###### RNNs

- Typically, these reviews consider RNNs that are artificial neural networks (aRNN) useful in technological applications. (scholarpedia.org)
- You will: - Understand how to build and train Recurrent Neural Networks (RNNs), and commonly-used variants such as GRUs and LSTMs. (coursera.org)
- Recurrent neural networks (RNNs) have shown promising performance for language modeling. (arxiv.org)
- Hardware accelerator templates and design frameworks for implementing recurrent neural networks (RNNs) and variants thereof are described. (patents.com)
- Recurrent Neural Networks (RNNs) model dynamic processing in the brain. (mpg.de)

###### processed by a recurrent neural

- Directions that a sequence of inputs can be processed by a recurrent neural network layer. (apple.com)

###### convolutional neural networks

- Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. (springer.com)
- The connection with these and convolutional neural networks is suggestive for the same reason. (danmackinlay.name)

###### mapping a neural network

- One embodiment of the invention provides a system for mapping a neural network onto a neurosynaptic substrate. (patents.com)

###### Fuzzy Neural Networks

- Lee, C.-H., Teng, C.-C.: Identification and Control of Dynamic Systems Using Recurrent Fuzzy Neural Networks. (springer.com)
- The complete back propagation (BP) algorithm tuning equations used to tune the antecedent and consequent parameters for the interval type-2 fuzzy neural networks (IT2FNNs) are developed to handle the training data corrupted by noise or rule uncertainties for nonlinear system identification involving external disturbances. (igi-global.com)
- Simulation results are obtained for the identification of nonlinear system, which yield more improved performance than those using recurrent type-1 fuzzy neural networks (RT1FNNs). (igi-global.com)

###### Prediction

- We propose a detection methodology by adapting a three-layered, deep learning architecture of (1) recurrent neural network [bi-directional long short-term memory (Bi-LSTM)] for character-level word representation to encode the morphological features of the medical terminology, (2) Bi-LSTM for capturing the contextual information of each word within a sentence, and (3) conditional random fields for the final label prediction by also considering the surrounding words. (springer.com)
- Smith, C , Doherty, J and Jin, Y (2014) Multi-Objective Evolutionary Recurrent Neural Network Ensemble for Prediction of Computational Fluid Dynamic Simulations In: IEEE Congress on Evolutionary Computation (CEC), 2014-07-06 - 2014-07-11, Beijing, PEOPLES R CHINA. (surrey.ac.uk)
- This is a simple framework for time-series prediction of the time to the next event applicable when we have any or all of the problems of continuous or discrete time, right censoring, recurrent events, temporal patterns, time varying covariates or time series of varying lengths. (chalmers.se)
- Martinsson2017, author={Martinsson, Egil}, title={WTTE-RNN : Weibull Time To Event Recurrent Neural Network A model for sequential prediction of time-to-event in the case of discrete or continuous censored data, recurrent events or time-varying covariates}, abstract={In this thesis we propose a new model for predicting time to events: the Weibull Time To Event RNN. (chalmers.se)

###### Exponential Dissipativity

- OPUS at UTS: Global exponential dissipativity and stabilization of memristor-based recurrent neural networks with time-varying delays. (edu.au)
- This paper addresses the global exponential dissipativity of memristor-based recurrent neural networks with time-varying delays. (edu.au)

###### based on neural networks

- Mikolov, T.: Statistical language models based on neural networks. (springerprofessional.de)
- We have developed a new method for identification of signal peptides and their cleavage sites based on neural networks trained on separate sets of prokaryotic and eukaryotic sequences. (psu.edu)

###### artificial neural

- Recurrent quantum neural network (RQNN) is an artificial neural network model which can perform stochastic filtering without any prior knowledge of the signal and noise. (springer.com)
- Recurrent nets are a type of artificial neural network designed to recognize patterns in sequences of data, such as text, genomes, handwriting, the spoken word, or numerical times series data emanating from sensors, stock markets and government agencies. (skymind.ai)
- This algorithm utilizes a type of artificial neural network known as an Echo State Network (ESN). (colostate.edu)

###### networks with time-varyin

- Therefore, increasing attention has been paid to the problem of stability analysis of neural networks with time-varying delays, and recently a lot of research works have been reported for delayed neural networks and system (see [ 1 - 17 ] and references therein). (hindawi.com)
- It is also shown that memristor-based recurrent neural networks with time-varying delays are stabilizable at the origin of the state space by using a linear state feedback control law with appropriate gains. (edu.au)

###### advances in neur

- Advances in Neural Information Processing Systems 25, pp. 1097-1105. (springer.com)

###### electroencephalography

- Major, T.C., Conrad, J.M.: The effects of pre-filtering and individualizing components for electroencephalography neural network classification. (springer.com)
- In this paper, the new model is proposed to automatically detect and predict absence seizure using hybrid deep learning algorithm [Convolutional Recurrent Neural Network (CRNN)] along with the Discrete Wavelet Transform (DWT) with Electroencephalography (EEG) as input. (springer.com)

