# Deep-testing: the case of dependence detection

arXiv: https://arxiv.org/abs/2604.26558 · HTML: https://mlsanity.com/paper/2604.26558/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 73 references that are in the map.
- First posted on arXiv 29 Apr 2026.

## Builds on (most cited first, 3 of 3)

- [The Randomized Dependence Coefficient](https://mlsanity.com/paper/1304.7717/) (2013)
- [Lets keep it simple, Using simple architectures to outperform deeper and more complex architectures](https://mlsanity.com/paper/1608.06037/) (2016)
- [Towards a universal representation of statistical dependence](https://mlsanity.com/paper/2302.08151/) (2023)

## Abstract (arXiv)

Deep learning methods have proved highly effective for classification and image recognition problems. In this paper, we ask whether this success can be transferred to hypothesis testing: if a neural network can distinguish, for example, an image of a handwritten digit from another, can it also distinguish an "image of a sample" (such as a scatter plot) generated under a given statistical model from one generated outside that model? Motivated by this idea, we propose a novel procedure called deep-testing, which approaches the classical inferential problem of hypothesis testing through deep learning. More specifically, the test statistic is a classification map learned by a deep neural network from simulated data satisfying the null and alternative hypotheses, leveraging its strong discriminating power to construct a highly powerful test. As a proof of concept, we apply deep-testing to the problem of independence testing, arguably one of the most important problems in statistics. In a large-scale simulation study, deep-testing achieves the highest overall power against nineteen competing methods across a broad range of complex dependence structures, confirming the viability of the proposed approach.