Neural variational inference

Neural variational inference is a machine learning technique that uses neural networks to approximate complex posterior distributions in probabilistic models. It combines variational inference with deep learning to enable scalable and flexible inference in models where exact Bayesian inference is intractable.

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Word error rate (WER)

Word error rate (WER) is a metric used to evaluate the performance of speech recognition systems by quantifying the differences between a recognized word sequence and a reference transcript. It is calculated based on the number of insertions, deletions, and substitutions needed to transform the hypothesis into the reference.

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Capsule neural network

A capsule neural network is an advanced type of artificial neural network designed to improve the recognition of objects in images by preserving hierarchical relationships and spatial information. It addresses limitations of traditional convolutional neural networks by grouping neurons into capsules that capture pose and other properties.

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GLM (language model)

GLM (General Language Model) is a type of pre-trained language model developed primarily in China that supports both English and Chinese language understanding and generation tasks. It is designed to perform various natural language processing applications, including text generation, completion, and understanding.

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