S. Hochreiter

发表

Long Short-Term Memory

Jürgen Schmidhuber, Sepp Hochreiter, S. Hochreiter, 1997, Neural Computation.

REINFORCEMENT DRIVEN INFORMATION ACQUISITION IN NONDETERMINISTIC ENVIRONMENTS

S. Hochreiter, Jan Storck, 1995 .

RUDDER: Return Decomposition for Delayed Rewards pdf

Sepp Hochreiter, Michael Widrich, Thomas Unterthiner, 2018, NeurIPS.

Recurrent Neural Net Learning and Vanishing

S. Hochreiter, 1998 .

The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions

Sepp Hochreiter, S. Hochreiter, 1998, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

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

Sepp Hochreiter, Djork-Arné Clevert, Thomas Unterthiner, 2015, ICLR.

EXPONENTIAL LINEAR UNITS (ELUS)

S. Hochreiter, Thomas Unterthiner, 2016 .

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

Yoshua Bengio, Sepp Hochreiter, S. Hochreiter, 2001 .

Flat Minima

Jürgen Schmidhuber, Sepp Hochreiter, S. Hochreiter, 1997, Neural Computation.

Learning to Learn Using Gradient Descent

Sepp Hochreiter, A. Steven Younger, Peter R. Conwell, 2001, ICANN.

Meta-learning with backpropagation

Sepp Hochreiter, A. S. Younger, P. R. Conwell, 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

Fast model-based protein homology detection without alignment

Klaus Obermayer, Sepp Hochreiter, Martin Heusel, 2007, Bioinform..

EVALUATING LONG-TERM DEPENDENCYBENCHMARK PROBLEMS BY RANDOM GUESSINGJ

S. Hochreiter, Yoshua Bengio, urgen Schmidhuber, 2001 .

LSTM can Solve Hard Long Time Lag Problems

Jürgen Schmidhuber, Sepp Hochreiter, S. Hochreiter, 1996, NIPS.

Lococode Performs Nonlinear ICA Without Knowing The Number Of Sources

S. Hochreiter, J. Schmidhuber, 1999 .

Feature Extraction Through LOCOCODE

Jürgen Schmidhuber, Sepp Hochreiter, S. Hochreiter, 1999, Neural Computation.

Convergence Proof for Actor-Critic Methods Applied to PPO and RUDDER pdf

Sepp Hochreiter, Lukas Gruber, Jose A. Arjona-Medina, 2020, Trans. Large Scale Data Knowl. Centered Syst..

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

Sepp Hochreiter, Martin Heusel, Bernhard Nessler, 2017, NIPS.

Guessing can Outperform Many Long Time Lag Algorithms

Sepp Hochreiter, Juergen Schmidhuber, S. Hochreiter, 1996 .

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

David P. Kreil, Todd M. Smith, Paweł P. Łabaj, 2014, Nature Biotechnology.

The Promise of AI for DILI Prediction

Jiye Shi, Sepp Hochreiter, Günter Klambauer, 2021, Frontiers in Artificial Intelligence.

Exploiting the Japanese Toxicogenomics Project for Predictive Modelling of Drug Toxicity

S. Hochreiter, Djork-Arné Clevert, Martin Heusel, 2012 .

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

S. Hochreiter, Djork-Arné Clevert, G. Klambauer, 2012, Nucleic acids research.

Rectified Factor Networks pdf

Sepp Hochreiter, Andreas Mayr, Djork-Arné Clevert, 2015, NIPS.

HapFABIA: Identification of very short segments of identity by descent characterized by rare variants in large sequencing data

S. Hochreiter, 2013, Nucleic acids research.

Using transcriptomics to guide lead optimization in drug discovery projects: Lessons learned from the QSTAR project.

Bie M. P. Verbist, S. Hochreiter, A. Bender, 2015, Drug discovery today.

The surprising efficiency of framing geo-spatial time series forecasting as a video prediction task - Insights from the IARAI Traffic4cast Competition at NeurIPS 2019

Raia Hadsell, Aleksandra Gruca, David Jonietz, 2020, NeurIPS.

DeepTox: Toxicity Prediction using Deep Learning

Günter Klambauer, Andreas Mayr, Sepp Hochreiter, 2016, Front. Environ. Sci..

DeepTox: Toxicity prediction using deep learning

S. Hochreiter, Thomas Unterthiner, G. Klambauer, 2017 .

Repurposing High-Throughput Image Assays Enables Biological Activity Prediction for Drug Discovery.

