mlsanityData to 14 Oct 2026

Cohort Organized Learning: Clustering Through Agreement

arXiv 2606.21743 ↗First posted 19 Jun 2026cs.MA cs.AIcited by 0

Not on the map. Why?

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.

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

21 of 65 references.

References · 21

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2017Inductive Representation Learning on Large Graphscited by 6,320
  4. 2015Unsupervised Deep Embedding for Clustering Analysiscited by 767
  5. 2015Empirical Evaluation of Rectified Activations in Convolutional Networkcited by 519
  6. 2016Joint Unsupervised Learning of Deep Representations and Image Clusterscited by 205
  7. 2017Neural Expectation Maximizationcited by 123
  8. 2021SPICE: Semantic Pseudo-labeling for Image Clusteringcited by 104
  9. 2018SpectralNet: Spectral Clustering using Deep Neural Networkscited by 94
  10. 2022A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directionscited by 73
  11. 2022Neural Manifold Clustering and Embeddingcited by 44
  12. 2021Pseudo-supervised Deep Subspace Clusteringcited by 29
9 more
  1. 2018ViTac: Feature Sharing between Vision and Tactile Sensing for Cloth Texture Recognitioncited by 27
  2. 2018XAI Beyond Classification: Interpretable Neural Clusteringcited by 24
  3. 2023Interpretable Deep Clustering for Tabular Datacited by 19
  4. 2019Deep clustering with concrete k-meanscited by 11
  5. 2024A Rapid Review of Clustering Algorithmscited by 9
  6. 2019Neural Network based Explicit Mixture Models and Expectation-maximization based Learningcited by 3
  7. 2021Neural Mixture Models with Expectation-Maximization for End-to-end Deep Clusteringcited by 2
  8. 2023Coincident Learning for Unsupervised Anomaly Detectioncited by 1
  9. 2024Data-Driven Gradient Optimization for Field Emission Management in a Superconducting Radio-Frequency Linaccited by 1

Built on by

Not cited yet.