# Weakly Supervised Learning Creates a Fusion of Modeling Cultures

arXiv: https://arxiv.org/abs/2106.01485 · HTML: https://mlsanity.com/paper/2106.01485/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 33 references that are in the map.
- First posted on arXiv 2 Jun 2021.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Understanding deep learning requires rethinking generalization](https://mlsanity.com/paper/1611.03530/) (2016)
- [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016)
- [Deep Bayesian Active Learning with Image Data](https://mlsanity.com/paper/1703.02910/) (2017)
- [Confident Learning: Estimating Uncertainty in Dataset Labels](https://mlsanity.com/paper/1911.00068/) (2019)
- [Data Programming: Creating Large Training Sets, Quickly](https://mlsanity.com/paper/1605.07723/) (2016)
- [Weakly-supervised learning of visual relations](https://mlsanity.com/paper/1707.09472/) (2017)

## Abstract (arXiv)

The past two decades have witnessed the great success of the algorithmic modeling framework advocated by Breiman et al. (2001). Nevertheless, the excellent prediction performance of these black-box models rely heavily on the availability of strong supervision, i.e. a large set of accurate and exact ground-truth labels. In practice, strong supervision can be unavailable or expensive, which calls for modeling techniques under weak supervision. In this comment, we summarize the key concepts in weakly supervised learning and discuss some recent developments in the field. Using algorithmic modeling alone under a weak supervision might lead to unstable and misleading results. A promising direction would be integrating the data modeling culture into such a framework.