| A mathematical theory of relational generalization in transitive inference S Lippl, K Kay, G Jensen, VP Ferrera, LF Abbott Proceedings of the National Academy of Sciences 121 (28), e2314511121, 2024 | 9 | 2024 |
| When does compositional structure yield compositional generalization? a kernel theory S Lippl, K Stachenfeld arXiv preprint arXiv:2405.16391, 2024 | 5 | 2024 |
| Inductive biases of multi-task learning and finetuning: multiple regimes of feature reuse S Lippl, JW Lindsey The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024 | 5* | 2024 |
| The implicit bias of gradient descent on generalized gated linear networks S Lippl, LF Abbott, SY Chung arXiv preprint arXiv:2202.02649, 2022 | 1 | 2022 |
| Source Invariance and Probabilistic Transfer: A Testable Theory of Probabilistic Neural Representations S Lippl, R Gerraty, J Morrison, N Kriegeskorte arXiv preprint arXiv:2404.08101, 2024 | | 2024 |
| Can neural networks benefit from objectives that encourage iterative convergent computations? A case study of ResNets and object classification S Lippl, B Peters, N Kriegeskorte Plos one 19 (3), e0293440, 2024 | | 2024 |
| Iterative convergent computation may not be a useful inductive bias for residual neural networks L Samuel, P Benjamin, K Nikolaus bioRxiv, 2023.10. 13.562196, 2023 | | 2023 |
| Probabilistic representations should be defined by their computational role: reply to Rahnev et al. 2021 R Gerraty, S Lippl, J Morrison, N Kriegeskorte OSF, 2021 | | 2021 |
| Description, implementation and validation of a user interface for complex datasets in the social sciences S Lippl | | 2018 |
