Generalized Additive Models
PulseAugur coverage of Generalized Additive Models — every cluster mentioning Generalized Additive Models across labs, papers, and developer communities, ranked by signal.
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New neural models enhance interpretability and efficiency with feature selection
Researchers have developed new neural additive and basis models that incorporate feature selection to improve computational efficiency and model size. These models, proposed by Shinichi Shirakawa, build upon generalized…
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New Tensor Separation Learning model enhances ML interpretability
Researchers have introduced Tensor Separation Learning (TSL), a novel regression model designed to improve interpretability in machine learning. Unlike existing methods that rely on additive representations, TSL uses a …
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New algorithms tackle mixture models with Fourier transforms
Researchers have developed a new algorithm for learning mixture models that can handle heavy-tailed distributions, a significant improvement over previous methods that relied on low-degree moments. This novel approach u…