence

Johannes Lehner

Benedikt Alkin

Andreas Fürst

Elisabeth Rumetshofer

Lukas Miklautz

...

2024/3/24

HyperPCM: Robust Task-Conditioned Modeling of Drug–Target Interactions

Journal of Chemical Information and Modeling

Emma Svensson

Pieter-Jan Hoedt

Sepp Hochreiter

Günter Klambauer

2024/1/7

Variational annealing on graphs for combinatorial optimization

Advances in Neural Information Processing Systems

Sebastian Sanokowski

Wilhelm Berghammer

Sepp Hochreiter

Sebastian Lehner

2024/2/13

A data-centric perspective on the information needed for hydrological uncertainty predictions

Hydrology and Earth System Sciences Discussions

Andreas Auer

Martin Gauch

Frederik Kratzert

Grey Nearing

Sepp Hochreiter

...

2024/3/14

A community effort in SARS‐CoV‐2 drug discovery

Molecular Informatics

Johannes Schimunek

Philipp Seidl

Katarina Elez

Tim Hempel

Tuan Le

...

2024/1

Semantic HELM: A Human-Readable Memory for Reinforcement Learning

Advances in Neural Information Processing Systems

Fabian Paischer

Thomas Adler

Markus Hofmarcher

Sepp Hochreiter

2024/2/13

Potential Predictors for Deterioration of Renal Function After Transfusion

Anesthesia & Analgesia

Thomas Tschoellitsch

Philipp Moser

Alexander Maletzky

Philipp Seidl

Carl Böck

...

2024/3/1

Conformal prediction for time series with Modern Hopfield Networks

Advances in Neural Information Processing Systems

Andreas Auer

Martin Gauch

Daniel Klotz

Sepp Hochreiter

2024/2/13

Overcoming Saturation in Density Ratio Estimation by Iterated Regularization

arXiv preprint arXiv:2402.13891

Lukas Gruber

Markus Holzleitner

Johannes Lehner

Sepp Hochreiter

Werner Zellinger

2024/2/21

Learning to Modulate pre-trained Models in RL

Advances in Neural Information Processing Systems

Thomas Schmied

Markus Hofmarcher

Fabian Paischer

Razvan Pascanu

Sepp Hochreiter

2024/2/13

Geometry-Informed Neural Networks

arXiv preprint arXiv:2402.14009

Arturs Berzins

Andreas Radler

Sebastian Sanokowski

Sepp Hochreiter

Johannes Brandstetter

2024/2/21

VN-EGNN: E (3)-Equivariant Graph Neural Networks with Virtual Nodes Enhance Protein Binding Site Identification

arXiv preprint arXiv:2404.07194

Florian Sestak

Lisa Schneckenreiter

Johannes Brandstetter

Sepp Hochreiter

Andreas Mayr

...

2024/4/10

SymbolicAI: A framework for logic-based approaches combining generative models and solvers

arXiv preprint arXiv:2402.00854

Marius-Constantin Dinu

Claudiu Leoveanu-Condrei

Markus Holzleitner

Werner Zellinger

Sepp Hochreiter

2024/2/1

MIM-Refiner: A Contrastive Learning Boost from Intermediate Pre-Trained Representations

arXiv preprint arXiv:2402.10093

Benedikt Alkin

Lukas Miklautz

Sepp Hochreiter

Johannes Brandstetter

2024/2/15

SITTA: A Semantic Image-Text Alignment for Image Captioning

arXiv preprint arXiv:2307.05591

Fabian Paischer

Thomas Adler

Markus Hofmarcher

Sepp Hochreiter

2023/7/10

Addressing parameter choice issues in unsupervised domain adaptation by aggregation

arXiv preprint arXiv:2305.01281

Marius-Constantin Dinu

Markus Holzleitner

Maximilian Beck

Hoan Duc Nguyen

Andrea Huber

...

2023/5/2

G-Signatures: Global Graph Propagation With Randomized Signatures

arXiv preprint arXiv:2302.08811

Bernhard Schäfl

Lukas Gruber

Johannes Brandstetter

Sepp Hochreiter

2023/2/17

Close the Gap: Lightweight Image Captioning via Retrieval Augmentation

Fabian Paischer

Thomas Adler

Markus Hofmarcher

Sepp Hochreiter

2023/10/13

Using emergency department triage for machine learning-based admission and mortality prediction

European journal of emergency medicine: official journal of the European Society for Emergency Medicine

Thomas Tschoellitsch

Philipp Seidl

Carl Böck

Alexander Maletzky

Philipp Moser

...

2023/8

Enhancing activity prediction models in drug discovery with the ability to understand human language

Philipp Seidl

Andreu Vall

Sepp Hochreiter

Günter Klambauer

2023/7/3

See List of Professors in Sepp Hochreiter University(Johannes Kepler Universität Linz)

Sepp Hochreiter FAQs

What is Sepp Hochreiter's h-index at Johannes Kepler Universität Linz?

The h-index of Sepp Hochreiter has been 54 since 2020 and 58 in total.

What are Sepp Hochreiter's top articles?

The articles with the titles of

Contrastive tuning: A little help to make masked autoencoders forget

HyperPCM: Robust Task-Conditioned Modeling of Drug–Target Interactions

Variational annealing on graphs for combinatorial optimization

A data-centric perspective on the information needed for hydrological uncertainty predictions

A community effort in SARS‐CoV‐2 drug discovery

Semantic HELM: A Human-Readable Memory for Reinforcement Learning

Potential Predictors for Deterioration of Renal Function After Transfusion

Conformal prediction for time series with Modern Hopfield Networks

...

are the top articles of Sepp Hochreiter at Johannes Kepler Universität Linz.

What are Sepp Hochreiter's research interests?

The research interests of Sepp Hochreiter are: Machine Learning, Deep Learning, Artificial Intelligence, Neural Networks, Bioinformatics

What is Sepp Hochreiter's total number of citations?

Sepp Hochreiter has 143,605 citations in total.