GFM applies conditional flow matching to neural weight trajectories, predicting final weights from a short observed prefix with accuracy competitive to Transformers on synthetic and CIFAR-10 tasks.
Ian goodfellow, yoshua bengio, and aaron courville: Deep learning: The mit press, 2016, 800 pp, isbn: 0262035618.Genetic programming and evolvable machines, 19(1):305–307,
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Gradient Flow Matching for Learning Update Dynamics in Neural Network Training
GFM applies conditional flow matching to neural weight trajectories, predicting final weights from a short observed prefix with accuracy competitive to Transformers on synthetic and CIFAR-10 tasks.