Researcher Portfolio

 
   

Bethge, Matthias

Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, Max Planck Institute for Biological Cybernetics, Max Planck Society, Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society  

 

Researcher Profile

 
Position: Max Planck Institute for Biological Cybernetics, Max Planck Society
Position: Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society
Position: Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society
Additional IDs: MPIKYB: mbethge
Researcher ID: https://pure.mpg.de/cone/persons/resource/persons83805

External references

 

Publications

 
  (1 - 25 of 241)
 : Binz, M., Akata, E., Bethge, M., Brändle, F., Callaway, F., Coda-Forno, J., Dayan, P., Demircan, C., Eckstein, M., Éltetö, N., Griffiths, T., Haridi, S., Jagadish, A., Ji-An, Kipnis, A., Kumar, S., Ludwig, T., Mathony, M., Mattar, M., Modirshanechi, A., Nath, S., Peterson, J., Rmus, M., Russek, E., Saanum, T., Scharfenberg, N., Schubert, J., Schulze Buschoff, L., Singhi, N., Sui, X., Thalmann, M., Theis, F., Truong, V., Udandarao, V., Voudouris, K., Wilson, R., Witte, K., Wu, S., Wulff, D., Xiong, H., & Schulz, E. (submitted). Centaur: a foundation model of human cognition. [PubMan] : Schulze Buschoff, L., Akata, E., Bethge, M., & Schulz, E. (submitted). Have we built machines that think like people? [PubMan] : Akata, E., Schulz, L., Coda-Forno, J., Oh, S., Bethge, M., & Schulz, E. (submitted). Playing repeated games with Large Language Models. [PubMan] : Bethge, M. (2022). Efficient Population Coding. In D. Jaeger, & R. Jung (Eds.), Encyclopedia of Computational Neuroscience (pp. 1257-1264). New York, NY, USA: Springer. doi:10.1007/978-1-0716-1006-0_578. [PubMan] : Brendel, W., Rauber, J., Kümmerer, M., Ustyuzhaninov, I., & Bethge, M. (2020). Accurate, reliable and fast robustness evaluation. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, & R. Garnett (Eds.), Advances in Neural Information Processing Systems 32 (pp. 12817-12827). Red Hook, NY, USA: Curran. [PubMan] : Higgins, I., Konkle, T., & Bethge, M. (2019). What sorts of cognitive or biological (architectural) inductive biases will be crucial for developing effective artificial intelligence?. Talk presented at NeurIPS 2019 Workshop: Shared Visual Representations in Human and Machine Intelligence. Vancouver, BC, Canada. 2019-12-13. [PubMan] : Wallis, T., Tobias, S., Bethge, M., & Wichmann, F. (2019). Correction to: Detecting distortions of peripherally presented letter stimuli under crowded conditions. Attention, Perception & Psychophysics, 81(8), 2968-2970. doi:10.3758/s13414-019-01855-9. [PubMan] : Geirhos, R., Rubisch, P., Rauber, J., Medina Temme, C., Michaelis, C., Brendel, W., Bethge, M., & Wichmann, F. (2019). Inducing a human-like shape bias leads to emergent human-level distortion robustness in CNNs. Poster presented at Nineteenth Annual Meeting of the Vision Sciences Society (VSS 2019), St. Pete Beach, FL, USA. doi:10.1167/19.10.209c. [PubMan] : Sinz, F., Pitkow, X., Reimer, J., Bethge, M., & Tolias, A. (2019). Engineering a Less Artificial Intelligence. Neuron, 103(6), 967-979. doi:10.1016/j.neuron.2019.08.034. [PubMan] : Michaelis, C., Weller, M., Funke, C., Ecker, A., Wallis, T., & Bethge, M. (2019). Comparing Search Strategies of Humans and Machines in Clutter. Poster presented at Nineteenth Annual Meeting of the Vision Sciences Society (VSS 2019), St. Pete Beach, FL, USA. doi:10.1167/19.10.309c. [PubMan] : Geirhos, R., Medina Temme, C., Rauber, J., Schuett, H., Bethge, M., & Wichmann, F. (2019). Generalisation in humans and deep neural networks. In S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, & R. Garnett (Eds.), Advances in Neural Information Processing Systems 31 (pp. 7549-7561). Red Hook, NY, USA: Curran. [PubMan] : Schott, L., Rauber, J., Bethge, M., & Brendel, W. (2019). Towards the First Adversarially Robust Neural Network Model on MNIST. In Seventh International Conference on Learning Representations (ICLR 2019) (pp. 1-16). [PubMan] : Cadena, S., Denfield, G., Walker, E., Gatys, L., Tolias, A., Bethge, M., & Ecker, A. (2019). Deep convolutional models improve predictions of macaque V1 responses to natural images. PLoS Computational Biology, 15(4), 1-27. doi:10.1371/journal.pcbi.1006897. [PubMan] : Wallis, T., Funke, C., Ecker, A., Gatys, L., Wichmann, F., & Bethge, M. (2019). Image content is more important than1Bouma’s Law for scene metamers. eLife, 8, 1-43. doi:10.7554/eLife.42512. [PubMan] : Wallis, T., Funke, C., Ecker, A., Gatys, L., Wichmann, F., & Bethge, M. (2019). Image content is more important than Bouma’s Law for scene metamers. eLife, 8, 1-43. doi:10.7554/eLife.42512.001. [PubMan] : Ecker, A., Sinz, F., Froudarakis, E., Fahey, P., Cadena, S., Walker, E., Cobos, E., Reimer, J., Tolias, A., & Bethge, M. (2019). A rotation-equivariant convolutional neural network model of primary visual cortex. In Seventh International Conference on Learning Representations (ICLR 2019) (pp. 1-11). [PubMan] : Bethge, M. (2019). Lack of Robustness in Artificial Neural Networks. Neuroforum, 25(Supplement 1): S23-1, 179. [PubMan] : Subramaniyan, M., Ecker, A., Patel, S., Cottonq, R., Bethge, M., Pitkow, X., Berens, P., & Tolias, A. (2018). Faster processing of moving compared with flashed bars in awake macaque V1 provides a neural correlate of the flash lag illusion. Journal of Neurophysiology, 120(5), 2430-2452. doi:10.1152/jn.00792.2017. [PubMan] : Mathis, A., Mamidanna, P., Cury, A., Abe, T., Murthy, V., Mathis, M., & Bethge, M. (2018). DeepLabCut: markerless pose estimation of user-defined body parts with deep learning. Nature Neuroscience, 21(9), 1281-1289. doi:10.1038/s41593-018-0209-y. [PubMan] : Funke, C., Borowski, J., Wallis, T., Brendel, W., Ecker, A., & Bethge, M. (2018). Comparing the ability of humans and DNNs to recognise closed contours in cluttered images. Poster presented at 18th Annual Meeting of the Vision Sciences Society (VSS 2018), St. Pete Beach, FL, USA. [PubMan] : Mathis, A., Mamidanna, P., Abe, T., Cury, K., Murthy, V., Mathis, M., & Bethge, M. (2018). Markerless tracking of user-defined anatomical features with deep learning. Poster presented at CSF Conference: Hand, Brain and Technology: The Somatosensory System (HBT 2018), Monte Verità, Switzerland. [PubMan] : Michaelis, C., Bethge, M., & Ecker, A. (2018). One-Shot Segmentation in Clutter. In J. Dy, & A. Krause (Eds.), International Conference on Machine Learning, 10-15 July 2018, Stockholmsmässan, Stockholm Sweden (pp. 3549-3558). Madison, WI, USA: International Machine Learning Society. [PubMan] : Denfield, G., Ecker, A., Shinn, T., Bethge, M., & Tolias, A. (2018). Attentional fluctuations induce shared variability in macaque primary visual cortex. Nature Communications, 9: 2654, pp. 1-14. doi:10.1038/s41467-018-05123-6. [PubMan] : Cotton, R., Ecker, A., Froudarakis, E., Berens, P., Bethge, M., Saggau, P., & Tolias, A. (2018). Scaling of information in large sensory populations. Poster presented at AREADNE 2018: Research in Encoding And Decoding of Neural Ensembles, Santorini, Greece. [PubMan] : Klindt, D., Ecker, A., Euler, T., & Bethge, M. (2018). Neural system identification for large populations: Separating what and where. Poster presented at AREADNE 2018: Research in Encoding And Decoding of Neural Ensembles, Santorini, Greece. [PubMan]