09081 Abstracts Collection Similarity-based learning on structures  Dagstuhl Seminar  Michael Biehl1 , Barbara Hammer2 , Sepp Hochreiter3 , Stefan C. Kremer4 and Thomas Villmann5

Source URL: www.bioinf.jku.at

• Computational biology
• Sepp Hochreiter
• Geostatistics
• Kernel method
• Bioinformatics
• Machine learning
• Structured prediction
• Similarity measure
• Quantitative structureactivity relationship
• Pattern recognition

LSTM CAN SOLVE HARD LONG TIME LAG PROBLEMS Sepp Hochreiter Fakultat fur Informatik Technische Universitat Munchen

Source URL: www.bioinf.jku.at

• Artificial neural networks
• Long short-term memory
• Recurrent neural network
• Sepp Hochreiter
• Backpropagation
• Artificial intelligence
• Pattern recognition
• Time delay neural network
• Computer
• Deep learning
• Types of artificial neural networks

Classification and Feature Selection on Matrix Data with Application to Gene-Expression Analysis Sepp Hochreiter Klaus Obermayer Fakult¨at f¨

Source URL: www.bioinf.jku.at

• Linear algebra
• Vectors
• Numerical linear algebra
• Statistical classification
• Support vector machine
• Euclidean vector
• Basis
• Kernel
• Vector space
• Isotope lists
• 73-96

FLAT MINIMUM SEARCH FINDS SIMPLE NETS Technical Report FKISepp Hochreiter Jurgen Schmidhuber

Source URL: www.bioinf.jku.at

• Estimation theory
• Mean squared error
• Test set
• Generalization error
• Prediction
• Sepp Hochreiter

CNV detection from exome sequencing data using a generative probabilistic model Günter Klambauer and Sepp Hochreiter Next generation sequencing (NGS) has emerged to one of the key technologies for analyzing genome varia

Source URL: www.bioinf.jku.at

• Molecular biology
• Genomics
• Genetics
• DNA sequencing
• Computational biology
• Sepp Hochreiter
• Exome sequencing
• Whole genome sequencing
• Copy-number variation
• Exome
• Sequencing
• Structural variation

MONAURAL SEPARATION AND CLASSIFICATION OF NON-LINEAR TRANSFORMED INDEPENDENT SIGNALS: AN SVM PERSPECTIVE Sepp Hochreiter and Michael C. Mozer Department of Computer Science University of Colorado Boulder, CO 80309

Source URL: www.bioinf.jku.at

• Signal processing
• Statistical models
• Convex optimization
• Operations research
• Independent component analysis
• Wavelet
• Linear programming
• Support vector machine
• Matrix
• Machine learning
• Sparse approximation
• Sparse dictionary learning

A Hierarchical Neural Autoencoder for Paragraphs and Documents Jiwei Li, Minh-Thang Luong and Dan Jurafsky Computer Science Department, Stanford University, Stanford, CA 94305, USA jiweil, lmthang,

Source URL: web.stanford.edu

• Computational linguistics
• Natural language processing
• Artificial neural networks
• Computational neuroscience
• Speech recognition
• Deep learning
• Long short-term memory
• Automatic summarization
• Sepp Hochreiter
• Parsing
• Feature learning
• N-gram

FLAT MINIMA Neural Computation 9(1):1{Sepp Hochreiter Fakultat fur Informatik Technische Universitat Munchen

Source URL: www.bioinf.jku.at

Workshop track - ICLRF EED -F ORWARD N ETWORKS WITH ATTENTION C AN S OLVE S OME L ONG -T ERM M EMORY P ROBLEMS Daniel P. W. Ellis LabROSA, Columbia University

Source URL: colinraffel.com

• Artificial neural networks
• Computational neuroscience
• Artificial intelligence
• Computational statistics
• Deep learning
• Recurrent neural network
• Speech recognition
• Sepp Hochreiter
• Long short-term memory
• Yoshua Bengio
• Backpropagation through time
• Memory

LONG SHORT-TERM MEMORY Technical Report FKI, Version 3.0 Sepp Hochreiter Fakultat fur Informatik Technische Universitat Munchen

Source URL: www.bioinf.jku.at

• Artificial neural networks
• Long short-term memory
• Sepp Hochreiter
• Backpropagation through time
• Backpropagation
• Artificial neuron
• Artificial intelligence
• Control theory
• Memory cell
• Recurrent neural network
• Deep learning