mlsanityData to 7 Oct 2026

The Limited Multi-Label Projection Layer

arXiv 1906.08707 ↗First posted 20 Jun 2019cs.LG cs.CV stat.MLcited by 21

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Abstract (arXiv)

We propose the Limited Multi-Label (LML) projection layer as a new primitive operation for end-to-end learning systems. The LML layer provides a probabilistic way of modeling multi-label predictions limited to having exactly k labels. We derive efficient forward and backward passes for this layer and show how the layer can be used to optimize the top-k recall for multi-label tasks with incomplete label information. We evaluate LML layers on top-k CIFAR-100 classification and scene graph generation. We show that LML layers add a negligible amount of computational overhead, strictly improve the model's representational capacity, and improve accuracy. We also revisit the truncated top-k entropy method as a competitive baseline for top-k classification.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Jun 2019.

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Citing papers per year: 2021: 2; 2022: 5; 2023: 5; 2024: 6; 2025: 1; 2026: 2.

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  1. 2023Decision-Focused Learning: Foundations, State of the Art, Benchmark and Future Opportunitiescited by 129
  2. 2022Tutorial on amortized optimizationcited by 83
  3. 2022Backpropagation through Combinatorial Algorithms: Identity with Projection Workscited by 29
  4. 2022Rank-based Decomposable Losses in Machine Learning: A Surveycited by 26
  5. 2023Fast, Differentiable and Sparse Top-k: a Convex Analysis Perspectivecited by 26
  6. 2023Backpropagation of Unrolled Solvers with Folded Optimizationcited by 13
  7. 2022Decision-Focused Learning without Differentiable Optimization: Learning Locally Optimized Decision Lossescited by 12
  8. 2021Regularized Frank-Wolfe for Dense CRFs: Generalizing Mean Field and Beyondcited by 10
  9. 2022ZippyPoint: Fast Interest Point Detection, Description, and Matching through Mixed Precision Discretizationcited by 10
  10. 2024LPGD: A General Framework for Backpropagation through Embedded Optimization Layerscited by 6
  11. 2024Locally Convex Global Loss Network for Decision-Focused Learningcited by 3
  12. 2023Adaptive Experimentation at Scale: A Computational Framework for Flexible Batchescited by 2
9 more
  1. 2024End-to-End Learning for Fair Multiobjective Optimization Under Uncertaintycited by 2
  2. 2024Preference-Optimized Pareto Set Learning for Blackbox Optimizationcited by 2
  3. 2025Prediction Loss Guided Decision-Focused Learningcited by 2
  4. 2023Analyzing and Enhancing the Backward-Pass Convergence of Unrolled Optimizationcited by 1
  5. 2026Differentiable Knapsack and Top-k Operators via Dynamic Programmingcited by 1
  6. 2021DiPS: Differentiable Policy for Sketching in Recommender Systemscited by 0
  7. 2024Efficient Public Health Intervention Planning Using Decomposition-Based Decision-Focused Learningcited by 0
  8. 2024End-to-End Optimization and Learning of Fair Court Schedulescited by 0
  9. 2026Differentiable Zero-One Loss via Hypersimplex Projectionscited by 0