Bidirectional recurrent neural networks (BRNN) connect two hidden layers of opposite directions to the same output. With this form of generative deep...

In artificial neural networks, recurrent neural networks (RNNs) are designed for processing sequential data, such as text, speech, and time series, where...

Gated recurrent units (GRUs) are a gating mechanism in recurrent neural networks, introduced in 2014 by Kyunghyun Cho et al. The GRU is like a long short-term...

Qian Y, Xie F, Soong FK (2014). "TTS synthesis with bidirectional LSTM based Recurrent Neural Networks". Proceedings of the Annual Conference of the International...

networks, deep belief networks, recurrent neural networks, convolutional neural networks, generative adversarial networks, transformers, and neural radiance...

as recurrent neural networks and convolutional neural networks, renewed interest in ANNs. The 2010s saw the development of a deep neural network (i.e...

types of artificial neural networks (ANN). Artificial neural networks are computational models inspired by biological neural networks, and are used to approximate...

generation was done by using plain recurrent neural networks (RNNs). A well-cited early example was the Elman network (1990). In theory, the information...

A Hopfield network (or associative memory) is a form of recurrent neural network, or a spin glass system, that can serve as a content-addressable memory...

(bidirectional encoder representations from transformers). For many years, sequence modelling and generation was done by using plain recurrent neural networks...

texts scraped from the public internet). They have superseded recurrent neural network-based models, which had previously superseded the purely statistical...

developed by Ian Goodfellow and his colleagues in June 2014. In a GAN, two neural networks compete with each other in the form of a zero-sum game, where one agent's...

Long short-term memory (LSTM) is a type of recurrent neural network (RNN) aimed at mitigating the vanishing gradient problem commonly encountered by traditional...

Bidirectional encoder representations from transformers (BERT) is a language model introduced in October 2018 by researchers at Google. It learns to represent...

modeling Transformer (machine learning model) State-space model Recurrent neural network The name comes from the sound when pronouncing the 'S's in S6,...

models trained from scratch. A Boltzmann machine is a type of stochastic neural network invented by Geoffrey Hinton and Terry Sejnowski in 1985. Boltzmann machines...

Hence, some early neural networks bear the name Boltzmann Machine. Paul Smolensky calls − E {\displaystyle -E\,} the Harmony. A network seeks low energy...

paths in artificial neural networks. To overcome this problem, Schmidhuber (1991) proposed a hierarchy of recurrent neural networks (RNNs) pre-trained...

Neural oscillations, or brainwaves, are rhythmic or repetitive patterns of neural activity in the central nervous system. Neural tissue can generate oscillatory...

in: SICK-R: 0.888 and SICK-E: 87.8 using a concatenation of bidirectional Gated recurrent unit. Distributional semantics Word embedding Scholia has a...

recognition. However, more recently, LSTM and related recurrent neural networks (RNNs), Time Delay Neural Networks(TDNN's), and transformers have demonstrated improved...

{\displaystyle {\frac {dx}{dt}}=-\lambda x+f(Wx)} . Bidirectional networks are similar to Hopfield networks, with the special case that the matrix W {\displaystyle...

problem of machine translation was solved[citation needed] by recurrent neural networks, with attention mechanism added. This was optimized into the transformer...

is a type of neural network output and associated scoring function, for training recurrent neural networks (RNNs) such as LSTM networks to tackle sequence...

detected in the motor cortex, utilizing Hidden Markov models and recurrent neural networks. Since researchers from UCSF initiated a brain-computer interface...

speech recognition using two deep convolutional neural networks that build on each other. Google's Bidirectional Encoder Representations from Transformers (BERT)...

“banana” to the different pattern “monkey.” Bidirectional associative memories (BAM) are artificial neural networks that have long been used for performing...

1991. Fetz, E.E. Dynamic recurrent neural network models of sensorimotor behavior, in THE NEUROBIOLOGY OF NEURAL NETWORKS, Daniel Gardner, Ed. MIT Press...

result in high label prediction accuracy. Examples include supervised neural networks, multilayer perceptrons, and dictionary learning. In unsupervised feature...

of itself Bidirectional associative memory, a type of recurrent neural network Hopfield network, a form of recurrent artificial neural network Transderivational...