High-Dimensional Function Approximation
Thomas explains the challenges of approximating functions in high dimensions due to sparse data distribution. In high-dimensional settings, test examples may differ significantly from training data, requiring the introduction of inductive biases to ensure model robustness.In this clip
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Machine Learning Street Talk (MLST)
061: Interpolation, Extrapolation and Linearisation (Prof. Yann LeCun, Dr. Randall Balestriero)
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