Publications

A full and up-to-date list, including citation metrics, is available on my Google Scholar profile.
^* equal contribution (first author)  ·  ^† equal contribution (second author)

Peer-reviewed journal & conference articles

2026

Ciernik, L.^*, Morik, M.^*, Thede, L., Eyring, L., Nakajima, S., Akata, Z., & Muttenthaler, L. (2026). Attentive Multi-Layer Fusion for Vision Transformers. Proceedings of the 43rd International Conference on Machine Learning (ICML).

Morik, M.^*, Ciernik, L.^*, Thede, L., Eyring, L., Nakajima, S., Akata, Z., & Muttenthaler, L. (2026). Revealing task-dependent layer relevance via attentive multi-layer fusion. ICLR 2026 Workshop on Science of Deep Learning (Sci4DL).

Born, F.^*, Neuhäuser, T.^*, Muttenthaler, L., Roads, B. D., Spitzer, B., Lampinen, A. K., Jones, M., Müller, K.-R., & Mozer, M. C. (2026). Context sensitivity improves human–machine visual alignment. ICLR 2026 Workshop on Representational Alignment (Re-Align).

2025

Muttenthaler, L., Greff, K., Born, F., Spitzer, B., Kornblith, S., Mozer, M. C., Müller, K.-R., Unterthiner, T., & Lampinen, A. K. (2025). Aligning machine and human visual representations across abstraction levels. Nature, 647(8089): 349–355.

Mahner, F.^*, Muttenthaler, L.^*, Güçlü, U., & Hebart, M. N. (2025). Dimensions underlying the representational alignment of deep neural networks with humans. Nature Machine Intelligence, 7: 848–859.

Ciernik, L., Linhardt, L., Morik, M., Dippel, J., Kornblith, S., & Muttenthaler, L. (2025). Training objective drives the consistency of representational similarity across datasets. Proceedings of the 42nd International Conference on Machine Learning (ICML).

Sucholutsky, I.^*, Muttenthaler, L.^*, … Lampinen, A. K., Müller, K.-R., Toneva, M., & Griffiths, T. (2025). Getting aligned on representational alignment. Transactions on Machine Learning Research (TMLR).

2024

Sundaram, S., Fu, S., Muttenthaler, L., Tamir, N. Y., Chai, L., Kornblith, S., Darrell, T., & Isola, P. (2024). When does perceptual alignment benefit vision representations? Advances in Neural Information Processing Systems (NeurIPS), 37: 55314–55341.

Muttenthaler, L., Vandermeulen, R. A., Zhang, Q. R., Unterthiner, T., & Müller, K.-R. (2024). Set learning for accurate and calibrated models. 12th International Conference on Learning Representations (ICLR).

Born, F.^*, Muttenthaler, L.^*, Greff, K., Unterthiner, T., Lampinen, A. K., Müller, K.-R., & Mozer, M. C. (2024). Evaluating and supervising vision models with multi-level similarity judgments. 7th Conference on Cognitive Computational Neuroscience (CCN).

2023

Muttenthaler, L., Linhardt, L., Dippel, J., Vandermeulen, R. A., Hermann, K., Lampinen, A. K., & Kornblith, S. (2023). Improving neural network representations using human similarity judgments. Advances in Neural Information Processing Systems (NeurIPS), 36: 50978–51007.

Muttenthaler, L., Dippel, J., Linhardt, L., Vandermeulen, R. A., & Kornblith, S. (2023). Human alignment of neural network representations. 11th International Conference on Learning Representations (ICLR).

Mahner, F.^*, Muttenthaler, L.^*, Güçlü, U., & Hebart, M. N. (2023). Dimensions that matter — interpretable object dimensions in humans and deep neural networks. 6th Conference on Cognitive Computational Neuroscience (CCN).

2022

Muttenthaler, L., Linhardt, L., Dippel, J., Vandermeulen, R. A., & Kornblith, S. (2022). Human alignment of neural network representations. SVRHM 2022 Workshop at NeurIPS.

Muttenthaler, L., Zheng, C. Y., McClure, P., Vandermeulen, R. A., Hebart, M. N., & Pereira, F. (2022). VICE: Variational interpretable concept embeddings. Advances in Neural Information Processing Systems (NeurIPS), 35: 33661–33675.

2021

Muttenthaler, L., & Hebart, M. N. (2021). THINGSvision: A Python toolbox for streamlining the extraction of activations from deep neural networks. Frontiers in Neuroinformatics, 15:45.

2020

Muttenthaler, L., Augenstein, I., & Bjerva, J. (2020). Unsupervised evaluation of question answering with Transformers. Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP, pp. 83–90. Association for Computational Linguistics (ACL).

2019

Buesel, C., Ditye, T., Muttenthaler, L., & Ansorge, U. (2019). A novel test of pure irrelevance-induced blindness. Frontiers in Psychology, 10:375.

López, H. A.^*, Marquard, M.^*, Muttenthaler, L.^*, & Strømsted, R.^* (2019). Assisted declarative process creation from natural language descriptions. 2019 IEEE 23rd International Enterprise Distributed Object Computing Conference (EDOC). Best Demonstration Award.

Muttenthaler, L., Lucas, G., & Amann, J. (2019). Authorship attribution in fan-fictional texts given variable length character and word n-grams. Notebook for PAN at CLEF 2019, Labs and Workshops, Notebook Papers. Winning team, PAN authorship attribution competition 2019.

2018

Muttenthaler, L. (2018). Effective enhancement of attentional functions in the amblyopic brain. Journal of European Psychology Students, 10(1): 1–10.

Preprints

Alber, M.^*, Milbich, T.^*, Carpen-Amarie, A.^*, Tietz, S.^†, Dippel, J.^†, Muttenthaler, L.^†, Perez Cancer, B.^†, Benetti, A.^†, Korfiatis, P., Eulig, E., Lüscher, J., Wu, J., Hashimi, S. A., Dernbach, G., Schallenberg, S., Shah, N., Krügener, M., Jammoria, A., Matras, J., Duffy, P., Redlon, M., Jurmeister, P., Horst, D., Ruff, L., Müller, K.-R., Klauschen, F., & Norgan, A. (2026). Atlas 2 — Foundation models for clinical deployment. arXiv preprint arXiv:2601.05148.

Muttenthaler, L., Hollenstein, N., & Barrett, M. (2020). Human brain activity for machine attention. arXiv preprint arXiv:2006.05113.