Researchers have developed new methods to make the Expectation-Maximisation (EM) algorithm, a common tool for statistical and machine learning tasks like Gaussian Mixture Models (GMMs), compatible with end-to-end gradient propagation. This advancement allows EM to be integrated into modern learning pipelines. The work also introduces a novel application of differentiable EM for computing the Mixture Wasserstein distance ($\mathrm{MW}_2$) between GMMs, enabling its use as a differentiable loss function in various imaging and machine learning applications, including image generation and texture synthesis. AI
IMPACT Enables integration of a common statistical algorithm into modern gradient-based learning pipelines, potentially improving performance in generative and imaging tasks.
RANK_REASON Academic paper detailing new algorithmic methods for machine learning. [lever_c_demoted from research: ic=1 ai=1.0]
- arXiv
- Eloi Tanguy
- Expectation-Maximisation
- Gaussian Mixture Models
- Hugging Face
- Mixture Wasserstein distance
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