# Inference of Abstraction for a Unified Account of Reasoning and Learning

arXiv: https://arxiv.org/abs/2402.09046 · HTML: https://mlsanity.com/paper/2402.09046/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 23 references that are in the map.
- First posted on arXiv 14 Feb 2024.

## Builds on (most cited first, 1 of 1)

- [Neural Module Networks](https://mlsanity.com/paper/1511.02799/) (2015)

## Abstract (arXiv)

Inspired by Bayesian approaches to brain function in neuroscience, we give a simple theory of probabilistic inference for a unified account of reasoning and learning. We simply model how data cause symbolic knowledge in terms of its satisfiability in formal logic. The underlying idea is that reasoning is a process of deriving symbolic knowledge from data via abstraction, i.e., selective ignorance. The logical consequence relation is discussed for its proof-based theoretical correctness. The MNIST dataset is discussed for its experiment-based empirical correctness.