• Publications
  • Influence
Long Short-Term Memory
We introduce a novel, efficient, gradient based method called long short-term memory (LSTM). Expand
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
We propose a two time-scale update rule (TTUR) for training GANs with stochastic gradient descent on arbitrary GAN loss functions. Expand
Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
We introduce the "exponential linear unit" (ELU) which speeds up learning in deep neural networks and leads to higher classification accuracies. Expand
Self-Normalizing Neural Networks
We introduce self-normalizing neural networks (SNNs) to enable high-level abstract representations. Expand
Gradient Flow in Recurrent Nets: the Difficulty of Learning Long-Term Dependencies
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LSTM can Solve Hard Long Time Lag Problems
We show that problems used to promote various previous algorithms can be solved more quickly by random weight guessing than by the proposed algorithms. Expand
The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions
  • S. Hochreiter
  • Mathematics, Computer Science
  • Int. J. Uncertain. Fuzziness Knowl. Based Syst.
  • 1 April 1998
Recurrent nets are in principle capable to store past inputs to produce the currently desired output. Expand
cn.MOPS: mixture of Poissons for discovering copy number variations in next-generation sequencing data with a low false discovery rate
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 ofExpand
A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control consortium
We present primary results from the Sequencing Quality Control (SEQC) project, coordinated by the US Food and Drug Administration. Examining Illumina HiSeq, Life Technologies SOLiD and Roche 454Expand
FABIA: factor analysis for bicluster acquisition
We propose a novel generative approach for biclustering called ‘FABIA: Factor Analysis for Bicluster Acquisition’. Expand