Summary of Towards Exact Computation Of Inductive Bias, by Akhilan Boopathy and William Yue and Jaedong Hwang and Abhiram Iyer and Ila Fiete
Towards Exact Computation of Inductive Bias
by Akhilan Boopathy, William Yue, Jaedong Hwang, Abhiram Iyer, Ila Fiete
First submitted to arxiv on: 22 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Machine learning involves finding suitable inductive biases to promote generalization on tasks. However, quantifying the amount of inductive bias associated with these architectures and hyperparameters has been limited. This paper proposes a novel method for efficiently computing the inductive bias required for generalization on a task with a fixed training data budget. The approach models the loss distribution of random hypotheses drawn from a hypothesis space to estimate the required inductive bias for a task relative to these hypotheses. Unlike prior work, this method provides a direct estimate of inductive bias without using bounds and is applicable to diverse hypothesis spaces. The paper derives approximation error bounds for its estimation approach in terms of the number of sampled hypotheses. Empirical results demonstrate that higher-dimensional tasks require greater inductive bias. Neural networks as a model class encode large amounts of inductive bias, and our proposed metric provides an information-theoretic interpretation of the benefits of specific model architectures for certain tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to make machine learning models better at predicting things we don’t already know. It’s like finding the right puzzle piece that fits perfectly with what we have learned so far. The researchers came up with a new way to measure this “puzzle piece” and showed that some types of models need more pieces than others. This is important because it helps us build better models that can solve harder problems. |
Keywords
» Artificial intelligence » Generalization » Machine learning
