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Heiko Schütt
Heiko Schütt
Associate Professor, University of Luxembourg
Verified email at uni.lu
Title
Cited by
Cited by
Year
Generalisation in humans and deep neural networks
R Geirhos, CRM Temme, J Rauber, HH Schütt, M Bethge, FA Wichmann
Advances in neural information processing systems 31, 2018
873*2018
Painfree and accurate Bayesian estimation of psychometric functions for (potentially) overdispersed data
HH Schütt, S Harmeling, JH Macke, FA Wichmann
Vision research 122, 105-123, 2016
365*2016
Likelihood-based parameter estimation and comparison of dynamical cognitive models.
HH Schütt, LOM Rothkegel, HA Trukenbrod, S Reich, FA Wichmann, ...
Psychological review 124 (4), 505, 2017
472017
Disentangling bottom-up versus top-down and low-level versus high-level influences on eye movements over time
HH Schütt, LOM Rothkegel, HA Trukenbrod, R Engbert, FA Wichmann
Journal of vision 19 (3), 1-1, 2019
462019
An image-computable psychophysical spatial vision model
HH Schütt, FA Wichmann
Journal of vision 17 (12), 12-12, 2017
412017
Temporal evolution of the central fixation bias in scene viewing
LOM Rothkegel, HA Trukenbrod, HH Schütt, FA Wichmann, R Engbert
Journal of vision 17 (13), 3-3, 2017
342017
Comparing representational geometries using whitened unbiased-distance-matrix similarity
J Diedrichsen, E Berlot, M Mur, HH Schütt, M Shahbazi, N Kriegeskorte
arXiv preprint arXiv:2007.02789, 2020
31*2020
Influence of initial fixation position in scene viewing
LOM Rothkegel, HA Trukenbrod, HH Schütt, FA Wichmann, R Engbert
Vision research 129, 33-49, 2016
202016
Methods and measurements to compare men against machines
FA Wichmann, DHJ Janssen, R Geirhos, G Aguilar, HH Schütt, ...
Electronic Imaging 2017 (14), 36-45, 2017
182017
Searchers adjust their eye-movement dynamics to target characteristics in natural scenes
LOM Rothkegel, HH Schütt, HA Trukenbrod, FA Wichmann, R Engbert
Scientific reports 9 (1), 1635, 2019
172019
Comparing deep neural networks against humans: Object recognition when the signal gets weaker. arXiv 2017
R Geirhos, DHJ Janssen, HH Schütt, J Rauber, M Bethge, FA Wichmann
arXiv preprint arXiv:1706.06969, 2018
152018
Deep neural models for color classification and color constancy
A Flachot, A Akbarinia, HH Schütt, RW Fleming, FA Wichmann, ...
Journal of Vision 22 (4), 17-17, 2022
122022
Statistical inference on representational geometries
HH Schütt, AD Kipnis, J Diedrichsen, N Kriegeskorte
Elife 12, e82566, 2023
102023
Perception of light source distance from shading patterns
HH Schuett, F Baier, RW Fleming
Journal of Vision 16 (3), 9-9, 2016
72016
Comparing deep neural networks against humans: object recognition when the signal gets weaker (2017)
R Geirhos, DHJ Janssen, HH Schütt, J Rauber, M Bethge, FA Wichmann
arXiv preprint arXiv:1706.06969, 0
7
Distinguishing representational geometries with controversial stimuli: Bayesian experimental design and its application to face dissimilarity judgments
T Golan, W Guo, HH Schütt, N Kriegeskorte
arXiv preprint arXiv:2211.15053, 2022
52022
Reward prediction error neurons implement an efficient code for reward
HH Schütt, D Kim, WJ Ma
bioRxiv, 2022.11. 03.515104, 2022
32022
Using deep neural networks as a guide for modeling human planning
I Kuperwajs, HH Schütt, WJ Ma
Scientific Reports 13 (1), 20269, 2023
22023
Deep neural networks are not a single hypothesis but a language for expressing computational hypotheses
T Golan, JM Taylor, H Schütt, B Peters, RP Sommers, K Seeliger, ...
PsyArXiv, 2023
22023
Color constancy in deep neural networks
AC Flachot, HH Schuett, RW Fleming, F Wichmann, KR Gegenfurtner
Journal of Vision 19 (10), 298-298, 2019
22019
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Articles 1–20