Suchergebnisse

Forschungspapier (13)

441.
Forschungspapier
Ahilan, S.; Dayan, P.: Correcting Experience Replay for Multi-Agent Communication. (eingereicht)
442.
Forschungspapier
Schulz, E.; Dayan, P.: Computational psychiatry for computers. (eingereicht)
443.
Forschungspapier
Silston, B.; Wise, T.; Qi, S.; Sui, X.; Dayan, P.; Mobbs, D.: Neural encoding of socially adjusted value during competitive and hazardous foraging. (eingereicht)
444.
Forschungspapier
Dezfouli, A.; Nock, R.; Arabzadeh, E.; Dayan, P.: Neural Network Poisson Models for Behavioural and Neural Spike Train Data. (eingereicht)
445.
Forschungspapier
Mancinelli, F.; Roiser, J.; Dayan, P.: Subjective Beliefs In, Out, and About Control: A Quantitative Analysis. (eingereicht)
446.
Forschungspapier
Faulkner, P.; Huys, Q.; Renz, D.; Eshel, N.; Pilling, S.; Dayan, P.; Roiser, J.: A Comparison of "Pruning" During Multi-Step Planning in Depressed and Healthy Individuals. (eingereicht)
447.
Forschungspapier
Dezfouli, A.; Nock, R.; Dayan, P.: Adversarial manipulation of human decision-making. (eingereicht)
448.
Forschungspapier
Neville, V.; Dayan, P.; Gilchrist, I.; Paul, E.; Mendl, M.: Dissecting the links between reward and loss, decision-making, and self-reported affect using a computational approach. (eingereicht)
449.
Forschungspapier
Kastner, D.; Miller, E.; Yang, Z.; Roumis, D.; Liu, D.; Frank, L.; Dayan, P.: Dynamic preferences account for inter-animal variability during the continual learning of a cognitive task. (eingereicht)
450.
Forschungspapier
Iigaya, K.; Hauser, T.; Kurth-Nelson, Z.; O'Doherty, J.; Dayan, P.; Dolan, R.: The value of what’s to come: neural mechanisms coupling prediction error and reward anticipation. (eingereicht)
451.
Forschungspapier
Iigaya, K.; Hauser, T.; Kurth-Nelson, Z.; O'Doherty, J.; Dayan, P.; Dolan, R.: Hippocampal-midbrain circuit enhances the pleasure of anticipation in the prefrontal cortex. (eingereicht)
452.
Forschungspapier
Zhao, S.; Chait, M.; Dick, F.; Dayan, P.; Furukawa, S.; Liao, H.-I.: Phasic norepinephrine is a neural interrupt signal for unexpected events in rapidly unfolding sensory sequences: evidence from pupillometry. (eingereicht)
453.
Forschungspapier
Danihelka, I.; Lakshminarayanan, B.; Uria, B.; Wierstra, D.; Dayan, P.: Comparison of Maximum Likelihood and GAN-based training of Real NVPs. (eingereicht)
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