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- Sepp Hochreiter, Jürgen Schmidhuber
- Neural Computation
- 1997

Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient-based method called long short-term memory (LSTM). Truncating the… (More)

- Djork-Arné Clevert, Thomas Unterthiner, Sepp Hochreiter
- ArXiv
- 2015

We introduce the “exponential linear unit” (ELU) which speeds up learning in deep neural networks and leads to higher classification accuracies. Like rectified linear units (ReLUs), leaky ReLUs (LReLUs) and parametrized ReLUs (PReLUs), ELUs alleviate the vanishing gradient problem via the identity for positive values. However ELUs have improved learning… (More)

- Sepp Hochreiter
- 2001

- Sepp Hochreiter, Ulrich Bodenhofer, +11 authors Djork-Arné Clevert
- Bioinformatics
- 2010

MOTIVATION
Biclustering of transcriptomic data groups genes and samples simultaneously. It is emerging as a standard tool for extracting knowledge from gene expression measurements. We propose a novel generative approach for biclustering called 'FABIA: Factor Analysis for Bicluster Acquisition'. FABIA is based on a multiplicative model, which accounts for… (More)

- Günter Klambauer, Karin Schwarzbauer, +4 authors Sepp Hochreiter
- Nucleic acids research
- 2012

Quantitative analyses of next-generation sequencing (NGS) data, such as the detection of copy number variations (CNVs), remain challenging. Current methods detect CNVs as changes in the depth of coverage along chromosomes. Technological or genomic variations in the depth of coverage thus lead to a high false discovery rate (FDR), even upon correction for GC… (More)

- Sepp Hochreiter, Klaus Obermayer
- Neural Computation
- 2006

We describe a new technique for the analysis of dyadic data, where two sets of objects (row and column objects) are characterized by a matrix of numerical values that describe their mutual relationships. The new technique, called potential support vector machine (P-SVM), is a large-margin method for the construction of classifiers and regression functions… (More)

- Sepp Hochreiter, Djork-Arné Clevert, Klaus Obermayer
- Bioinformatics
- 2006

MOTIVATION
We propose a new model-based technique for summarizing high-density oligonucleotide array data at probe level for Affymetrix GeneChips. The new summarization method is based on a factor analysis model for which a Bayesian maximum a posteriori method optimizes the model parameters under the assumption of Gaussian measurement noise. Thereafter, the… (More)

- Sepp Hochreiter, Jürgen Schmidhuber
- Neural Computation
- 1999

Low-complexity coding and decoding (LOCOCODE) is a novel approach to sensory coding and unsupervised learning. Unlike previous methods, it explicitly takes into account the information-theoretic complexity of the code generator. It computes lococodes that convey information about the input data and can be computed and decoded by low-complexity mappings. We… (More)

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

This paper introduces the application of gradient descent methods to meta-learning. The concept of “meta-learning”, i.e. of a system that improves or discovers a learning algorithm, has been of interest in machine learning for decades because of its appealing applications. Previous meta-learning approaches have been based on evolutionary methods and,… (More)

- Sepp Hochreiter
- International Journal of Uncertainty, Fuzziness…
- 1998

Received () Revised () Recurrent nets are in principle capable to store past inputs to produce the currently desired output. Because of this property recurrent nets are used in time series prediction and process control. Practical applications involve temporal dependencies spanning many time steps, e.g. between relevant inputs and desired outputs. In this… (More)