###### RNNs

- You will: - Understand how to build and train Recurrent Neural Networks (RNNs), and commonly-used variants such as GRUs and LSTMs. (coursera.org)
- Typically, these reviews consider RNNs that are artificial neural networks (aRNN) useful in technological applications. (scholarpedia.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)

###### convolutional neural

- 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)
- CS231n: Convolutional Neural Networks for Visual Recognition at Stanford ( archived 2015 version ) is an amazing advanced course, taught by Fei-Fei Li and Andrej Karpathy (a UofT alum). (toronto.edu)

###### 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)

###### feedforward networks

- 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)
- 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)

###### 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)

###### 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)

###### 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)

###### 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)

###### Bengio

- Vinyals O, Toshev A, Bengio S, Erhan D (2015) Show and tell: a neural image caption generator. (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)
- Development of RLS algorithm for localization in wireless sensor networks. (springer.com)

###### 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)

###### 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)
- 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)
- 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)

###### auto-associative

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

###### 1993

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

###### 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)

###### 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)

###### biological

- To complement these contributions, the present summary focuses on biological recurrent neural networks (bRNN) that are found in the brain . (scholarpedia.org)
- 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)

###### 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)

###### sequence

- Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. (springer.com)
- We evaluate this learning rule for sequence memory networks with instantaneous feedback inhibition and show that little surprisingly, memory capacity degrades with increased variability in sparseness. (springeropen.com)
- Directions that a sequence of inputs can be processed by a recurrent neural network layer. (apple.com)

###### 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)

###### Memory

- Robust Exponential Memory in Hopfield Networks. (readbyqxmd.com)
- Previous studies have shown that there are many common structures between the neural network of pain and memory, and the main structure in the pain network is also part of the memory network. (readbyqxmd.com)
- networks that's called Long Short Term Memory. (coursera.org)
- You can consider the dynamic state of a neural network to be a short term memory. (coursera.org)
- the rest of the recurrent neural network gets written into the memory cell. (coursera.org)
- Feedback networks structured to have memory and a notion of "current" and "past" states, which can encode time (or whatever). (danmackinlay.name)
- 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)
- Research shows them to be one of the most powerful and useful type of neural network, alongside the attention mechanism and memory networks . (skymind.ai)
- Since recurrent networks possess a certain type of memory, and memory is also part of the human condition, we'll make repeated analogies to memory in the brain. (skymind.ai)
- It is often said that recurrent networks have memory. (skymind.ai)
- 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)
- 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)
- 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)

###### recognition

- Graves A, Mohamed A, Hinton GE (2013) Speech recognition with deep recurrent neural networks. (springer.com)
- 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)

###### connectivity

- The aim of the present analyses was to investigate whether emotionally challenged remitted depressed participants show higher respiration pattern variability (RPV) and whether this is related to mood, clinical outcome and increased default mode network connectivity. (readbyqxmd.com)
- Many brain areas exhibit extensive recurrent connectivity. (springeropen.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)

###### 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)

###### hidden

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

###### 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)

###### architecture

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

###### Learning

- LeCun, Y.: Deep learning of convolutional networks. (springer.com)
- In this paper, it is presented a neural network methodology for learning traversability cost maps to aid autonomous robotic navigation. (springer.com)
- This course serves as an introduction to machine learning, with an emphasis on neural networks. (toronto.edu)

###### time

- 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, 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)

###### memories

- 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)

###### 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)

###### deep

- 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)

###### 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)

###### applications

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