Sepp Hochreiter

Jürgen Schmidhuber, Sepp Hochreiter, S. Hochreiter, 1999, ISCAS'99. Proceedings of the 1999 IEEE International Symposium on Circuits and Systems VLSI (Cat. No.99CH36349).

Untersuchungen zu dynamischen neuronalen Netzen

Sepp Hochreiter, Sepp Hochreiter, 1991 .

09081 Summary - Similarity-based learning on structures

Thomas Villmann, Michael Biehl, Barbara Hammer, 2009, Similarity-based learning on structures.

P-SVM Variable Selection for Discovering Dependencies Between Genetic and Brain Imaging Data

Klaus Obermayer, Andreas Heinz, Jana Wrase, 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

Visual Scene Understanding for Autonomous Driving Using Semantic Segmentation

Sepp Hochreiter, Günter Klambauer, Markus Hofmarcher, 2019, Explainable AI.

Rectified factor networks for biclustering of omics data

Sepp Hochreiter, Djork-Arné Clevert, Gundula Povysil, 2017, Bioinform..

Nonlinear Feature Selection with the Potential Support Vector Machine

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

Bioinformatics Research and Development, First International Conference, BIRD 2007, Berlin, Germany, March 12-14, 2007, Proceedings

Roland Wagner, Sepp Hochreiter, 2007, BIRD.

A new summarization method for affymetrix probe level data

Klaus Obermayer, Sepp Hochreiter, Djork-Arné Clevert, 2006, Bioinform..

Quantum Optical Experiments Modeled by Long Short-Term Memory pdf

Mario Krenn, Sepp Hochreiter, Thomas Adler, 2019, Photonics.

Hopfield Networks is All You Need pdf

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

Modern Hopfield Networks and Attention for Immune Repertoire Classification

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

Modeling Position Specificity in Sequence Kernels by Fuzzy Equivalence Relations

Ulrich Bodenhofer, Sepp Hochreiter, Mihaela Ionescu, 2009, IFSA/EUSFLAT Conf..

Gene Selection for Microarray Data

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

DeepSynergy: predicting anti-cancer drug synergy with Deep Learning

Andreas Bender, Sepp Hochreiter, Günter Klambauer, 2017, Bioinform..

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

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

Benchmarking a Catchment-Aware Long Short-Term Memory Network (LSTM) for Large-Scale Hydrological Modeling pdf

Sepp Hochreiter, Frederik Kratzert, Daniel Klotz, 2019, ArXiv.

Monaural Separation and Classification of Mixed Signals : a Support-vector Regression Perspective

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

Patch Refinement - Localized 3D Object Detection pdf

Sepp Hochreiter, Thomas Adler, Andreas Mitterecker, 2019, ArXiv.

Low-Complexity Coding and Decoding

Sepp Hochreiter, S. Hochreiter, 1997 .

Source Separation as a By-Product of Regularization

Jürgen Schmidhuber, Sepp Hochreiter, S. Hochreiter, 1998, NIPS.

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..

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).

Human-level Protein Localization with Convolutional Neural Networks

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

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

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

Fréchet ChemblNet Distance: A metric for generative models for molecules pdf

Sepp Hochreiter, Günter Klambauer, Philipp Renz, 2018, ArXiv.

Cross-Domain Few-Shot Learning by Representation Fusion pdf

Sepp Hochreiter, Thomas Adler, Andreas Mayr, 2020, ArXiv.

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

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

Self-Normalizing Neural Networks pdf

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

Furby: fuzzy force-directed bicluster visualization

Marc Streit, Sepp Hochreiter, Andreas Mayr, 2014, BMC Bioinformatics.

First Order Generative Adversarial Networks pdf

Sepp Hochreiter, Nikolay Jetchev, Urs Bergmann, 2018, ICML.

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

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

Coulomb Classifiers: Generalizing Support Vector Machines via an Analogy to Electrostatic Systems

Klaus Obermayer, Michael C. Mozer, Sepp Hochreiter, 2002, NIPS.

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

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

Two Time-Scale Update Rule for Generative Adversarial Nets

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

Learning to Learn Using Gradient Descent

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

RUDDER: Return Decomposition for Delayed Rewards pdf

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

Similarity-based learning on structures

Thomas Villmann, Michael Biehl, Barbara Hammer, 2009 .

Simplifying Neural Nets by Discovering Flat Minima

Jürgen Schmidhuber, Sepp Hochreiter, S. Hochreiter, 1994, NIPS.

Feature Extraction Through LOCOCODE

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

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

Sepp Hochreiter, Frederik Kratzert, Daniel Klotz, 2019 .

Filtering data from high-throughput experiments based on measurement reliability

Adetayo Kasim, Ziv Shkedy, Hinrich W. H. Göhlmann, 2010, Proceedings of the National Academy of Sciences.

