# Deep Gaussian Mixture Models

arXiv: https://arxiv.org/abs/1711.06929 · HTML: https://mlsanity.com/paper/1711.06929/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2017.

## Built on by (most cited first, 30 of 30)

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- [Image Modeling with Deep Convolutional Gaussian Mixture Models](https://mlsanity.com/paper/2104.12686/) (2021)
- [Gradient-free variational learning with conditional mixture networks](https://mlsanity.com/paper/2408.16429/) (2024)
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- [IntTrajSim: Trajectory Prediction for Simulating Multi-Vehicle driving at Signalized Intersections](https://mlsanity.com/paper/2506.08957/) (2025)
- [Probabilistic partition of unity networks: clustering based deep approximation](https://mlsanity.com/paper/2107.03066/) (2021)
- [A new perspective on probabilistic image modeling](https://mlsanity.com/paper/2203.11034/) (2022)
- [A Multitask Deep Learning Model for Classification and Regression of Hyperspectral Images: Application to the large-scale dataset](https://mlsanity.com/paper/2407.16384/) (2024)
- [SKETCH: Structured Knowledge Enhanced Text Comprehension for Holistic Retrieval](https://mlsanity.com/paper/2412.15443/) (2024)
- [Designing Object Detection Models for TinyML: Foundations, Comparative Analysis, Challenges, and Emerging Solutions](https://mlsanity.com/paper/2508.08352/) (2025)
- [PMODE: Theoretically Grounded and Modular Mixture Modeling](https://mlsanity.com/paper/2508.21396/) (2025)
- [How to Relieve Distribution Shifts in Semantic Segmentation for Off-Road Environments](https://mlsanity.com/paper/2605.29599/) (2026)
- [CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeters](https://mlsanity.com/paper/2606.04165/) (2026)
- [Characterizing Heterogeneous Rates in Finite Mixture Estimation via Partial Optimal Transport](https://mlsanity.com/paper/2609.16622/) (2026)
- [Tabular GANs for uneven distribution](https://mlsanity.com/paper/2010.00638/) (2020)
- [Noise-Resistant Deep Metric Learning with Probabilistic Instance Filtering](https://mlsanity.com/paper/2108.01431/) (2021)
- [Fitting large mixture models using stochastic component selection](https://mlsanity.com/paper/2110.04776/) (2021)
- [Encoding large information structures in linear algebra and statistical models](https://mlsanity.com/paper/2201.08233/) (2022)
- [Large-scale gradient-based training of Mixtures of Factor Analyzers](https://mlsanity.com/paper/2308.13778/) (2023)
- [Scoring rule nets: beyond mean target prediction in multivariate regression](https://mlsanity.com/paper/2409.14456/) (2024)
- [Latent Action Priors for Locomotion with Deep Reinforcement Learning](https://mlsanity.com/paper/2410.03246/) (2024)
- [Application of Generative Adversarial Network (GAN) for Synthetic Training Data Creation to improve performance of ANN Classifier for extracting Built-Up pixels from Landsat Satellite Imagery](https://mlsanity.com/paper/2501.19283/) (2025)
- [Differentiable Expectation-Maximisation and Applications to Gaussian Mixture Model Optimal Transport](https://mlsanity.com/paper/2509.02109/) (2025)
- [Rethinking Nonlinearity: Trainable Gaussian Mixture Modules for Modern Neural Architectures](https://mlsanity.com/paper/2510.06660/) (2025)
- [Classification EM-PCA for clustering and embedding](https://mlsanity.com/paper/2511.18992/) (2025)
- [Joint Representation Learning and Clustering via Gradient-Based Manifold Optimization](https://mlsanity.com/paper/2604.13484/) (2026)
- [Deep Skew-t Mixture Models](https://mlsanity.com/paper/2609.00773/) (2026)

## Abstract (arXiv)

Deep learning is a hierarchical inference method formed by subsequent multiple layers of learning able to more efficiently describe complex relationships. In this work, Deep Gaussian Mixture Models are introduced and discussed. A Deep Gaussian Mixture model (DGMM) is a network of multiple layers of latent variables, where, at each layer, the variables follow a mixture of Gaussian distributions. Thus, the deep mixture model consists of a set of nested mixtures of linear models, which globally provide a nonlinear model able to describe the data in a very flexible way. In order to avoid overparameterized solutions, dimension reduction by factor models can be applied at each layer of the architecture thus resulting in deep mixtures of factor analysers.