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Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. [1]
Definition of an ontology – taxonomy – of concepts needed to describe tasks in the topic addressed. Each concept and all their attributes are defined in natural-language words. This ontology will define the data structures the NLP can use in sentences. Definition of one or more top-level sentences in terms of concepts from the ontology.
In machine learning, knowledge distillation or model distillation is the process of transferring knowledge from a large model to a smaller one. While large models (such as very deep neural networks or ensembles of many models) have more knowledge capacity than small models, this capacity might not be fully utilized.
Natural language processing (NLP) is a subfield of computer science and especially artificial intelligence.It is primarily concerned with providing computers with the ability to process data encoded in natural language and is thus closely related to information retrieval, knowledge representation and computational linguistics, a subfield of linguistics.
The adaptive mixtures of local experts [5] [6] uses a gaussian mixture model.Each expert simply predicts a gaussian distribution, and totally ignores the input. Specifically, the -th expert predicts that the output is (,), where is a learnable parameter.
A large language model (LLM) is a type of machine learning model designed for natural language processing tasks such as language generation.LLMs are language models with many parameters, and are trained with self-supervised learning on a vast amount of text.
Methods to achieve this task are varied and span many disciplines; most well known among them are machine learning and statistics. Classification and prediction tasks aim at building models that describe and distinguish classes or concepts for future prediction. The differences between them are the following:
Many real-world applications fall between the two extremes, for instance text classification for the automatic analysis of emails and their routing to a suitable department in a corporation does not require an in-depth understanding of the text, [22] but needs to deal with a much larger vocabulary and more diverse syntax than the management of ...