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    <title>Deep-Learning on David Wilde</title>
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      <title>Adapting Karpathy&#39;s Autoresearch to Numerai: 358 Experiments Later</title>
      <link>https://david.wilde-ventures.com/posts/adapting-karpathy-autoresearch-to-numerai/</link>
      <pubDate>Thu, 26 Mar 2026 00:00:00 +0000</pubDate>
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      <description>&lt;p&gt;&lt;strong&gt;TL;DR&lt;/strong&gt;: I adapted Karpathy&amp;rsquo;s autoresearch to optimize a neural network for the Numerai tournament. 358 autonomous experiments, 22 kept (6% hit rate), payout improved from -0.008 to 0.0280. Key adaptations: era-purged CV, multi-seed validation gates, synthesized learnings after each iteration, and a DO NOT RETRY table to prevent re-exploring exhausted search regions. Biggest surprise: every fancy tabular DL architecture (FT-Transformer, TabNet, GLU, MoE) lost to a plain 3-layer feedforward net with GELU.&lt;/p&gt;</description>
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