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- W4386185266 abstract "Mode connectivity is a phenomenon where trained models are connected by a path of low loss. We reframe this in the context of Information Geometry, where neural networks are studied as spaces of parameterized distributions with curved geometry. We hypothesize that shortest paths in these spaces, known as geodesics, correspond to mode-connecting paths in the loss landscape. We propose an algorithm to approximate geodesics and demonstrate that they achieve mode connectivity." @default.
- W4386185266 created "2023-08-26" @default.
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- W4386185266 date "2023-08-24" @default.
- W4386185266 modified "2023-09-26" @default.
- W4386185266 title "Geodesic Mode Connectivity" @default.
- W4386185266 doi "https://doi.org/10.48550/arxiv.2308.12666" @default.
- W4386185266 hasPublicationYear "2023" @default.
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