uGMM-NN: Neural Network Architecture with Gaussian Mixture Model Neurons for Probabilistic Reasoning
By
zakeria
Toasted just enough. A reliable bake, gently seasoned.
Summary
This research paper introduces uGMM-NN (Univariate Gaussian Mixture Model Neural Network), a novel neural architecture that embeds probabilistic reasoning directly into neural network units. Unlike traditional neurons with fixed nonlinearities, each uGMM-NN node parameterizes activations as univariate Gaussian mixtures with learnable parameters (means, variances, mixing coefficients). This enables richer representations by capturing multimodality and uncertainty at the individual neuron level while maintaining scalability. The framework achieves competitive performance compared to standard MLPs while providing probabilistic activation interpretations, opening new directions for both discriminative and generative modeling.
Key quotes
· 4 pulledeach uGMM-NN node parameterizes its activations as a univariate Gaussian mixture, with learnable means, variances, and mixing coefficients
This design enables richer representations by capturing multimodality and uncertainty at the level of individual neurons
The proposed framework provides a foundation for integrating uncertainty-aware components into modern neural architectures
opening new directions for both discriminative and generative modeling
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