Anne E Carpenter, Yolanda T. Chong, S. Hochreiter, 2018, Cell Chemical Biology.

Self-Normalizing Neural Networks pdf

Sepp Hochreiter, Günter Klambauer, Andreas Mayr, 2017, NIPS.

Toxicity Prediction using Deep Learning pdf

Sepp Hochreiter, Günter Klambauer, Andreas Mayr, 2015, ArXiv.

Assessing technical performance in differential gene expression experiments with external spike-in RNA control ratio mixtures.

David P. Kreil, Paweł P. Łabaj, Sarah A. Munro, 2014, Nature communications.

Modern Hopfield Networks and Attention for Immune Repertoire Classification

Geir Kjetil Sandve, Sepp Hochreiter, Victor Greiff, 2020, bioRxiv.

Coulomb Classi ers: Reinterpreting SVMs as Electrostatic Systems

S. Hochreiter, M. Mozer, .

msa: an R package for multiple sequence alignment

Ulrich Bodenhofer, Sepp Hochreiter, Enrico Bonatesta, 2015, Bioinform..

Artificial neural networks and pathologists recognize basal cell carcinomas based on different histological patterns

Martin Kaltenbrunner, Sepp Hochreiter, Harald Kindermann, 2020, Modern Pathology.

Nonlinear Feature Selection with the Potential Support Vector Machine

Klaus Obermayer, Sepp Hochreiter, S. Hochreiter, 2006, Feature Extraction.

Classification, Regression, and Feature Selection on Matrix Data

S. Hochreiter, K. Obermayer, 2004 .

A Two Time-Scale Update Rule Ensuring Convergence of Episodic Reinforcement Learning Algorithms at the Example of RUDDER

Jose A. Arjona-Medina, S. Hochreiter, Markus Holzleitner, 2022 .

VERY DEEP NETWORKS VIA VOLUME CONSERVATION

S. Hochreiter, Thomas Unterthiner, 2016 .

IBD Sharing between Africans, Neandertals, and Denisovans

Sepp Hochreiter, Gundula Povysil, S. Hochreiter, 2016, Genome biology and evolution.

Sharing of Very Short IBD Segments between Humans, Neandertals, and Denisovans

Sepp Hochreiter, Gundula Povysil, S. Hochreiter, 2014 .

In silico proof of principle of machine learning-based antibody design at unconstrained scale

Cédric R. Weber, Ingrid Hobæk Haff, S. Hochreiter, 2021, bioRxiv.

Rectified Factor Networks and Dropout

S. Hochreiter, Thomas Unterthiner, 2014 .

Characterising activation functions by their backward dynamics around forward fixed points

S. Hochreiter, G. Klambauer, Pieter-Jan Hoedt, 2020 .

A GAN based solver of black-box inverse problems

S. Hochreiter, Michael Gillhofer, Johannes Brandstetter, 2019 .

DEXUS: identifying differential expression in RNA-Seq studies with unknown conditions

S. Hochreiter, Thomas Unterthiner, G. Klambauer, 2013, Nucleic acids research.

On Failure Modes of Molecule Generators and Optimizers

S. Hochreiter, J. Wegner, G. Klambauer, 2020 .

On Failure Modes of Molecule Generators and Optimizers

S. Hochreiter, J. Wegner, G. Klambauer, 2020 .

Feature extraction through LOCOCODENeural Computation

S. Hochreiter, 2013 .

Gene Selection on Micro Array Data through Support Vector Machines

S. Hochreiter, K. Obermayer, 2013 .

Classification, Regression, and Feature Selection

Klaus Obermayer, Sepp Hochreiter, Revised December, 2004 .

panelcn.MOPS: Copy‐number detection in targeted NGS panel data for clinical diagnostics

Sepp Hochreiter, Günter Klambauer, Verena Haunschmid, 2017, Human mutation.

Low-Complexity Coding and DecodingSepp

Sepp Hochreiter, S. Hochreiter, urgen Schmidhuber, 2013 .

KeBABS: an R package for kernel-based analysis of biological sequences

Ulrich Bodenhofer, Sepp Hochreiter, Johannes Palme, 2015, Bioinform..

Complex Networks Govern Coiled-Coil Oligomerization – Predicting and Profiling by Means of a Machine Learning Approach*

Ingrid G. Abfalter, Carsten C. Mahrenholz, S. Hochreiter, 2011, Molecular & Cellular Proteomics.

