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Showing 1 to 3 of 3 for “"Mixture of Experts Models"”.
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A Class of Mixture of Experts Models for General Insurance Ratemaking and Reserving
Understanding the effect of policyholders' risk profile on the number and the amount of claims, as well as the dependence among different types of claims, are critical to insurance ratemaking and IBNR-type reserving. To accurately quantify such features, it is essential to develop a regression …
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Load balancing and memory optimizations for expert parallel training of large language models
Large language models (LLMs) are an effective way to solve many text-based machine learning tasks, but require huge amounts of computation to train and evaluate. Mixture of experts models have emerged as a way to reduce the amount of computation required for LLMs without compromising accuracy. It …
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Improving Deep Learning with Probabilistic Approaches
… be used to improve deep learning?'' On the topic of uncertainty estimation, we have three sets of contributions. Firstly, we show that probabilistic inference over the depth of a neural network not only side-steps challenges involved with scaling inference to the large weight spaces of modern …