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The mixture of experts, being similar to the gaussian mixture model, can also be trained by the expectation-maximization algorithm, just like gaussian mixture models. Specifically, during the expectation step, the "burden" for explaining each data point is assigned over the experts, and during the maximization step, the experts are trained to ...
DBRX is an open-sourced large language model (LLM) developed by Mosaic ML team at Databricks, released on March 27, 2024. [1] [2] [3] It is a mixture-of-experts transformer model, with 132 billion parameters in total. 36 billion parameters (4 out of 16 experts) are active for each token. [4]
A committee machine is a type of artificial neural network using a divide and conquer strategy in which the responses of multiple neural networks (experts) are combined into a single response. [1] The combined response of the committee machine is supposed to be superior to those of its constituent experts. Compare with ensembles of classifiers.
MoE Mamba represents a pioneering integration of the Mixture of Experts (MoE) technique with the Mamba architecture, enhancing the efficiency and scalability of State Space Models (SSMs) in language modeling.
The mixture of experts (MoE) is a machine learning paradigm that incorporates FRP by dividing a complex problem into simpler, manageable sub-tasks, each handled by a specialized expert. [8] In the filtering stage, a gating mechanism—acting as a filter that determines the most suitable expert for each specific part of the input data based on ...
Product of experts (PoE) is a machine learning technique. It models a probability distribution by combining the output from several simpler distributions. It was proposed by Geoffrey Hinton in 1999, [1] along with an algorithm for training the parameters of such a system.
For instance, mixtures of Gaussian process experts, where the number of required experts must be inferred from the data. [8] [9] As draws from a Dirichlet process are discrete, an important use is as a prior probability in infinite mixture models. In this case, is the parametric set of component distributions. The generative process is ...
In machine learning, weighted majority algorithm (WMA) is a meta learning algorithm used to construct a compound algorithm from a pool of prediction algorithms, which could be any type of learning algorithms, classifiers, or even real human experts.