Sepp Hochreiter

2008
22	EE	Tilman Knebel, Sepp Hochreiter, Klaus Obermayer: An SMO Algorithm for the Potential Support Vector Machine. Neural Computation 20(1): 271-287 (2008)
2007
21		Sepp Hochreiter, Roland Wagner: Bioinformatics Research and Development, First International Conference, BIRD 2007, Berlin, Germany, March 12-14, 2007, Proceedings Springer 2007
20	EE	Steffen Grünewälder, Sepp Hochreiter, Klaus Obermayer: Optimality of LSTD and its Relation to MC. IJCNN 2007: 338-343
19	EE	Sepp Hochreiter, Martin Heusel, Klaus Obermayer: Fast model-based protein homology detection without alignment. Bioinformatics 23(14): 1728-1736 (2007)
18	EE	Willem Talloen, Djork-Arné Clevert, Sepp Hochreiter, Dhammika Amaratunga, Luc Bijnens, Stefan Kass, Hinrich W. H. Göhlmann: I/NI-calls for the exclusion of non-informative genes: a highly effective filtering tool for microarray data. Bioinformatics 23(21): 2897-2902 (2007)
2006
17	EE	Johannes Mohr, Imke Puis, Jana Wrase, Sepp Hochreiter, Andreas Heinz, Klaus Obermayer: P-SVM Variable Selection for Discovering Dependencies Between Genetic and Brain Imaging Data. IJCNN 2006: 5020-5027
16	EE	Sepp Hochreiter, Djork-Arné Clevert, Klaus Obermayer: A new summarization method for affymetrix probe level data. Bioinformatics 22(8): 943-949 (2006)
15	EE	Sepp Hochreiter, Klaus Obermayer: Support Vector Machines for Dyadic Data. Neural Computation 18(6): 1472-1510 (2006)
2002
14	EE	Sepp Hochreiter, Michael Mozer, Klaus Obermayer: Coulomb Classifiers: Generalizing Support Vector Machines via an Analogy to Electrostatic Systems. NIPS 2002: 545-552
13	EE	Sepp Hochreiter, Klaus Obermayer: Feature Selection and Classification on Matrix Data: From Large Margins to Small Covering Numbers. NIPS 2002: 889-896
2001
12	EE	Sepp Hochreiter, Michael Mozer: A Discrete Probabilistic Memory Model for Discovering Dependencies in Time. ICANN 2001: 661-668
11	EE	Sepp Hochreiter, A. Steven Younger, Peter R. Conwell: Learning to Learn Using Gradient Descent. ICANN 2001: 87-94
2000
10		Sepp Hochreiter, Michael Mozer: Beyond Maximum Likelihood and Density Estimation: A Sample-Based Criterion for Unsupervised Learning of Complex Models. NIPS 2000: 535-541
1999
9	EE	Sepp Hochreiter, Jürgen Schmidhuber: Nonlinear ICA through low-complexity autoencoders. ISCAS (5) 1999: 53-56
8		Sepp Hochreiter, Jürgen Schmidhuber: Feature Extraction Through LOCOCODE. Neural Computation 11(3): 679-714 (1999)
1998
7	EE	Sepp Hochreiter, Jürgen Schmidhuber: Source Separation as a By-Product of Regularization. NIPS 1998: 459-465
6	EE	Sepp Hochreiter: The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 6(2): 107-116 (1998)
1997
5		Sepp Hochreiter, Jürgen Schmidhuber: Unsupervised Coding with LOCOCODE. ICANN 1997: 655-660
4	EE	Sepp Hochreiter, Jürgen Schmidhuber: Flat Minima Neural Computation 9(1): 1-42 (1997)
3	EE	Sepp Hochreiter, Jürgen Schmidhuber: Long Short-Term Memory. Neural Computation 9(8): 1735-1780 (1997)
1996
2	EE	Sepp Hochreiter, Jürgen Schmidhuber: LSTM can Solve Hard Long Time Lag Problems. NIPS 1996: 473-479
1994
1	EE	Sepp Hochreiter, Jürgen Schmidhuber: Simplifying Neural Nets by Discovering Flat Minima. NIPS 1994: 529-536

Coauthor Index

1	Dhammika Amaratunga	[18]
2	Luc Bijnens	[18]
3	Djork-Arné Clevert	[16] [18]
4	Peter R. Conwell	[11]
5	Hinrich W. H. Göhlmann	[18]
6	Steffen Grünewälder	[20]
7	Andreas Heinz	[17]
8	Martin Heusel	[19]
9	Stefan Kass	[18]
10	Tilman Knebel	[22]
11	Johannes Mohr	[17]
12	Michael C. Mozer (Michael Mozer)	[10] [12] [14]
13	Klaus Obermayer	[13] [14] [15] [16] [17] [19] [20] [22]
14	Imke Puis	[17]
15	Jürgen Schmidhuber	[1] [2] [3] [4] [5] [7] [8] [9]
16	Willem Talloen	[18]
17	Roland Wagner	[21]
18	Jana Wrase	[17]
19	A. Steven Younger	[11]

Colors in the list of coauthors