A G-only collective kernel EFT for pre-activation ResNets yields accurate mean kernel predictions at all depths but accumulates O(1) errors in covariance and fails for the 1/n correction due to source closure breakdown.
and Yaida, Sho and Hanin, Boris
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Information defined as maximum-caliber deviation derives IIT 3.0 cause-effect repertoires from constrained entropy maximization and equates to prediction error under CLT and LDT.
Lectures reviewing three established numerical methods for inverse problems in extracting PDFs and spectral functions from lattice QCD and experimental data.
A survey of variational quantum algorithms, quantum neural networks, and tensor networks for addressing scalability challenges in computational fluid dynamics.
Review of neural scaling laws and their relation to constraints and inductive biases when applying machine learning to physics problems.
citing papers explorer
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Collective Kernel EFT for Pre-activation ResNets
A G-only collective kernel EFT for pre-activation ResNets yields accurate mean kernel predictions at all depths but accumulates O(1) errors in covariance and fails for the 1/n correction due to source closure breakdown.
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Information as Maximum-Caliber Deviation: A bridge between Integrated Information Theory and the Free Energy Principle
Information defined as maximum-caliber deviation derives IIT 3.0 cause-effect repertoires from constrained entropy maximization and equates to prediction error under CLT and LDT.
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Some Inverse Problems in Particle Physics
Lectures reviewing three established numerical methods for inverse problems in extracting PDFs and spectral functions from lattice QCD and experimental data.
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A review of quantum machine learning and quantum-inspired applied methods to computational fluid dynamics
A survey of variational quantum algorithms, quantum neural networks, and tensor networks for addressing scalability challenges in computational fluid dynamics.
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Statistical Properties of Training & Generalization
Review of neural scaling laws and their relation to constraints and inductive biases when applying machine learning to physics problems.