Recurrent Neural Networks
Found 4 free book(s)
On the difficulty of training Recurrent Neural Networks
arxiv.orgFigure 1. Schematic of a recurrent neural network. The recurrent connections in the hidden layer allow information to persist from one input to another. and exploding gradient problems described in Bengio et al. (1994). 1.1. Training recurrent networks A generic recurrent neural network, with input utand state xt for time step t, is given by ...
On Neural Di erential Equations
arxiv.orgdemonstrate that neural networks and di erential equation are two sides of the same coin. Traditional parameterised di erential equations are a special case. Many popular neural network architectures, such as residual networks and recurrent networks, are discretisations. NDEs are suitable for tackling generative problems, dynamical systems,
Non-Local Neural Networks - CVF Open Access
openaccess.thecvf.commore abstract model called graph neural networks [41]. Feedforward modeling for sequences. Recently there emerged a trend of using feedforward (i.e., non-recurrent) networks for modeling sequences in speech and language [36, 54, 15]. In these methods, long-term dependencies are captured by the large receptive fields contributed by
Spatio-Temporal Graph Convolutional Networks: A Deep …
www.ijcai.orgthese networks would be hindered seriously. To take full advantage of spatial features, some researchers use convolutional neural network (CNN) to capture adjacent relations among the trafÞc network, along with employing recurrent neural network (RNN) on time axis. By combin-ing long short-term memory (LSTM) network[Hochreiter