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  "path": "/abs/2602.17423v1",
  "publishedAt": "2026-02-20T01:00:00.000Z",
  "site": "https://arxiv.org",
  "tags": [
    "Afroditi Kolomvaki",
    "Fangshuo Liao",
    "Evan Dramko",
    "Ziyun Guang",
    "Anastasios Kyrillidis"
  ],
  "textContent": "**Authors:** Afroditi Kolomvaki, Fangshuo Liao, Evan Dramko, Ziyun Guang, Anastasios Kyrillidis\n\nWe investigate the convergence guarantee of two-layer neural network training with Gaussian randomly masked inputs. This scenario corresponds to Gaussian dropout at the input level, or noisy input training common in sensor networks, privacy-preserving training, and federated learning, where each user may have access to partial or corrupted features. Using a Neural Tangent Kernel (NTK) analysis, we demonstrate that training a two-layer ReLU network with Gaussian randomly masked inputs achieves linear convergence up to an error region proportional to the mask's variance. A key technical contribution is resolving the randomness within the non-linear activation, a problem of independent interest.",
  "title": "Convergence Analysis of Two-Layer Neural Networks under Gaussian Input Masking"
}