mlsanityData to 14 Oct 2026

Differentiable Clustering with Perturbed Spanning Forests

arXiv 2305.16358 ↗First posted 25 May 2023cs.LG cs.AI stat.MLcited by 14

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Abstract (arXiv)

We introduce a differentiable clustering method based on stochastic perturbations of minimum-weight spanning forests. This allows us to include clustering in end-to-end trainable pipelines, with efficient gradients. We show that our method performs well even in difficult settings, such as data sets with high noise and challenging geometries. We also formulate an ad hoc loss to efficiently learn from partial clustering data using this operation. We demonstrate its performance on several data sets for supervised and semi-supervised tasks.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 31 of its 56 references that are in the map. First posted on arXiv 25 May 2023.

Builds on

31 of 56 references.

References · 31

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  4. 2020Unsupervised Learning of Visual Features by Contrasting Cluster Assignmentscited by 2,857
  5. 2016Categorical Reparameterization with Gumbel-Softmaxcited by 2,739
  6. 2013Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distancescited by 2,316
  7. 2016The Concrete Distribution: A Continuous Relaxation of Discrete Random Variablescited by 1,148
  8. 2018Deep Clustering for Unsupervised Learning of Visual Featurescited by 1,028
  9. 2015Unsupervised Deep Embedding for Clustering Analysiscited by 767
  10. 2014SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectivescited by 402
  11. 2017Soft-DTW: a Differentiable Loss Function for Time-Seriescited by 271
  12. 2016Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clusteringcited by 237
19 more
  1. 2019Differentiation of Blackbox Combinatorial Solverscited by 226
  2. 2021Efficient and Modular Implicit Differentiationcited by 203
  3. 2020Fast Differentiable Sorting and Rankingcited by 197
  4. 2019Learning with Fenchel-Young Lossescited by 119
  5. 2021A Review of the Gumbel-max Trick and its Extensions for Discrete Stochasticity in Machine Learningcited by 82
  6. 2021Differentiable Patch Selection for Image Recognitioncited by 68
  7. 2020Gradient Estimation with Stochastic Softmax Trickscited by 67
  8. 2021GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detectioncited by 66
  9. 2018Differentiable Dynamic Programming for Structured Prediction and Attentioncited by 58
  10. 2020Structured Prediction with Partial Labelling through the Infimum Losscited by 34
  11. 2023Fast, Differentiable and Sparse Top-k: a Convex Analysis Perspectivecited by 26
  12. 2019Differentiable Deep Clustering with Cluster Size Constraintscited by 22
  13. 2021Self-Supervised Learning of Audio Representations from Permutations with Differentiable Rankingcited by 14
  14. 2023Learning by Sorting: Self-supervised Learning with Group Ordering Constraintscited by 13
  15. 2020Efficient Computation of Expectations under Spanning Tree Distributionscited by 11
  16. 2013On Sampling from the Gibbs Distribution with Random Maximum A-Posteriori Perturbationscited by 7
  17. 2021Differentiable Rendering with Perturbed Optimizerscited by 7
  18. 2021Leveraging Recursive Gumbel-Max Trick for Approximate Inference in Combinatorial Spacescited by 7
  19. 2022Fast Stochastic Composite Minimization and an Accelerated Frank-Wolfe Algorithm under Parallelizationcited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 6; 2025: 3; 2026: 4.

14 papers.

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  2. 2024Uncertainty Quantification via Stable Distribution Propagationcited by 10
  3. 2025Towards Foundational Models for Dynamical System Reconstruction: Hierarchical Meta-Learning via Mixture of Expertscited by 5
  4. 2024Generalizing Stochastic Smoothing for Differentiation and Gradient Estimationcited by 4
  5. 2024Soft Condorcet Optimization for Ranking of General Agentscited by 3
  6. 2024Differentiable Cluster Graph Neural Networkcited by 2
  7. 2025Building Bridges between Regression, Clustering, and Classificationcited by 2
  8. 2026Differentiable Integer Linear Programming is not Differentiable & it's not a mere technical problemcited by 2
  9. 2024DistrictNet: Decision-aware learning for geographical districtingcited by 1
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  11. 2024CF-OPT: Counterfactual Explanations for Structured Predictioncited by 0
  12. 2026A Study on Building Efficient Zero-Shot Relation Extraction Modelscited by 0
2 more
  1. 2026Classification Fields: Arbitrarily Fine Recursive Hierarchical Clustering From Few Examplescited by 0
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