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Learning Parities with Product Networks using GD
Learning Parities with Product Networks using GD

Contents:

  • Research Paper
  • Studies
    • Run All Studies
    • Study A — Impact of \(p_e\) on convergence
    • Study B — Optimal \(p_e\) for different \(N\)
    • Study C — Impact of \(\alpha\) (learning rate)
    • Study D — Optimal \(\alpha\) for different \(N\)
    • Study E — Impact of \(p_e\) under larger batch size
    • Study F — Distribution of weights during training
    • Study G — Joint impact of \(\alpha\), \(p_e\), and batch size for different \(N\)
    • Study H — Product Node vs MLP: Sparse vs Full-Table Training
  • Results
    • Study A — Impact of Dataset Sparsity \(p_e\) on Convergence
    • Study B — Optimal Dataset Sparsity \(p_e^*\) vs Problem Size \(N\)
    • Study C — Impact of Learning Rate \(\alpha\) on Convergence
    • Study D — Optimal Learning Rate \(\alpha^*\) vs Problem Size \(N\)
    • Study E — Impact of \(p_e\) on Convergence (Large Batch)
    • Study F — Weight Distribution Dynamics During Training
    • Study G — Joint Impact of \(\alpha\), \(p_e\), and Batch Size on Convergence
    • Study H — Product Node vs MLP: Sparse vs Full-Table Training
  • API Reference
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