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Neural networks possess finite sample complexity, paper shows

By PulseAugur Editorial · [2 sources] ·

A new paper demonstrates that a wide range of feedforward neural network architectures possess finite sample complexity. This means they can learn effectively in the PAC model, even with unbounded parameters. The findings suggest that learnability is a baseline property for many modern architectures, shifting research focus to other aspects like inductive biases and optimization. AI

IMPACT Establishes finite sample complexity as a baseline for many neural network architectures, redirecting research focus to other architectural properties.

RANK_REASON The cluster contains an academic paper detailing theoretical findings about neural network learnability.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Neural networks possess finite sample complexity, paper shows

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COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Anastasis Kratsios, Gregory Cousins, Haitz S\'aez de Oc\'ariz Borde, Bum Jun Kim, Simone Brugiapaglia ·

    Every Feedforward Neural Network Definable in an o-Minimal Structure Has Finite Sample Complexity

    arXiv:2605.07097v1 Announce Type: new Abstract: We show that, in a precise sense, a broad class of feedforward neural networks learn (have finite sample complexity) in the PAC model: every fixed finite feedforward architecture whose layers are definable in an o-minimal structure …

  2. arXiv stat.ML TIER_1 English(EN) · Simone Brugiapaglia ·

    Every Feedforward Neural Network Definable in an o-Minimal Structure Has Finite Sample Complexity

    We show that, in a precise sense, a broad class of feedforward neural networks learn (have finite sample complexity) in the PAC model: every fixed finite feedforward architecture whose layers are definable in an o-minimal structure has finite sample complexity in the agnostic PAC…

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