Learning in High Dimension Always Amounts to Extrapolation
Overview
Paper Summary
This paper argues that in high-dimensional data (like images), machine learning models almost always extrapolate rather than interpolate, meaning they make predictions for data points outside the range of their training data. Surprisingly, the authors find that this extrapolation doesn't necessarily hurt performance and might even be crucial for the success of current models.
Explain Like I'm Five
Imagine teaching a computer to recognize cats from a few pictures. This paper shows that when there are many details (high dimensions), the computer effectively guesses what new cats look like, rather than just memorizing the training examples.
Possible Conflicts of Interest
Authors are employed by Facebook AI Research, which has a vested interest in advancing machine learning techniques.
Identified Limitations
Rating Explanation
Strong theoretical and empirical evidence challenging common assumptions about interpolation in high-dimensional data. The limited practical demonstrations and potential oversimplification of "interpolation regime" prevent a top rating.
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