# A Theory of Human-Like Few-Shot Learning

arXiv: https://arxiv.org/abs/2301.01047 · HTML: https://mlsanity.com/paper/2301.01047/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 9 references that are in the map.
- First posted on arXiv 3 Jan 2023.

## Built on by (most cited first, 2 of 2)

- [Lossless data compression by large models](https://mlsanity.com/paper/2407.07723/) (2024)
- [Approximating Human-Like Few-shot Learning with GPT-based Compression](https://mlsanity.com/paper/2308.06942/) (2023)

## Abstract (arXiv)

We aim to bridge the gap between our common-sense few-sample human learning and large-data machine learning. We derive a theory of human-like few-shot learning from von-Neuman-Landauer's principle. modelling human learning is difficult as how people learn varies from one to another. Under commonly accepted definitions, we prove that all human or animal few-shot learning, and major models including Free Energy Principle and Bayesian Program Learning that model such learning, approximate our theory, under Church-Turing thesis. We find that deep generative model like variational autoencoder (VAE) can be used to approximate our theory and perform significantly better than baseline models including deep neural networks, for image recognition, low resource language processing, and character recognition.