Fine-tuning neural PDE operators to regime endpoints reveals a physical direction in weight space that CCM uses to compose accurate merged models for new or extrapolated regimes from metadata or short prefixes.
Model fusion via optimal transport.Advances in Neural Information Processing Systems, 33:22045–22055
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Framing visual text compression as measure transport decomposes encoding loss into precision and coverage costs, enabling a label-free routing rule that matches oracle performance on 17 of 24 NLP datasets while using 10% fewer tokens.
citing papers explorer
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Discovering Physical Directions in Weight Space: Composing Neural PDE Experts
Fine-tuning neural PDE operators to regime endpoints reveals a physical direction in weight space that CCM uses to compose accurate merged models for new or extrapolated regimes from metadata or short prefixes.
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Visual Text Compression as Measure Transport
Framing visual text compression as measure transport decomposes encoding loss into precision and coverage costs, enabling a label-free routing rule that matches oracle performance on 17 of 24 NLP datasets while using 10% fewer tokens.