Report from Dagstuhl Seminar 13212 Computational Methods Aiding Early-Stage Drug Design

S. Hochreiter, A. Bender, Z. Shkedy, 2013 .

Genome-Wide Chromatin Remodeling Identified at GC-Rich Long Nucleosome-Free Regions

S. Hochreiter, Karin Schwarzbauer, Ulrich Bodenhofer, 2012, PloS one.

Potential Support Vector Machines for Dyadic Data

S. Hochreiter, K. Obermayer, 2013 .

Sphered Support Vector Machine

S. Hochreiter, K. Obermayer, J. Mohr, 2012 .

Sequence Classification For Protein Analysis

S. Hochreiter, K. Obermayer, 2013 .

Optimal kernels for unsupervised learning

S. Hochreiter, K. Obermayer, S. Hochreiter, 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

Human-level Protein Localization with Convolutional Neural Networks

Sepp Hochreiter, Günter Klambauer, Markus Hofmarcher, 2018, ICLR.

Low-Complexity Coding and Decoding

Sepp Hochreiter, S. Hochreiter, 1997 .

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

Sepp Hochreiter, Martin Heusel, Günter Klambauer, 2017, ArXiv.

Modern Hopfield Networks for Few- and Zero-Shot Reaction Prediction pdf

Sepp Hochreiter, Philipp Renz, S. Hochreiter, 2021, ArXiv.

Learning from mistakes: Online updating for deep learning models.

Sepp Hochreiter, Frederik Kratzert, Grey Nearing, 2020 .

Trusted Artificial Intelligence: Towards Certification of Machine Learning Applications pdf

Sepp Hochreiter, Bernhard Nessler, Johannes Weissenbock, 2021, ArXiv.

Modern Hopfield Networks

Sepp Hochreiter, S. Hochreiter, 2021, Proceedings of the 7th World Congress on Electrical Engineering and Computer Systems and Science.

Learning 3D Granular Flow Simulations pdf

Sepp Hochreiter, Andreas Mayr, Christoph Kloss, 2021, ArXiv.

Do internals of neural networks make sense in the context of hydrology

Sepp Hochreiter, Frederik Kratzert, Daniel Klotz, 2018 .

Prediction in Ungauged Basins with Long Short-Term 1 Memory Networks

S. Hochreiter, G. Nearing, D. Klotz, 2019 .

Towards Improved Predictions in Ungauged Basins:

S. Hochreiter, G. Nearing, D. Klotz, 2019 .

Boundary Graph Neural Networks for 3D Simulations pdf

Sepp Hochreiter, Andreas Mayr, Christoph Kloss, 2021, ArXiv.

Modern Hopfield Networks for Few- and Zero-Shot Reaction Template Prediction

Marwin H. S. Segler, Sepp Hochreiter, Philipp Renz, 2021, 2104.03279.

Gene Selection for Microarray Data

Klaus Obermayer, Sepp Hochreiter, S. Hochreiter, 2004 .

Recurrent Neural Net Learning and Vanishing

S. Hochreiter, 1998 .

A Machine Learner’s Guide to Streamflow Prediction

S. Hochreiter, G. Nearing, D. Klotz, 2020 .

Rchemcpp: a web service for structural analoging in ChEMBL, Drugbank and the Connectivity Map

Sepp Hochreiter, Günter Klambauer, Andreas Mayr, 2015, Bioinform..

Machine learning–based prediction of transfusion

S. Hochreiter, Andreas Mitterecker, Karin Schwarzbauer, 2020, Transfusion.

Uncertainty Estimation with Deep Learning for Rainfall–Runoff Modelling

S. Hochreiter, Johannes Brandstetter, G. Klambauer, 2020 .

Machine Learning in Drug Discovery

Matthias Rarey, Sepp Hochreiter, Guenter Klambauer, 2018, J. Chem. Inf. Model..

Few-Shot Learning by Dimensionality Reduction in Gradient Space

S. Hochreiter, Markus Holzleitner, Johannes Brandstetter, 2022, 2206.03483.

Unsupervised coding with LococodeIn

S. Hochreiter, 2013 .

Hopular: Modern Hopfield Networks for Tabular Data

S. Hochreiter, Lukas Gruber, Bernhard Schäfl, 2022, ArXiv.

Entangled Residual Mappings

Ramin M. Hasani, S. Hochreiter, T. Henzinger, 2022, ArXiv.

