On convex decision regions in deep network representations
Abstract Current work on human-machine alignment aims at understanding machine-learned latent spaces and their relations to human representations. We study the convexity of concept regions in machine-learned latent spaces, inspired by Gärdenfors’ conceptual spaces. In cognitive science, convexity is...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-60809-y |
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| _version_ | 1849769092884463616 |
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| author | Lenka Tětková Thea Brüsch Teresa Dorszewski Fabian Martin Mager Rasmus Ørtoft Aagaard Jonathan Foldager Tommy Sonne Alstrøm Lars Kai Hansen |
| author_facet | Lenka Tětková Thea Brüsch Teresa Dorszewski Fabian Martin Mager Rasmus Ørtoft Aagaard Jonathan Foldager Tommy Sonne Alstrøm Lars Kai Hansen |
| author_sort | Lenka Tětková |
| collection | DOAJ |
| description | Abstract Current work on human-machine alignment aims at understanding machine-learned latent spaces and their relations to human representations. We study the convexity of concept regions in machine-learned latent spaces, inspired by Gärdenfors’ conceptual spaces. In cognitive science, convexity is found to support generalization, few-shot learning, and interpersonal alignment. We develop tools to measure convexity in sampled data and evaluate it across layers of state-of-the-art deep networks. We show that convexity is robust to relevant latent space transformations and, hence, meaningful as a quality of machine-learned latent spaces. We find pervasive approximate convexity across domains, including image, text, audio, human activity, and medical data. Fine-tuning generally increases convexity, and the level of convexity of class label regions in pretrained models predicts subsequent fine-tuning performance. Our framework allows investigation of layered latent representations and offers new insights into learning mechanisms, human-machine alignment, and potential improvements in model generalization. |
| format | Article |
| id | doaj-art-c7bc035083304c51ace56f3b146ab7bc |
| institution | DOAJ |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-c7bc035083304c51ace56f3b146ab7bc2025-08-20T03:03:34ZengNature PortfolioNature Communications2041-17232025-07-0116111210.1038/s41467-025-60809-yOn convex decision regions in deep network representationsLenka Tětková0Thea Brüsch1Teresa Dorszewski2Fabian Martin Mager3Rasmus Ørtoft Aagaard4Jonathan Foldager5Tommy Sonne Alstrøm6Lars Kai Hansen7Section for Cognitive Systems, DTU Compute, Technical University of DenmarkSection for Cognitive Systems, DTU Compute, Technical University of DenmarkSection for Cognitive Systems, DTU Compute, Technical University of DenmarkSection for Cognitive Systems, DTU Compute, Technical University of DenmarkSection for Cognitive Systems, DTU Compute, Technical University of DenmarkSection for Cognitive Systems, DTU Compute, Technical University of DenmarkSection for Cognitive Systems, DTU Compute, Technical University of DenmarkSection for Cognitive Systems, DTU Compute, Technical University of DenmarkAbstract Current work on human-machine alignment aims at understanding machine-learned latent spaces and their relations to human representations. We study the convexity of concept regions in machine-learned latent spaces, inspired by Gärdenfors’ conceptual spaces. In cognitive science, convexity is found to support generalization, few-shot learning, and interpersonal alignment. We develop tools to measure convexity in sampled data and evaluate it across layers of state-of-the-art deep networks. We show that convexity is robust to relevant latent space transformations and, hence, meaningful as a quality of machine-learned latent spaces. We find pervasive approximate convexity across domains, including image, text, audio, human activity, and medical data. Fine-tuning generally increases convexity, and the level of convexity of class label regions in pretrained models predicts subsequent fine-tuning performance. Our framework allows investigation of layered latent representations and offers new insights into learning mechanisms, human-machine alignment, and potential improvements in model generalization.https://doi.org/10.1038/s41467-025-60809-y |
| spellingShingle | Lenka Tětková Thea Brüsch Teresa Dorszewski Fabian Martin Mager Rasmus Ørtoft Aagaard Jonathan Foldager Tommy Sonne Alstrøm Lars Kai Hansen On convex decision regions in deep network representations Nature Communications |
| title | On convex decision regions in deep network representations |
| title_full | On convex decision regions in deep network representations |
| title_fullStr | On convex decision regions in deep network representations |
| title_full_unstemmed | On convex decision regions in deep network representations |
| title_short | On convex decision regions in deep network representations |
| title_sort | on convex decision regions in deep network representations |
| url | https://doi.org/10.1038/s41467-025-60809-y |
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