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The Long Short-Term Memory (LSTM) cell can process data sequentially and keep its hidden state through time. Long short-term memory (LSTM) [1] is a type of recurrent neural network (RNN) aimed at dealing with the vanishing gradient problem [2] present in traditional RNNs. Its relative insensitivity to gap length is its advantage over other RNNs ...
Residual neural network. A Residual Block in a deep Residual Network. Here the Residual Connection skips two layers. A residual neural network (also referred to as a residual network or ResNet) [1] is a deep learning architecture in which the weight layers learn residual functions with reference to the layer inputs.
v. t. e. A large language model (LLM) is a computational model capable of language generation or other natural language processing tasks. As language models, LLMs acquire these abilities by learning statistical relationships from vast amounts of text during a self-supervised and semi-supervised training process.
Time Aware LSTM ( T-LSTM) is a long short-term memory (LSTM) unit capable of handling irregular time intervals in longitudinal patient records. T-LSTM was developed by researchers from Michigan State University, IBM Research, and Cornell University and was first presented in the Knowledge Discovery and Data Mining (KDD) conference. [1 ...
v. t. e. Original GPT model. Generative pre-trained transformers (GPTs) are a type of large language model (LLM) [1][2][3] and a prominent framework for generative artificial intelligence. [4][5] They are artificial neural networks that are used in natural language processing tasks. [6] GPTs are based on the transformer architecture, pre ...
Mathematical foundations. Journals and conferences. Related articles. v. t. e. Generative Pre-trained Transformer 2 (GPT-2) is a large language model by OpenAI and the second in their foundational series of GPT models. GPT-2 was pre-trained on a dataset of 8 million web pages. [2] It was partially released in February 2019, followed by full ...
v. t. e. Gated recurrent units (GRUs) are a gating mechanism in recurrent neural networks, introduced in 2014 by Kyunghyun Cho et al. [1] The GRU is like a long short-term memory (LSTM) with a gating mechanism to input or forget certain features, [2] but lacks a context vector or output gate, resulting in fewer parameters than LSTM. [3]
Hyperparameter (machine learning) In machine learning, a hyperparameter is a parameter, such as the learning rate or choice of optimizer, which specifies details of the learning process, hence the name hyper parameter. This is in contrast to parameters which determine the model itself. An additional contrast is that hyperparameters typically ...