mlsanityData to 13 Oct 2026

DISCO Nets: DISsimilarity COefficient Networks

arXiv 1606.02556 ↗First posted 8 Jun 2016cs.CV cs.AIcited by 18

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Abstract (arXiv)

We present a new type of probabilistic model which we call DISsimilarity COefficient Networks (DISCO Nets). DISCO Nets allow us to efficiently sample from a posterior distribution parametrised by a neural network. During training, DISCO Nets are learned by minimising the dissimilarity coefficient between the true distribution and the estimated distribution. This allows us to tailor the training to the loss related to the task at hand. We empirically show that (i) by modeling uncertainty on the output value, DISCO Nets outperform equivalent non-probabilistic predictive networks and (ii) DISCO Nets accurately model the uncertainty of the output, outperforming existing probabilistic models based on deep neural networks.

Key facts. Cited by 18 papers in the map (data through 13 Oct 2026). First posted on arXiv 8 Jun 2016.

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Citing papers per year: 2021: 2; 2022: 3; 2023: 2; 2024: 2; 2025: 6; 2026: 3.

18 papers.

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  9. 2024A Gentle Introduction and Tutorial on Deep Generative Models in Transportation Researchcited by 4
  10. 2022Sample-based Uncertainty Quantification with a Single Deterministic Neural Networkcited by 3
  11. 2023Latent-Conditioned Policy Gradient for Multi-Objective Deep Reinforcement Learningcited by 1
  12. 2025Multivariate Uncertainty Quantification with Tomographic Quantile Forestscited by 1
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  4. 2025One-shot Conditional Sampling: MMD meets Nearest Neighborscited by 0
  5. 2026Calibrating Generative Models to Feature Distributions with MMD Finetuningcited by 0
  6. 2026Optimizing ML Workload Partitioning between CPUs and CIM Accelerators for Heterogeneous Computingcited by 0