Publications of Philipp Kaniuth

Roth, J.; Duan, Y.; Mahner, F.; Kaniuth, P.; Wallis, T. S. A.; Hebart, M. N.: Ten principles for reliable, efficient, and adaptable coding in psychology and cognitive neuroscience. Communications Psychology 3 (1), 62 (2025)

Kaniuth, P.; Hebart, M. N.: Feature-reweighted representational similarity analysis: A method for improving the fit between computational models, brains, and behavior. NeuroImage 257, 119294 (2022)

Kaniuth, P.; Hebart, M. N.: Tuned representational similarity analysis: Improving the fit between computational models of vision and brain data. In Journal of Vision, 20, 1076. Vision Sciences Society Annual Meeting 2020, June 19, 2020 - June 24, 2020. Scholar One, Inc., Charlottesville, VA (2020)

Hebart, M. N.; Perkuhn, J.; Kaniuth, P.: Efficiently-generated object similarity scores predicted from human feature ratings and deep neural network activations. Annual Meeting of the Vision Science Society (VSS), St. Pete Beach, FL, USA (2022)

Kaniuth, P.; Hebart, M. N.: Feature-reweighted RSA: A general purpose method for increasing the fit between vision models and brain data. 10th IMPRS NeuroCom Summer School, Virtual (2021)

Kaniuth, P.; Hebart, M. N.: Object similarities can be efficiently generated using human ratings and neural network predictions. 11th IMPRS NeuroCom Summer School, Leipzig, Germany (2022)

Kaniuth, P.; Perkuhn, J.; Hebart, M. N.: Object similarities can be efficiently generated using human ratings and neural network predictions. 28th Annual Meeting of the Organization for Human Brain Mapping (OHBM), Glasgow, United Kingdom (2022)

Kaniuth, P.; Hebart, M. N.: Feature-reweighted RSA: A general purpose method for increasing the fit between vision models and brain data. 10th IMPRS NeuroCom Summer School, Virtual (2021)

Kaniuth, P.; Hebart, M. N.: Feature-reweighted RSA: A general purpose method for increasing the fit between vision models and brain data? Society for Neuroscience Global Connectome, Virtual (2021)

Kaniuth, P.; Hebart, M. N.: Tuned representational similarity analysis: Improving the fit between computational models of vision and brain data. Vision Sciences Society Annual Meeting 2020, Virtual (2020)

Kaniuth, P.; Mahner, F.; Perkuhn, J.; Hebart, M. N.: A high-throughput approach for the efficient prediction of perceived similarity of natural objects. bioRxiv (2024)