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Long short-term memory.
Hochreiter S, Schmidhuber J. Hochreiter S, et al. Neural Comput. 1997 Nov 15;9(8):1735-80. doi: 10.1162/neco.1997.9.8.1735. Neural Comput. 1997. PMID: 9377276
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). ...
We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient-ba …
Support vector machines for dyadic data.
Hochreiter S, Obermayer K. Hochreiter S, et al. Neural Comput. 2006 Jun;18(6):1472-510. doi: 10.1162/neco.2006.18.6.1472. Neural Comput. 2006. PMID: 16764511
An SMO algorithm for the potential support vector machine.
Knebel T, Hochreiter S, Obermayer K. Knebel T, et al. Neural Comput. 2008 Jan;20(1):271-87. doi: 10.1162/neco.2008.20.1.271. Neural Comput. 2008. PMID: 18045009
In contrast to standard support vector machines (SVMs), the P-SVM is applicable to arbitrary dyadic data sets, but benchmarks are provided against libSVM's epsilon-SVR and C-SVC implementations for problems that are also solvable by standard SVM methods. ...
In contrast to standard support vector machines (SVMs), the P-SVM is applicable to arbitrary dyadic data sets, but benchmarks are provided a …
Feature extraction through LOCOCODE.
Hochreiter S, Schmidhuber J. Hochreiter S, et al. Neural Comput. 1999 Apr 1;11(3):679-714. doi: 10.1162/089976699300016629. Neural Comput. 1999. PMID: 10085426 Review.
Flat minima.
Hochreiter S, Schmidhuber J. Hochreiter S, et al. Neural Comput. 1997 Jan 1;9(1):1-42. doi: 10.1162/neco.1997.9.1.1. Neural Comput. 1997. PMID: 9117894
Although our algorithm requires the computation of second-order derivatives, it has backpropagation's order of complexity. Automatically, it effectively prunes units, weights, and input lines. ...
Although our algorithm requires the computation of second-order derivatives, it has backpropagation's order of complexity. Automatica …
Fast model-based protein homology detection without alignment.
Hochreiter S, Heusel M, Obermayer K. Hochreiter S, et al. Bioinformatics. 2007 Jul 15;23(14):1728-36. doi: 10.1093/bioinformatics/btm247. Epub 2007 May 8. Bioinformatics. 2007. PMID: 17488755
Complex networks govern coiled-coil oligomerization--predicting and profiling by means of a machine learning approach.
Mahrenholz CC, Abfalter IG, Bodenhofer U, Volkmer R, Hochreiter S. Mahrenholz CC, et al. Mol Cell Proteomics. 2011 May;10(5):M110.004994. doi: 10.1074/mcp.M110.004994. Epub 2011 Feb 10. Mol Cell Proteomics. 2011. PMID: 21311038 Free PMC article.
A new summarization method for Affymetrix probe level data.
Hochreiter S, Clevert DA, Obermayer K. Hochreiter S, et al. Bioinformatics. 2006 Apr 15;22(8):943-9. doi: 10.1093/bioinformatics/btl033. Epub 2006 Feb 10. Bioinformatics. 2006. PMID: 16473874
RESULTS: We compare FARMS on Affymetrix's spike-in and Gene Logic's dilution data to established algorithms like Affymetrix Microarray Suite (MAS) 5.0, Model Based Expression Index (MBEI), Robust Multi-array Average (RMA). ...
RESULTS: We compare FARMS on Affymetrix's spike-in and Gene Logic's dilution data to established algorithms like Affymetrix Mi …
msa: an R package for multiple sequence alignment.
Bodenhofer U, Bonatesta E, Horejš-Kainrath C, Hochreiter S. Bodenhofer U, et al. Bioinformatics. 2015 Dec 15;31(24):3997-9. doi: 10.1093/bioinformatics/btv494. Epub 2015 Aug 26. Bioinformatics. 2015. PMID: 26315911
KeBABS: an R package for kernel-based analysis of biological sequences.
Palme J, Hochreiter S, Bodenhofer U. Palme J, et al. Bioinformatics. 2015 Aug 1;31(15):2574-6. doi: 10.1093/bioinformatics/btv176. Epub 2015 Mar 25. Bioinformatics. 2015. PMID: 25812745
KeBABS provides a powerful, flexible and easy to use framework for KE: rnel- B: ased A: nalysis of B: iological S: equences in R. ...
KeBABS provides a powerful, flexible and easy to use framework for KE: rnel- B: ased A: nalysis of B: iological S: equences in R. ...
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