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2020 ), Turing-NLG , BST ( Roller et al., 2020 ), and GPT-3 ( Brown et al., 2020 ; Fan et al., 2020 ), quantization ( Fan et al., 2020 ), and compression ( Xu et al., 2020 ; Fan et al., 2020 ), quantization ( Fan et al., 2020 ), and compression ( Xu et al., 2020 ) and Big Bird ( Zaheer et al.,
With the rise of deep learning (deep learning means multiple levels of neuralnetworks) and neuralnetworks, models such as Recurrent NeuralNetworks (RNNs) and ConvolutionalNeuralNetworks (CNNs) began to be used in NLP. 2020) “GPT-4 Technical report ” by Open AI.
BERTBERT, an acronym that stands for “Bidirectional Encoder Representations from Transformers,” was one of the first foundation models and pre-dated the term by several years. BERT proved useful in several ways, including quantifying sentiment and predicting the words likely to follow in unfinished sentences.
Vision Transformer (ViT) have recently emerged as a competitive alternative to ConvolutionalNeuralNetworks (CNNs) that are currently state-of-the-art in different image recognition computer vision tasks. No 2018 Oct BERT Pre-trained transformer models started dominating the NLP field.
GCNs use a combination of graph-based representations and convolutionalneuralnetworks to analyze large amounts of textual data. A GCN consists of multiple layers, each of which applies a graph convolution operation to the input graph. References Paperwithcode | Graph ConvolutionalNetwork Kai, S.,
A paper that exemplifies the Classifier Cage Match era is LeCun et al [ 109 ], which pits support vector machines (SVMs), k-nearest neighbor (KNN) classifiers, and convolutionneuralnetworks (CNNs) against each other to recognize images from the NORB database. The base model of BERT [ 103 ] had 12 (!) Hinton (again!)
NeurIPS’18 presented several papers with deep theoretical studies of building hyperbolic neural nets. Source: Chami et al Chami et al present Hyperbolic Graph ConvolutionalNeuralNetworks (HGCN) and Liu et al propose Hyperbolic Graph NeuralNetworks (HGNN). Thank you for reading!
Instead of complex and sequential architectures like Recurrent NeuralNetworks (RNNs) or ConvolutionalNeuralNetworks (CNNs), the Transformer model introduced the concept of attention, which essentially meant focusing on different parts of the input text depending on the context.
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