Nihat Ay

	2011
21		Guido Montufar, Johannes Rauh, Nihat Ay: Expressive Power and Approximation Errors of Restricted Boltzmann Machines. NIPS 2011: 415-423
20		Johannes Rauh, Thomas Kahle, Nihat Ay: Support sets in exponential families and oriented matroid theory. Int. J. Approx. Reasoning 52(5): 613-626 (2011)
19		Guido Montufar, Nihat Ay: Refinements of Universal Approximation Results for Deep Belief Networks and Restricted Boltzmann Machines. Neural Computation 23(5): 1306-1319 (2011)
	2010
18		Nihat Ay, Markus Müller, Arleta Szkola: Effective complexity of stationary process realizations CoRR abs/1001.2686: (2010)
17		Bastian Steudel, Nihat Ay: Information-theoretic inference of common ancestors CoRR abs/1010.5720: (2010)
16		Nihat Ay, Markus Müller, Arleta Szkola: Effective complexity and its relation to logical depth. IEEE Transactions on Information Theory 56(9): 4593-4607 (2010)
	2009
15		Wolfgang Löhr, Nihat Ay: Non-sufficient Memories That Are Sufficient for Prediction. Complex (1) 2009: 265-276
14		Wolfgang Löhr, Nihat Ay: On the Generative Nature of Prediction. Advances in Complex Systems 12(2): 169-194 (2009)
13		Keyan Zahedi, Nihat Ay, Ralf Der: Higher coordination with less control - A result of information maximisation in the sensori-motor loop CoRR abs/0910.2039: (2009)
12		Nihat Ay: A refinement of the common cause principle. Discrete Applied Mathematics 157(10): 2439-2457 (2009)
	2008
11		Nihat Ay, Daniel Polani: Information Flows in Causal Networks. Advances in Complex Systems 11(1): 17-41 (2008)
10		Nils Bertschinger, Eckehard Olbrich, Nihat Ay, Jürgen Jost: Autonomy: An information theoretic perspective. Biosystems 91(2): 331-345 (2008)
9		Nihat Ay, Markus Müller, Arleta Szkola: Effective Complexity and its Relation to Logical Depth CoRR abs/0810.5663: (2008)
	2007
8		Thomas Wennekers, Nihat Ay, Péter András: High-resolution multiple-unit EEG in cat auditory cortex reveals large spatio-temporal stochastic interactions. Biosystems 89(1-3): 190-197 (2007)
	2006
7		Thomas Wennekers, Nihat Ay: A temporal learning rule in recurrent systems supports high spatio-temporal stochastic interactions. Neurocomputing 69(10-12): 1199-1202 (2006)
	2005
6		Thomas Wennekers, Nihat Ay: Finite State Automata Resulting from Temporal Information Maximization and a Temporal Learning Rule. Neural Computation 17(10): 2258-2290 (2005)
5		Thomas Wennekers, Nihat Ay: Stochastic interaction in associative nets. Neurocomputing 65-66: 387-392 (2005)
	2003
4		Nihat Ay, Thomas Wennekers: Dynamical properties of strongly interacting Markov chains. Neural Networks 16(10): 1483-1497 (2003)
3		Thomas Wennekers, Nihat Ay: Temporal Infomax on Markov chains with input leads to finite state automata. Neurocomputing 52-54: 431-436 (2003)
2		Nihat Ay, Thomas Wennekers: Temporal infomax leads to almost deterministic dynamical systems. Neurocomputing 52-54: 461-466 (2003)
	2002
1		Nihat Ay: Locality of Global Stochastic Interaction in Directed Acyclic Networks. Neural Computation 14(12): 2959-2980 (2002)

Coauthor Index

1	Péter András	[8]
2	Nils Bertschinger	[10]
3	Ralf Der	[13]
4	Jürgen Jost	[10]
5	Thomas Kahle	[20]
6	Wolfgang Löhr	[14] [15]
7	Guido Montufar	[19] [21]
8	Markus Müller	[9] [16] [18]
9	Eckehard Olbrich	[10]
10	Daniel Polani	[11]
11	Johannes Rauh	[20] [21]
12	Bastian Steudel	[17]
13	Arleta Szkola	[9] [16] [18]
14	Thomas Wennekers	[2] [3] [4] [5] [6] [7] [8]
15	Keyan Zahedi	[13]

Colors in the list of coauthors

Last update Sun May 27 04:04:01 2012 CET by the DBLP Team — Data released under the ODC-BY 1.0 license — See also our legal information page