Publikationen von P Dayan

Dayan, P.; Nakahara, H.: Models and Methods for Reinforcement Learning. In: Stevens' Handbook of Experimental Psychology and Cognitive Neuroscience, Bd. 5: Methodology, 4. Aufl., S. 507 - 546 (Hg. Wixted, J.; Wagenmakers, E.-J.). Wiley, Hoboken, NJ, USA (2018)

Dayan, P.: Exploration from Generalization Mediated by Multiple Controllers. In: Intrinsically Motivated Learning in Natural and Artificial Systems, S. 73 - 91 (Hg. Baldassarre, G.; Mirolli, M.). Springer, Berlin, Germany (2013)

Dayan, P.: Models of Value and Choice. In: Neuroscience of Preference and Choice: Cognitive and Neural Mechanisms, 2, S. 33 - 52 (Hg. Dolan, R.; Sharot, T.). Elsevier/Academic Press, London, UK (2012)

Daw, N.; Courville, A.; Dayan, P.: Semi-rational models of conditioning: The case of trial order. In: The Probabilistic Mind: Prospects for Bayesian cognitive science$, 19, S. 427 - 448 (Hg. Chater, N.; Oaksford, M.). Oxford University Press, Oxford, UK (2008)

Platt, M.; Dayan, P.; Dehaene, S.; McCabe, H.; Menzel, R.; Phelps, E.; Plassmann, H.; Ratcliff, R.; Shadlen, M.; Singer, W.: Neuronal Correlates of Decision Making. In: Better than conscious?: decision making, the human mind, and implications for institutions, 6 (Hg. Engel, C.; Singer, W.). MIT Press, Cambridge, MA, USA (2008)

Daw, N.; Niv, Y.; Dayan, P.: Actions, policies, values, and the basal ganglia. In: Recent Breakthroughs in Basal Ganglia Research, 8, S. 91 - 106 (Hg. Bezard, E.). Nova Science Publishers, Hauppauge, NY, USA (2006)

Dayan, P.: Levels of Analysis in Neural Modeling. In: Encyclopedia of Cognitive Science, 2 Aufl. (Hg. Nadel, L.). Wiley, Chichester, UK (2005)

Dayan, P.; Watkins, C.: Reinforcement Learning. In: Encyclopedia of Cognitive Science, Bd. 3 (Hg. Nadel, L.). Wiley, Chichester, UK (2005)

Dayan, P.: Helmholtz machines and wake-sleep learning. In: The Handbook of Brain Theory and Neural Networks, 2. Aufl., S. 522 - 524 (Hg. Arbib, M.). MIT Press, Cambridge, MA, USA (2003)

Schraudolph, N.; Dayan, P.; Sejnowski, T.: Learning to Evaluate Go Positions via Temporal Difference Methods. In: Computational Intelligence in Games, S. 77 - 98 (Hg. Baba, N.; Jain, L.). Physica Verlag, Heidelberg, Germany (2001)

Dayan, P.: Unsupervised Learning. In: The MIT Encyclopedia of the Cognitive Sciences (Hg. Wilson, R.; Keil, F.). MIT Press, Cambridge, MA, USA (1999)

Montague, P.; Dayan, P.: Neurobiological Modeling: Squeezing Top Down to Meet Bottom Up. In: A Companion to Cognitive Science, S. 526 - 542 (Hg. Bechtel, W.; Graham, G.). Blackwell, Malden, MA, USA (1998)

Oberlander, J.; Dayan, P.: Altered states and virtual beliefs. In: Connectionism, Concepts and Folk Psychology: The Legacy of Alan Turing Vol. 2, S. 101 - 114 (Hg. Millican, P.; Clark, A.). Clarendon Press, Oxford, UK (1996)

Exploration and Curiosity in Robot Learning and Inference (Dagstuhl Reports, 1). Dagstuhl Seminar 11131, Dagstuhl, Germany, 27. März 2011 - 01. April 2011. (2011)

Wu, S.; Alaniz, S.; Karthik, S.; Dayan, P.; Schulz, E.; Akata, Z.: Concept-Guided Interpretability via Neural Chunking. In: Thirty-ninth Conference on Neural Information Processing Systems (NeurIPS 2025). Thirty-ninth Conference on Neural Information Processing Systems (NeurIPS 2025), Vancouver, Canada, 10. Dezember 2024 - 15. Dezember 2024. (2025)

Nagy, D.; Shen, K.; Zhou, H.; Wu, C.; Dayan, P.: Analogy making as amortised model construction. In: Second Reinforcement Learning Conference (RLC 2025). Second Reinforcement Learning Conference (RLC 2025), Edmonton, AB, Canada, 05. August 2025 - 09. August 2025. (2025)

Sarıtaş, K.; Dayan, P.; Shen, K.; Nath, S.: Complexity in Complexity: Understanding Visual Complexity Through Structure, Color, and Surprise. In: Proceedings of the 47th Annual Conference of the Cognitive Science Society, V5-S-60, S. 2623 - 2630 (Hg. Barner, D.; Bramley, N.; Ruggeri, A.; Walker, C.). 47th Annual Meeting of the Cognitive Science Society (CogSci 2025): Theories of the Past, Theories of the Future , San Francisco, CA, USA, 30. Juli 2025 - 02. August 2025. (2025)

Saanum, T.; Dayan, P.; Schulz, E.: Simplifying Latent Dynamics with Softly State-Invariant World Models. In: NIPS '24: Proceedings of the 38th International Conference on Neural Information Processing Systems, 1212, S. 38355 - 38382 (Hg. Globerson, A.; Mackay, L.; Belgrave, D.; Fan, A.; Paquet, U. et al.). Thirty-eighth Conference on Neural Information Processing Systems (NeurIPS 2024), Vancouver, Canada, 10. Dezember 2024 - 15. Dezember 2024. Curran, Red Hook, NY, USA (2025)

Saanum, T.; Schulze Buschoff, L.; Dayan, P.; Schulz, E.: Next state prediction gives rise to entangled, yet compositional representations of objects. In: Thirteenth International Conference on Learning Representations (ICLR 2025). Thirteenth International Conference on Learning Representations (ICLR 2025), Singapore, 24. April 2025 - 28. April 2025. (2025)

Sarıtaş, K.; Dayan, P.; Shen, K.; Nath, S.: Complexity in Complexity: Understanding Visual Complexity Through Structure, Color, and Surprise. In: ICLR 2025 Workshop on Representational Alignment (Re-Align). ICLR 2025 Workshop on Representational Alignment (Re-Align), Singapore, 28. April 2025. (2025)