gaussian mixture models
Probabilistic models that represent a distribution as a combination of multiple Gaussian distributions, used widely in clustering and density estimation tasks where data can be assumed to derive from multiple underlying subpopulations.
- Can Diffusion Models Disentangle? A Theoretical Perspective
- Continual Gaussian Mixture Distribution Modeling for Class Incremental Semantic Segmentation
- LoSplit: Loss-Guided Dynamic Split for Training-Time Defense Against Graph Backdoor Attacks
- Understanding Contrastive Learning via Gaussian Mixture Models