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Generalized Shape Metrics on Neural Representations| title | Generalized Shape Metrics on Neural Representations |
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| start_date | 2023/10/02 |
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| schedule | 14h |
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| online | no |
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| location_info | Salle Ribot |
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| details | GNT external seminar series |
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| summary | Understanding the operation of biological and artificial networks remains a difficult and important challenge. To identify general principles, researchers are increasingly interested in surveying large collections of networks that are trained on, or biologically adapted to, similar tasks. A standardized set of analysis tools is now needed to identify how network-level covariates -- such as architecture, anatomical brain region, and model organism -- impact neural representations (hidden layer activations). Here, we provide a rigorous foundation for these analyses by defining a broad family of metric spaces that quantify representational dissimilarity. Using this framework we modify existing representational similarity measures based on canonical correlation analysis to satisfy the triangle inequality, formulate a novel metric that respects the inductive biases in convolutional layers, and identify approximate Euclidean embeddings that enable network representations to be incorporated into essentially any off-the-shelf machine learning method. We demonstrate these methods on large-scale datasets from biology (Allen Institute Brain Observatory) and deep learning (NAS-Bench-101). In doing so, we identify relationships between neural representations that are interpretable in terms of anatomical features and model performance. |
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| responsibles | NC |
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Workflow history| from state (1) | to state | comment | date |
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| submitted | published | | 2023/09/26 12:58 UTC |
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