Weibull
PulseAugur coverage of Weibull — every cluster mentioning Weibull across labs, papers, and developer communities, ranked by signal.
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New method tackles semi-supervised classification with informative missing labels
Researchers have developed a new semi-supervised classification method for data with missing labels, specifically addressing scenarios where the probability of a missing label is dependent on the observed features. This…
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New survival models enhance e-commerce repurchase prediction
Researchers have developed a new approach to predicting customer repurchase behavior in e-commerce using survival models, which directly estimate the time until a repurchase occurs. This method replaces multiple binary …
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New research links data predictability to transformer weight scaling
A new research paper proposes that the weight magnitudes in trained transformers can be described by a Weibull distribution. The study identifies a pre-training statistic, the bigram conditional entropy, as a key predic…
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Weibull framework reveals AdamW training dynamics in transformers
A new research paper explores the evolution of weight-scale parameters in transformer models during AdamW training. The study derives a three-force decomposition of the squared weight norm, identifying alignment, inject…
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TabSurv adapts tabular neural networks for improved survival analysis
Researchers have introduced TabSurv, a novel approach that adapts modern tabular neural network architectures for survival analysis tasks. This method utilizes a new histogram loss function called SurvHL, which is desig…
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SHIFT estimator improves robust double machine learning for heavy-tailed data
Researchers have developed SHIFT, a new robust estimator for Double Machine Learning (DML) pipelines designed to handle heavy-tailed data contamination. SHIFT combines cross-fit nuisance orthogonalization with a kernel-…