# Cohort Organized Learning: Clustering Through Agreement

arXiv: https://arxiv.org/abs/2606.21743 · HTML: https://mlsanity.com/paper/2606.21743/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 65 references that are in the map.
- First posted on arXiv 19 Jun 2026.

## Builds on (most cited first, 21 of 21)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [Empirical Evaluation of Rectified Activations in Convolutional Network](https://mlsanity.com/paper/1505.00853/) (2015)
- [Joint Unsupervised Learning of Deep Representations and Image Clusters](https://mlsanity.com/paper/1604.03628/) (2016)
- [Neural Expectation Maximization](https://mlsanity.com/paper/1708.03498/) (2017)
- [SPICE: Semantic Pseudo-labeling for Image Clustering](https://mlsanity.com/paper/2103.09382/) (2021)
- [SpectralNet: Spectral Clustering using Deep Neural Networks](https://mlsanity.com/paper/1801.01587/) (2018)
- [A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directions](https://mlsanity.com/paper/2206.07579/) (2022)
- [Neural Manifold Clustering and Embedding](https://mlsanity.com/paper/2201.10000/) (2022)
- [Pseudo-supervised Deep Subspace Clustering](https://mlsanity.com/paper/2104.03531/) (2021)
- [ViTac: Feature Sharing between Vision and Tactile Sensing for Cloth Texture Recognition](https://mlsanity.com/paper/1802.07490/) (2018)
- [XAI Beyond Classification: Interpretable Neural Clustering](https://mlsanity.com/paper/1808.07292/) (2018)
- [Interpretable Deep Clustering for Tabular Data](https://mlsanity.com/paper/2306.04785/) (2023)
- [Deep clustering with concrete k-means](https://mlsanity.com/paper/1910.08031/) (2019)
- [A Rapid Review of Clustering Algorithms](https://mlsanity.com/paper/2401.07389/) (2024)
- [Neural Network based Explicit Mixture Models and Expectation-maximization based Learning](https://mlsanity.com/paper/1907.13432/) (2019)
- [Neural Mixture Models with Expectation-Maximization for End-to-end Deep Clustering](https://mlsanity.com/paper/2107.02453/) (2021)
- [Coincident Learning for Unsupervised Anomaly Detection](https://mlsanity.com/paper/2301.11368/) (2023)
- [Data-Driven Gradient Optimization for Field Emission Management in a Superconducting Radio-Frequency Linac](https://mlsanity.com/paper/2411.07018/) (2024)

## Abstract (arXiv)

In this article we describe Cohort Organized Learning (CoOL), a method for clustering data without explicit distance or similarity computations. Herein, we will describe CoOL, derive the gradients determined by expectation maximization to train the networks, show how to monitor convergence during training and evaluate the clusters after training, and discuss a series of examples and use cases. We also discuss CoOL's limitations and future prospects on related tasks. Because CoOL uses neural networks to estimate the clusters, it can be used to cluster any data that can be made compatible and we illustrate this on vector data and images.