Suchergebnisse

Jastorff, J.: Learning of artificial biological motion stimuli based on either local or global stimulus features. Master, Max-Planck-Institute for Biological Cybernetics, Tübingen, Germany (2002)

Di Luca, M.: Percezione di superfici e illuminazione dinamica. Master, Università di Trieste, Trieste, Italy (2001)

Yasuhara, K.: Self-Organization of Jumping Motion using Genetic Programming. Master, University of Tsukuba, Tsukuba, Japan (1994)

Mahler, L.: Spatio-Temporal Feature Extraction for Action Recognition in Videos. Bachelor, Technische Hochschule Ulm, Ulm, Germany (2020)

Hanrieder, M.: Collaborative Spatial Search: Implementation and Validation of a Multi-User Task in Walkable Virtual Environments. Bachelor, Eberhard-Karls-Universität, Tübingen, Germany (2017)

Valsala, P.: Electrical Impedance tomography. Bachelor, Anna University, Chennai, India (2014)

Brielmann, A.: Eine experimentelle Studie zur Vorstellung eines neuen standardisierten Stimulus-Sets zur Erfassung des Erkennens von Hilfsbedürftigkeit bei Kindern (NeoHelp) [An experimental study introducing a new standardized stimulus set for studying need-of-help recognition in children (NeoHelp)]. Bachelor, 99 S., Universität Konstanz, Konstanz, Germany (2013)

International Brain Laboratory; Banga, K.; Boussard, J.; Chapuis, G.; Faulkner, M.; Harris, K.; Huntenburg, J.; Hurwitz, C.; Lee, H.; Paninski, L. et al.; Rossant, C.; Roth, N.; Steinmetz, N.; Windolf, C.; Winter, O.: Spike sorting pipeline for the International Brain Laboratory. (2022), 17 S.

Meding, K.; Schulze Buschoff, L.; Geirhos, R.; Wichmann, F.: Trivial or impossible: dichotomous data difficulty masks model differences (on ImageNet and beyond). (eingereicht)

Avraham, G.; Taylor, J.; Breska, A.; Ivry, R.; McDougle, S.: Contextual effects in motor adaptation adhere to associative learning rules. (eingereicht)

Eckstein, M.; Master, S.; Dahl, R.; Wilbrecht, L.; Collins, A.: The Unique Advantage of Adolescents in Probabilistic Reversal: Reinforcement Learning and Bayesian Inference Provide Adequate and Complementary Models. (eingereicht)

Eckstein, M.; Master, S.; Dahl, R.; Wilbrecht, L.; Collins, A.: Understanding the Unique Advantage of Adolescents in Stochastic, Volatile Environments: Combining Reinforcement Learning and Bayesian Inference. (eingereicht)

Alon, N.; Meilijson, I.: Randomtime transformation analysis of Covid19 2020. (eingereicht)

Hinman, M.; Richardson, J.; Sockol, R.; Aronson, E.; Stednitz, S.; Murray, K.; Berglund, J.; Guillemin, K.: Zebrafish mbnl mutants model physical and molecular phenotypes of myotonic dystrophy. (eingereicht)

Feld, G.; Ngo, H.-V.; Durán, E.; Gebhardt, S.; Kleist, L.; Brugger, K.; Fritsche, A.; Born, J.; Hallschmid, M.: The gap junction blocker mefloquine impairs sleep-dependent declarative memory consolidation in humans. (eingereicht)

Shi, D.; Shih, C.; Lo, C.; Lin, Y.; Chiang, A.: Detecting local processing unit in drosophila brain by using network theory. (eingereicht)

Howard-Jones, P.; Bogacz, R.; Lloyd, K.: To act or not to act: Learning the value of not acting. (2014)

Bokharaie, V.; Parsaee, G.: An Application of Joint Spectral Radius in Power Control Problem for Wireless Communications. (eingereicht)

Pilz, K.; Breidt, M.; Thornton, I.; Bülthoff, H.: Video capture of non-rigidly moving faces out of five different viewpoints. (2002)

Schölkopf, B.: Das Spiel mit dem künstlichen Leben. Frankfurter Allgemeine Zeitung: Wissenschaftsbeilage (1997)