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Deep Learning Theory: Neural Networks as Models of Computation

By PulseAugur Editorial · [2 sources] ·

A new paper explores the theoretical underpinnings of deep learning, proposing that neural networks should be understood not just as function approximators but also as models of computation. The research, authored by Anastasis Kratsios, demonstrates that neural networks can emulate complex numerical algorithms like Newton's method. The work establishes that neural network complexity is influenced by algorithmic complexity in addition to regularity, providing universal approximation guarantees for various function classes. AI

IMPACT This research reframes the understanding of neural network capabilities, potentially influencing future model architectures and theoretical analyses.

RANK_REASON The cluster contains an academic paper detailing theoretical advancements in deep learning.

Read on arXiv cs.LG →

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

Deep Learning Theory: Neural Networks as Models of Computation

COVERAGE [2]

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

    Algorithmic Foundations of Deep Learning: Complexity-Theoretic Rates and a Characterization of Universal Approximation

    arXiv:2606.26705v1 Announce Type: cross Abstract: Feedforward neural network (NN) expressivity is typically studied by emulating optimal basis-expansion schemes. While powerful, this perspective is incomplete: it primarily captures complexity through regularity, and therefore doe…

  2. arXiv cs.LG TIER_1 English(EN) · Haitz Sáez de Ocáriz Borde ·

    Algorithmic Foundations of Deep Learning: Complexity-Theoretic Rates and a Characterization of Universal Approximation

    Feedforward neural network (NN) expressivity is typically studied by emulating optimal basis-expansion schemes. While powerful, this perspective is incomplete: it primarily captures complexity through regularity, and therefore does not distinguish intuitively simple and complicat…

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