Feature Selection and Classification on Matrix Data: From Large Margins to Small Covering Numbers

Klaus Obermayer, Sepp Hochreiter, S. Hochreiter, 2002, NIPS.

Long Short-Term Memory

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

09081 Abstracts Collection - Similarity-based learning on structures

Thomas Villmann, Michael Biehl, Barbara Hammer, 2009, Similarity-based learning on structures.

LSTM can Solve Hard Long Time Lag Problems

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

Rectified Factor Networks for Biclustering

Sepp Hochreiter, Djork-Arné Clevert, Thomas Unterthiner, 2017 .

Interpretable Deep Learning in Drug Discovery pdf

Friedrich Rippmann, Sepp Hochreiter, Günter Klambauer, 2019, Explainable AI.

On failure modes in molecule generation and optimization.

Sepp Hochreiter, Günter Klambauer, Philipp Renz, 2019, Drug discovery today. Technologies.

APCluster: an R package for affinity propagation clustering

Ulrich Bodenhofer, Sepp Hochreiter, Andreas Kothmeier, 2011, Bioinform..

Detecting cutaneous basal cell carcinomas in ultra-high resolution and weakly labelled histopathological images pdf

Sepp Hochreiter, Günter Klambauer, Markus Hofmarcher, 2019, ArXiv.

Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields pdf

Sepp Hochreiter, Martin Heusel, Gunter Klambauer, 2017, ICLR.

Posterior Sampling: Make Reinforcement Learning Sample Efficient Again

Roland Vollgraf, Sepp Hochreiter, Urs Bergmann, 2019 .

A Discrete Probabilistic Memory Model for Discovering Dependencies in Time

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

I/NI-calls for the exclusion of non-informative genes: a highly effective filtering tool for microarray data

Hinrich W. H. Göhlmann, Luc Bijnens, Willem Talloen, 2007, Bioinform..

Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution pdf

Sepp Hochreiter, Markus Hofmarcher, Matthias Dorfer, 2020, ArXiv.

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.

MC-LSTM: Mass-Conserving LSTM

Sepp Hochreiter, Frederik Kratzert, Daniel Klotz, 2021, ICML.

CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP pdf

Sepp Hochreiter, Elisabeth Rumetshofer, Angela Bitto-Nemling, 2021, ArXiv.

Boundary Graph Neural Networks for 3D Simulations pdf

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

Learning 3D Granular Flow Simulations pdf

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

The Promise of AI for DILI Prediction

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

Understanding the Effects of Dataset Characteristics on Offline Reinforcement Learning pdf

Sepp Hochreiter, Markus Hofmarcher, Philipp Renz, 2021, ArXiv.

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

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

Large-scale river network modeling using Graph Neural Networks

Sepp Hochreiter, Martin Gauch, Frederik Kratzert, 2021 .

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

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

Modern Hopfield Networks

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

Trusted Artificial Intelligence: Towards Certification of Machine Learning Applications pdf

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

CDCEO'21 - First Workshop on Complex Data Challenges in Earth Observation

Aleksandra Gruca, Pedram Ghamisi, Sepp Hochreiter, 2021, CIKM.

FABIA: factor analysis for bicluster acquisition

Ulrich Bodenhofer, Adetayo Kasim, Ziv Shkedy, 2010 .

An electric field approach to independent component analysis

Michael C. Mozer, Sepp Hochreiter, S. Hochreiter, 2000 .

Low-Complexity Coding and DecodingSepp

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

Informative or Noninformative Calls for Gene Expression: A Latent Variable Approach

Adetayo Kasim, Ziv Shkedy, Hinrich W. H. Göhlmann, 2010, Statistical applications in genetics and molecular biology.

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

Sepp Hochreiter, Frederik Kratzert, Daniel Klotz, 2020 .

δ-Clustering of Monotone Profiles

Adetayo Kasim, Suzy Van Sanden, Willem Talloen, 2012 .

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

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

IBD Sharing between Africans, Neandertals, and Denisovans

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

cn.FARMS: a latent variable model to detect copy number variations in microarray data with a low false discovery rate

Willem Talloen, Sepp Hochreiter, An De Bondt, 2011, Nucleic acids research.

A glimpse into the Unobserved: Runoff simulation for ungauged catchments with LSTMs

Sepp Hochreiter, Frederik Kratzert, Daniel Klotz, 2018 .

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

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

Repurposed high-throughput images enable biological activity prediction for drug discovery

Anne E Carpenter, Yves Moreau, Anne E. Carpenter, 2017, bioRxiv.

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

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

Sequence classificatio n for protein analysis

Klaus Obermayer, Sepp Hochreiter, S. Hochreiter, 2005 .

Detection of Nonlinear Effects in Gene Expression Pathways

Sepp Hochreiter, Andreas Mayr, Djork-Arné Clevert, 2010, Nature Precedings.

Classification, Regression, and Feature Selection

Klaus Obermayer, Sepp Hochreiter, Revised December, 2004 .