A note on leveraging synergy in multiple meteorological datasets with deep learning for regional rainfall-runoff modeling

S. Hochreiter, G. Nearing, D. Klotz, 2020 .

A note on leveraging synergy in multiple meteorological data sets with deep learning for rainfall–runoff modeling

S. Hochreiter, G. Nearing, D. Klotz, 2020, Hydrology and Earth System Sciences.

Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution

Jose A. Arjona-Medina, Vihang P. Patil, S. Hochreiter, 2020, ArXiv.

History Compression via Language Models in Reinforcement Learning

Vihang P. Patil, S. Hochreiter, Markus Holzleitner, 2022, ArXiv.

C ONTRASTIVE LEARNING OF IMAGE - AND STRUCTURE BASED REPRESENTATIONS IN DRUG DISCOVERY

S. Hochreiter, Elisabeth Rumetshofer, Ana Sánchez-Fernández, 2022 .

Boundary Graph Neural Networks for 3D Simulations

S. Hochreiter, Johannes Brandstetter, Andreas Mayr, 2021, ArXiv.

Reactive Exploration to Cope with Non-Stationarity in Lifelong Reinforcement Learning

Vihang P. Patil, S. Hochreiter, Hamid Eghbal-zadeh, 2022, 2207.05742.

CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP

David P. Kreil, S. Hochreiter, Hubert Ramsauer, 2021, ArXiv.

R EACTIVE E XPLORATION TO C OPE WITH N ON -S TATIONARITY IN L IFELONG R EINFORCEMENT L EARNING

Vihang P. Patil, S. Hochreiter, Hamid Eghbal-zadeh, 2022 .

Improving Few- and Zero-Shot Reaction Template Prediction Using Modern Hopfield Networks

Marwin H. S. Segler, S. Hochreiter, J. Wegner, 2022, J. Chem. Inf. Model..

Sequence analysis msa : an R package for multiple sequence alignment

S. Hochreiter, Ulrich Bodenhofer, Enrico Bonatesta, 2015 .

Coulomb Classifiers: Reinterpreting SVMs as Electrostatic Systems ; CU-CS-921-01

Michael C. Mozer, Sepp Hochreiter, S. Hochreiter, 2001 .

Txt2Img-MHN: Remote Sensing Image Generation from Text Using Modern Hopfield Networks

S. Hochreiter, Michael Kopp, Pedram Ghamisi, 2022, ArXiv.

History Compression via Language Models in Reinforcement Learning

Vihang P. Patil, S. Hochreiter, Markus Holzleitner, 2022, ICML.

Reactive Exploration to Cope with Non-Stationarity in Lifelong Reinforcement Learning

Vihang P. Patil, S. Hochreiter, Hamid Eghbal-zadeh, 2022, ArXiv.

A Dataset Perspective on Offline Reinforcement Learning

Vihang P. Patil, S. Hochreiter, Hamid Eghbal-zadeh, 2021, 2111.04714.

CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP

David P. Kreil, S. Hochreiter, Hubert Ramsauer, 2021, ArXiv.

In Defense of Metrics: Metrics Sufficiently Encode Typical Human Preferences Regarding Hydrological Model Performance

Bryan A. Tolson, Juliane, S. Hochreiter, 2022 .

CLOOME: a new search engine unlocks bioimaging databases for queries with chemical structures

S. Hochreiter, G. Klambauer, Elisabeth Rumetshofer, 2022, bioRxiv.

Unconstrained generation of synthetic antibody–antigen structures to guide machine learning methodology for antibody specificity prediction

Ingrid Hobæk Haff, S. Hochreiter, R. Akbar, 2022, Nature Computational Science.

Machine Learning–Based Mortality Prediction of Patients at Risk During Hospital Admission

S. Hochreiter, Andreas Mitterecker, Karin Schwarzbauer, 2022, Journal of patient safety.

Hopfield Networks is All You Need

David P. Kreil, S. Hochreiter, G. K. Sandve, 2020, International Conference on Learning Representations.

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

S. Hochreiter, G. Nearing, D. Klotz, 2019 .

CLOOME: contrastive learning unlocks bioimaging databases for queries with chemical structures

S. Hochreiter, G. Klambauer, Elisabeth Rumetshofer, 2023, bioRxiv.