LeCun: High-Dimensional Parameter Spaces Ease Model Estimation
CSProfKGD · x · 2026-08-22
Yann LeCun shared a fundamental perspective on machine learning:
- Input Dimensions: High-dimensional inputs make modeling harder.
- Parameter Space Dimensions: High-dimensional parameter spaces make model estimation easier.
Supporting Arguments:
- Procedures like ADMM and EM artificially increase parameter space dimension with auxiliary variables to ease optimization.
- Kernel methods rely on the idea of one parameter per training sample.
- The intuition that larger neural nets are easier to train (eliminating local minima) is long-standing.
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