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It is named "chinchilla" because it is a further development over a previous model family named Gopher.Both model families were trained in order to investigate the scaling laws of large language models.
Extreme learning machines are feedforward neural networks for classification, regression, clustering, sparse approximation, compression and feature learning with a single layer or multiple layers of hidden nodes, where the parameters of hidden nodes (not just the weights connecting inputs to hidden nodes) need to be tuned.
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.
The specific algorithms SuperMemo uses have been published, and re-implemented in other programs. Different algorithms have been used; SM-0 refers to the original (non-computer-based) algorithm, while SM-2 refers to the original computer-based algorithm released in 1987 (used in SuperMemo versions 1.0 through 3.0, referred to as SM-2 because SuperMemo version 2 was the most popular of these).
Self-GenomeNet is an example of self-supervised learning in genomics. [18] Self-supervised learning continues to gain prominence as a new approach across diverse fields. Its ability to leverage unlabeled data effectively opens new possibilities for advancement in machine learning, especially in data-driven application domains.
Google also extended PaLM using a vision transformer to create PaLM-E, a state-of-the-art vision-language model that can be used for robotic manipulation. [11] [12] The model can perform tasks in robotics competitively without the need for retraining or fine-tuning. [13] In May 2023, Google announced PaLM 2 at the annual Google I/O keynote. [14]
The plain transformer architecture had difficulty converging. In the original paper [1] the authors recommended using learning rate warmup. That is, the learning rate should linearly scale up from 0 to maximal value for the first part of the training (usually recommended to be 2% of the total number of training steps), before decaying again.
In machine learning, instance-based learning (sometimes called memory-based learning [1]) is a family of learning algorithms that, instead of performing explicit generalization, compare new problem instances with instances seen in training, which have been stored in memory. Because computation is postponed until a new instance is observed ...