###### 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)

###### 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)

###### 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)
- This algorithm utilizes a type of artificial neural network known as an Echo State Network (ESN). (colostate.edu)
- 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)

###### 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)
- In this paper, we extend the classical clipped learning rule by Willshaw to networks with inhomogeneous sparseness, i.e., the number of active neurons may vary across memory items. (springeropen.com)

###### 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)

###### simple recurrent

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

###### advances in neur

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

###### 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)

###### 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)

###### 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)

###### 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)

###### parameters

- Theoretical models of associative memory generally assume most of their parameters to be homogeneous across the network. (springeropen.com)
- Conversely, biological neural networks exhibit high variability of structural as well as activity parameters. (springeropen.com)

###### auto-associative

- We use the standard formalism of auto-associative networks: a discrete-time dynamical system. (springeropen.com)

###### sequences

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

###### feedforward

- A feedforward network is trained on labeled images until it minimizes the error it makes when guessing their categories. (skymind.ai)
- A trained feedforward network can be exposed to any random collection of photographs, and the first photograph it is exposed to will not necessarily alter how it classifies the second. (skymind.ai)
- That is, a feedforward network has no notion of order in time, and the only input it considers is the current example it has been exposed to. (skymind.ai)
- 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)

###### 1993

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

###### Bengio

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

###### globally attractive sets

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

###### Comput

- Neural Comput. (springer.com)

###### 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)
- 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)

###### 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)

###### 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)

###### dynamics

- Direction-of-Change Forecasting using a Volatility- Based Recurrent Neural Network ," CeNDEF Working Papers 06-16, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance. (repec.org)
- 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)

###### patterns

- determines which patterns can be recalled, or are stored in the network. (springeropen.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)

###### data

- Continuous-nonlinear network laws typically arose from an analysis of behavioral or neural data. (scholarpedia.org)
- 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)
- We motivate why recurrent neural networks are important for dealing with sequence data and review LSTMs and GRU architectures. (intel.com)

###### delays

- 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)

###### 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)

###### Hopfield

- Robust Exponential Memory in Hopfield Networks. (readbyqxmd.com)
- It is also shown that the memory recovery process developed for the new network can be used to greatly expand the radius of attraction of standard Hopfield networks for "incomplete" (as opposed to "noisy") prompts. (ubc.ca)
- The mathematical analysis begins by deriving an expression for the free energy of a Hopfield network when a near-orthogonal memory set has been stored. (ubc.ca)

###### 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)

###### language models

- Mikolov, T.: Statistical language models based on neural networks. (springerprofessional.de)

###### hidden

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

###### architecture

- It is the most basic neural net architecture right out of an introductory textbook. (xed.ch)

###### time

- Feedback networks structured to have memory and a notion of "current" and "past" states, which can encode time (or whatever). (danmackinlay.name)
- 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)

###### memories

- As a result, the network actually learns a memory set which is "near-orthogonal", even though the visible components of the memories are randomly selected. (ubc.ca)
- Finally, the theoretical maximum information content of sets of near-orthogonal memories is calculated as a function of the level of orthogonality, and is compared to the amount of information that can be stored in the new network. (ubc.ca)

###### Long

- networks that's called Long Short Term Memory. (coursera.org)

###### paper

- In this paper, it is presented a neural network methodology for learning traversability cost maps to aid autonomous robotic navigation. (springer.com)
- In this paper, we extend a particular class of such auto-association networks, viz. (springeropen.com)

###### system

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

###### nodes

- Both of these networks are named after the way they channel information through a series of mathematical operations performed at the nodes of the network. (skymind.ai)

###### memory cell

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