###### sequential

- See Sequential Neural Models with Stochastic Layers . (danmackinlay.name)
- That sequential information is preserved in the recurrent network's hidden state, which manages to span many time steps as it cascades forward to affect the processing of each new example. (skymind.ai)

###### neurons

- A recurrent neural network (RNN) is any network whose neurons send feedback signals to each other. (scholarpedia.org)
- The Hopfield recurrent neural network is a classical auto-associative model of memory, in which collections of symmetrically coupled McCulloch-Pitts binary neurons interact to perform emergent computation. (readbyqxmd.com)
- In a traditional recurrent neural network, during the gradient back-propagation phase, the gradient signal can end up being multiplied a large number of times (as many as the number of timesteps) by the weight matrix associated with the connections between the neurons of the recurrent hidden layer. (danmackinlay.name)
- A neural network model is presented which extends Hopfield's model by adding hidden neurons. (ubc.ca)
- The resulting model remains fully recurrent, and still learns by prescriptive Hebbian learning, but the hidden neurons give it power and flexibility which were not available in Hopfield's original network. (ubc.ca)

###### LSTM

- This model enhances the feature extraction and also the overall performance by feeding the segmented data into Long Short Tern Memory (LSTM) which is one of the Recurrent Neural Network (RNN). (springer.com)
- This paper presents a model based on Recurrent Neural Network architecture, in particular LSTM, for modeling the behavior of Large Hadron Collider superconducting magnets. (springer.com)

###### weights

- An important question in neuroevolution is how to gain an advantage from evolving neural network topologies along with weights. (psu.edu)
- be the input gate weights for input, recurrent input, and memory cell (peephole) data, respectively. (apple.com)
- A fuzzy model, recurrent interval type-2 fuzzy neural network (RIT2FNN), is constructed by using a recurrent neural network which recurrent weights, mean and standard deviation of the membership functions are updated. (igi-global.com)

###### nets

- To understand recurrent nets, first you have to understand the basics of feedforward nets . (skymind.ai)
- 2 Adding memory to neural networks has a purpose: There is information in the sequence itself, and recurrent nets use it to perform tasks that feedforward networks can't. (skymind.ai)
- Just as human memory circulates invisibly within a body, affecting our behavior without revealing its full shape, information circulates in the hidden states of recurrent nets. (skymind.ai)

###### back propagation

- Ping, F.F., Fang, X.F.: Multivariant forecasting mode of Guangdong province port throughput with genetic algorithms and back propagation neural network. (springer.com)

###### algorithm

- In this paper we propose a fuzzy recurrent neural network (FRNN) based fuzzy time series forecasting method using genetic algorithm. (springer.com)
- The proposed controller is based on the Gen-eralized predictive control (GPC) algorithm, and a recur-rent fuzzy neural network (RFNN) is used to approximate the unknown nonlinear plant. (psu.edu)

###### sequences

- Alex Graves Generating Sequences With Recurrent Neural Networks , generates handwriting. (danmackinlay.name)

###### Bengio

- Vinyals O, Toshev A, Bengio S, Erhan D (2015) Show and tell: a neural image caption generator. (springer.com)

###### 1993

- Indeed, here is a paper from 1993 which attempts to apply neural networks to this problem. (xed.ch)

###### feedforward

- Recurrent networks are distinguished from feedforward networks by that feedback loop connected to their past decisions, ingesting their own outputs moment after moment as input. (skymind.ai)

###### biological

- To complement these contributions, the present summary focuses on biological recurrent neural networks (bRNN) that are found in the brain . (scholarpedia.org)
- Conversely, biological neural networks exhibit high variability of structural as well as activity parameters. (springeropen.com)
- sequence memory networks, to include variable sparseness and thereby add one aspect of variability that is to be expected in biological neural networks. (springeropen.com)

###### parameters

- The proposed model estimates the distribution of time to the next event as having a discrete or continuous Weibull distribution with parameters being the output of a recurrent neural network. (chalmers.se)

###### neuron

- A memory cell is composed of four main elements: an input gate, a neuron with a self-recurrent connection (a connection to itself), a forget gate and an output gate. (danmackinlay.name)
- It is proven herein that there are 2(2n(2)-n) equilibria for an n-neuron memristor-based neural network and they are located in the derived globally attractive sets. (edu.au)
- Our group develops (i) computational models for cognitive processes like decision making, and recognition and learning of complex spatiotemporal patterns, and (ii) neuronal models at several spatiotemporal scales, e.g. neural mass or single neuron models. (mpg.de)

###### computation

- Neural networks have been a subject of intense research activities over the past few decades due to their wide applications in many areas such as signal processing, pattern recognition, associative memories, parallel computation, and optimization solution. (hindawi.com)

###### model

- 0\). The McCulloch-Pitts model had an influence far beyond the field of neural networks through its influence on von Neumann's development of the digital computer. (scholarpedia.org)
- Finally we train the Ranking SVM model and show that combination of recurrent neural networks and morphological information gives better results than 5-gram model with Knesser-Ney discounting. (springerprofessional.de)

###### globally attractive sets

- Generally speaking, the goal of study on globally dissipative for neural networks is to determine globally attractive sets. (hindawi.com)

