Sho Sonoda
PulseAugur coverage of Sho Sonoda — every cluster mentioning Sho Sonoda across labs, papers, and developer communities, ranked by signal.
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New research explores infinite-dimensional null spaces in neural networks
A new arXiv paper by Sho Sonoda explores the existence, structure, and role of infinite-dimensional null spaces within continuous-width depth-two fully connected neural networks. The research introduces a direct method …
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Formalization of ML generalization bounds achieved in Lean 4
Researchers have formalized generalization error bounds using Rademacher complexity in the Lean 4 proof assistant. This work builds upon measure-theoretic probability theory within the Mathlib library. The formalization…
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Paper: Hierarchical provers offer exponential sample complexity gains
Researchers Sho Sonoda and others have published a paper detailing a statistical learning approach to analyze agentic theorem provers. Their work focuses on the sample complexity of imitation learning from verified proo…
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Deep learning's depth advantage explained by state-transition model
A new research paper explores the theoretical underpinnings of why deep learning models often outperform shallower ones. The study introduces an implementation-agnostic state-transition model to analyze generalization b…