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Mixture of experts (MoE) is a machine learning technique where multiple expert networks (learners) are used to divide a problem space into homogeneous regions. [1] MoE represents a form of ensemble learning.
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.
Mamba [a] is a deep learning architecture focused on sequence modeling. It was developed by researchers from Carnegie Mellon University and Princeton University to address some limitations of transformer models , especially in processing long sequences.
His goal was to use this autonomous agent to create a virtual player in Quake III Arena that can learn from gameplay. Since its original 1.0.0 version release, the library's functions have been expanded by the creator and its many contributors to include more practical constructors , different activation functions , simpler access to parameters ...
Performance of AI models on various benchmarks from 1998 to 2024. In machine learning, a neural scaling law is an empirical scaling law that describes how neural network performance changes as key factors are scaled up or down.
Chainer was the first deep learning framework to introduce the define-by-run approach. [ 10 ] [ 11 ] The traditional procedure to train a network was in two phases: define the fixed connections between mathematical operations (such as matrix multiplication and nonlinear activations) in the network, and then run the actual training calculation.
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.
scikit-learn includes a Python implementation of DBSCAN for arbitrary Minkowski metrics, which can be accelerated using k-d trees and ball trees but which uses worst-case quadratic memory. A contribution to scikit-learn provides an implementation of the HDBSCAN* algorithm.