###### 2018

- Arana-Daniel N., Valdés-López J., Alanís A.Y., López-Franco C. (2018) Traversability Cost Identification of Dynamic Environments Using Recurrent High Order Neural Networks for Robot Navigation. (springer.com)

###### classification

- Ciresan D, Meier U, Schmidhuber J (2012) Multi-column deep neural networks for image classification. (springer.com)

###### interval

- This paper is concerned with the robust dissipativity problem for interval recurrent neural networks (IRNNs) with general activation functions, and continuous time-varying delay, and infinity distributed time delay. (hindawi.com)
- It is also possible that there is no equilibrium point in some situations, especially for interval recurrent neural networks with infinity distributed delays. (hindawi.com)

###### predict

- And look at how the neural network maybe try to predict the output. (coursera.org)
- This paper investigates the profitability of a trading strategy, based on recurrent neural networks, that attempts to predict the direction-of-change of the market in the case of the NASDAQ composite index. (repec.org)

###### Identification

- Recurrent High Order Neural Networks (RHONN) trained with Extended Kalman Filter (EKF) are used to identify rough terrain traversability costs, and besides the good results in the identification tasks, we get the advantages of using a robust machine learning method such as RHONNs. (springer.com)

###### spatial

- Anderson (1968) initially described his intuitions about neural pattern recognition using a spatial cross-correlation function. (scholarpedia.org)
- However, neural networks usually have a spatial extent due to the presence of a multitude of parallel pathways with a variety of axon sizes and lengths. (hindawi.com)

###### patterns

- Although previous researchers have explored the potential of this network to solve combinatorial optimization problems or store reoccurring activity patterns as attractors of its deterministic dynamics, a basic open problem is to design a family of Hopfield networks with a number of noise-tolerant memories that grows exponentially with neural population size. (readbyqxmd.com)

###### sequence

- Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. (springer.com)

###### theory

- Concepts from linear system theory were adapted to represent some aspects of neural dynamics, including solutions of simultaneous linear equations \(Y = AX\) using matrix theory, and concepts about cross-correlation. (scholarpedia.org)
- Rovithakis, G.A., Christodoulou, M.A.: Adaptive Control with Recurrent High-Order Neural Networks: Theory and Industrial Applications. (springer.com)

###### continuous

- Continuous-nonlinear network laws typically arose from an analysis of behavioral or neural data. (scholarpedia.org)
- Decoding continuous hind limb joint angles from sensory recordings of neural system provides a feedback for closed-loop control of hind limb movement using functional electrical stimulation. (readbyqxmd.com)

###### stability

- Therefore, many initial findings on the global dissipativity [ 18 , 22 - 30 ] or Lagrange stability [ 31 - 36 ] analysis of neural networks have been reported. (hindawi.com)

###### decades

- Over decades such neuronal feedback attracted a huge amount of theoretical modeling [ 1 - 3 ] and one of the most prominent functions that is proposed for the recurrent synaptic connections is that of associative memory. (springeropen.com)

###### prominent

- The purpose of this post is to give students of neural networks an intuition about the functioning of recurrent neural networks and purpose and structure of a prominent RNN variation, LSTMs. (skymind.ai)

###### deep

- Graves A, Mohamed A, Hinton GE (2013) Speech recognition with deep recurrent neural networks. (springer.com)
- A knowledge graph is traversed by receiving a knowledge graph at a deep neural network, the knowledge graph including a plurality of nodes connected by a. (patents.com)
- Using deep neural networks to make sense of unstructured text. (oreilly.com)

###### paper

- In this paper, it is presented a neural network methodology for learning traversability cost maps to aid autonomous robotic navigation. (springer.com)

###### output

- Here's a diagram of an early, simple recurrent net proposed by Elman , where the BTSXPE at the bottom of the drawing represents the input example in the current moment, and CONTEXT UNIT represents the output of the previous moment. (skymind.ai)

###### hidden

- So that's a hidden layer of the first neural network. (coursera.org)

###### approach

- However, from a practical point of view, it is not always the case that the neural network trajectories will approach a single equilibrium point that is the equilibrium point will be unstable. (hindawi.com)

###### data

- We train several recurrent neural networks on a lemmatized news corpus to mitigate the problem of data sparseness. (springerprofessional.de)
- So recurrent networks have two sources of input, the present and the recent past, which combine to determine how they respond to new data, much as we do in life. (skymind.ai)

###### time

- Recurrent networks, on the other hand, take as their input not just the current input example they see, but also what they have perceived previously in time. (skymind.ai)

###### applications

- We present our first results in applications of recurrent neural networks to Russian. (springerprofessional.de)

###### recognition

- Learn about artificial neural networks and how they're being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc. (coursera.org)

###### memory cell

- the rest of the recurrent neural network gets written into the memory cell. (coursera.org)

###### network layer

- What we're going to do is take the first word and feed it into a neural network layer. (coursera.org)
- A recurrent neural network layer for inference on Metal Performance Shaders images. (apple.com)

###### simple

- A description of a simple recurrent block or layer. (apple.com)