Two-shot learning of multiple strange attractors
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866914289220059136 |
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| author | Köglmayr, Daniel Spahic, Miralem Flynn, Andrew Räth, Christoph |
| author_facet | Köglmayr, Daniel Spahic, Miralem Flynn, Andrew Räth, Christoph |
| contents | The brain combines short- and long-term memory to process, store, and recall multiple different pieces of information. Inspired by this and recent results on multifunctional and parameter-aware learning, we extend a new machine learning technique that combines short- and long-term memory units, specifically, a system consisting of a next-generation reservoir computer (NGRC) and extremely randomized trees (ERT), to process, store, and recall multiple different strange attractors. We train the combined NGRC+ERT system using a two-shot learning approach which significantly improves performance by filtering out unnecessary features, thereby avoiding extensive hyperparameter optimization. We first show that an NGRC+ERT system achieves highly accurate reconstruction of the short- and long-term dynamics of both the Lorenz and Halvorsen chaotic attractors when using an exponential filtering scheme. We validate these finding by training the NGRC+ERT system to reconstruct different pairs of attractors and also a greater number of attractors. We focus on the task of training a single NGRC+ERT system to reconstruct 16 different attractors and show that sufficient index-based separation in feature space suppresses unwanted switching dynamics, thus stabilizing long-term memory recall. Finally, we identify that defects in short-term memory processing can provoke failure modes in long-term memory recall resulting in confabulation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_21117 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Two-shot learning of multiple strange attractors Köglmayr, Daniel Spahic, Miralem Flynn, Andrew Räth, Christoph Chaotic Dynamics The brain combines short- and long-term memory to process, store, and recall multiple different pieces of information. Inspired by this and recent results on multifunctional and parameter-aware learning, we extend a new machine learning technique that combines short- and long-term memory units, specifically, a system consisting of a next-generation reservoir computer (NGRC) and extremely randomized trees (ERT), to process, store, and recall multiple different strange attractors. We train the combined NGRC+ERT system using a two-shot learning approach which significantly improves performance by filtering out unnecessary features, thereby avoiding extensive hyperparameter optimization. We first show that an NGRC+ERT system achieves highly accurate reconstruction of the short- and long-term dynamics of both the Lorenz and Halvorsen chaotic attractors when using an exponential filtering scheme. We validate these finding by training the NGRC+ERT system to reconstruct different pairs of attractors and also a greater number of attractors. We focus on the task of training a single NGRC+ERT system to reconstruct 16 different attractors and show that sufficient index-based separation in feature space suppresses unwanted switching dynamics, thus stabilizing long-term memory recall. Finally, we identify that defects in short-term memory processing can provoke failure modes in long-term memory recall resulting in confabulation. |
| title | Two-shot learning of multiple strange attractors |
| topic | Chaotic Dynamics |
| url | https://arxiv.org/abs/2601.21117 |