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

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

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

to obtain outputs (inputs-to-output): feedforward. Recurrent neural networks, or neural networks with loops allow information from later processing stages...

In machine learning, a neural network (also artificial neural network or neural net, abbreviated ANN or NN) is a computational model inspired by the structure...

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

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...

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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical...

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

A recursive neural network is a kind of deep neural network created by applying the same set of weights recursively over a structured input, to produce...

A neural Turing machine (NTM) is a recurrent neural network model of a Turing machine. The approach was published by Alex Graves et al. in 2014. NTMs...

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

A residual neural network (also referred to as a residual network or ResNet) is a deep learning architecture in which the layers learn residual functions...

weaknesses of using information from the hidden layers of recurrent neural networks. Recurrent neural networks favor more recent information contained in words...

statistical physics. Rajan continues to apply recurrent neural network modelling to behavioral and neural data. In collaboration with Karl Deisseroth and...

Spiking neural networks (SNNs) are artificial neural networks (ANN) that mimic natural neural networks. These models leverage timing of discrete spikes...

dot-product attention and self-attention mechanism instead of a Recurrent neural network or Long short-term memory (which rely on recurrence instead) allow...

Graph neural networks (GNN) are specialized artificial neural networks that are designed for tasks whose inputs are graphs. One prominent example is molecular...

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

recurrent neural networks, such as Elman networks. The algorithm was independently derived by numerous researchers. The training data for a recurrent...

An echo state network (ESN) is a type of reservoir computer that uses a recurrent neural network with a sparsely connected hidden layer (with typically...

Memory Units: Continuous-Time Representation in Recurrent Neural Networks (PDF). Advances in Neural Information Processing Systems. Arfken & Weber 2005...

A convolutional neural network (CNN) is a type of feedforward neural network that learns features via filter (or kernel) optimization. This type of deep...

paper On the difficulty of training Recurrent Neural Networks by Pascanu, Mikolov, and Bengio. A generic recurrent network has hidden states h 1 , h 2 , …...

biological relationship between neural firing rates and input current, in addition to enabling recurrent neural network dynamics to stabilise under weaker...

undergoing fact rather than a history of facts. Recurrent skipping networks (RSN) uses a recurrent neural network to learn relational path using a random walk...

Reservoir computing is a framework for computation derived from recurrent neural network theory that maps input signals into higher dimensional computational...

differentiable neural computer (DNC) is a memory augmented neural network architecture (MANN), which is typically (but not by definition) recurrent in its implementation...

Traditional deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), excel in processing data on regular grids...

activation and gradient signals. They are most prominently used in recurrent neural networks (RNNs), but have also found applications in other architectures...