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Showing 1 to 3 of 3 for “"Mixture of Experts Models"”.

  1. 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 …

    toronto-retro Repository record for A Class of Mixture of Experts Models for General Insurance Ratemaking and Reserving (opens in a new tab)

  2. 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 …

    mit Repository record for Load balancing and memory optimizations for expert parallel training of large language models (opens in a new tab)

  3. 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 …

    cambridge Repository record for Improving Deep Learning with Probabilistic Approaches (opens in a new tab)