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Gradient Descent on Two ReLU Neurons: Global Landscape and Bifurcation Dynamics

Oct 30, 2026 Source ↗

Key Insights

Understanding how gradient descent learns features remains a central challenge in neural network theory.
We study this question in a minimal multi-index setting that already exhibits rich multi-phase.

Cite this synthesis DOI: 10.48550/arXiv.2610.42118 ↗

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@misc{ acaciadata-research-gradient-descent-on-two-relu-neurons-global-landscape-and-bi,
  title = { Gradient Descent on Two ReLU Neurons: Global Landscape and Bifurcation Dynamics },
  author = { Leszek },
  year = { 2026 },
  doi = { 10.48550/arXiv.2610.42118 },
  url = { http://arxiv.org/abs/2610.42118v1 },
  note = {Summarized and classified by AcaciaFund}
}

TY  - GEN
TI  - Gradient Descent on Two ReLU Neurons: Global Landscape and Bifurcation Dynamics
AU  - Leszek
PY  - 2026
DO  - 10.48550/arXiv.2610.42118
UR  - http://arxiv.org/abs/2610.42118v1
ER  -

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Overview

Understanding how gradient descent learns features remains a central challenge in neural network theory. We study this question in a minimal multi-index setting that already exhibits rich multi-phase

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Query Optimization: A theme covered by this item within the data engineering context.

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Article Metadata

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dataops query-optimization machine-learning

Query Optimization DataOps

SQI 0.750