Two adaptive kernel selection techniques for Kernelized Diffusion Maps are developed, backed by proofs of Lipschitz dependence on kernel weights, spectral projector continuity under gap conditions, residual control, and exponential consistency of the selector.
Gradient-Free Training of Recurrent Neural Networks
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Hamiltonian Graph Networks achieve 150-600x faster training via random feature parameter construction while retaining comparable accuracy and physical invariances on N-body systems up to 10,000 particles.
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Adaptive Kernel Selection for Kernelized Diffusion Maps
Two adaptive kernel selection techniques for Kernelized Diffusion Maps are developed, backed by proofs of Lipschitz dependence on kernel weights, spectral projector continuity under gap conditions, residual control, and exponential consistency of the selector.
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Rapid training of Hamiltonian graph networks using random features
Hamiltonian Graph Networks achieve 150-600x faster training via random feature parameter construction while retaining comparable accuracy and physical invariances on N-body systems up to 10,000 particles.