The first problem is how to include dynamics in a neural net . We have considered recurrent neural networks and dynamical neurons . We have implemented an extended backpropagation to take into account the dynamics in the training phase . Methods Sampling from 143,003 unique vaccine-related Web pages shared on Twitter between January 2017 and March 2018 , we used a 7-point checklist adapted from validated tools and guidelines to manually appraise the credibility of 474 Web pages . These were used to train several classifiers ( random forests , support vector machines , and recurrent neural networks ) using the text from a Web page to predict whether the information satisfies each of the 7 criteria . Estimating the credibility of all other Web pages , we used the follower network to estimate potential exposures relative to a credibility score defined by the 7-point checklist . We show that the long-term dependencies problem is lessened for a class of architectures called nonlinear autoregressive models with exogenous ( NARX ) recurrent neural networks , which have powerful representational capabilities . We have previously reported that gradient descent learning can be more effective in NARX networks than in recurrent neural network architectures that have " hidden states " on problems including grammatical inference and nonlinear system identification . Typically , the network converges much faster and generalizes better than other networks . We discuss the tracking problem in the presence of unmodelled dynamics , for affine in the control nonlinear dynamical systems , whose nonlinearities are assumed unknown , using recurrent neural network structures . Based upon their proven approximation capabilities , Lyapunov stability theory is employed to develop smooth , partial state control and update laws , to guarantee the uniform ultimate boundedness of the tracking error , as well as uniform boundedness of all other signals in the closed loop . In both cases , the neural network structure is trained with an extended Kalman filter algorithm . Simulation results illustrate the applicability of the recurrent neural network structures to estimate complex states of the biological processes in the presence of disturbances . Since the behaviour of electric load time series is very much non-linear and seasonal , Neural Networks are best suited model for learning the Non-Linear behaviour within the data and for forecasting purpose . This paper deals with the Recurrent Neural Networks based Models : Long-Short-Term-Memory ( LSTM ) and Gated-Recurrent-Unit ( GRU ) to deal with this challenge . Observations have been made based on the distributed implementation of various configurations of LSTM-RNN & GRU-RNN on spark clusters for hyper parameter tuning purpose and deploying best suited configuration with least RMSE value using apache memos resource management . Regular Papers.- CT and PET Registration Using Deformations Incorporating Tumor-Based Constraints.- Surface Grading Using Soft Colour-Texture Descriptors.- Support Vector Machines with Huffman Tree Architecture for Multiclass Classification.- Automatic Removal of Impulse Noise from Highly Corrupted Images.- Smoothing of Polygonal Chains for 2D Shape Representation Using a G 2-Continuous Cubic A-Spline.- A Robust Statistical Method for Brain Magnetic Resonance Image Segmentation.- Inference Improvement by Enlarging the Training Set While Learning DFAs.- A Computational Approach to Illusory Contour Perception Based on the Tensor Voting Technique.- A Novel Clustering Technique Based on Improved Noising Method.- Object Recognition in Indoor Video Sequences by Classifying Image Segmentation Regions Using Neural Networks.- Analysis of Directional Reflectance and Surface Orientation Using Fresnel Theory.- Lacunarity as a Texture Measure for Address Block Segmentation.- Measuring the Quality Evaluation for Image Segmentation.- Frame Deformation Energy Matching of On-Line Handwritten Characters.- Nonlinear Civil Structures Identification Using a Polynomial Artificial Neural Network.- A Method of Automatic Speaker Recognition Using Cepstral Features and Vectorial Quantization.- Classification of Boar Spermatozoid Head Images Using a Model Intracellular Density Distribution.- Speech Recognition Using Energy Parameters to Classify Syllables in the Spanish Language.- A Strategy for Atherosclerotic Lesions Segmentation.- Image Scale-Space from the Heat Kernel.- A Naive Solution to the One-Class Problem and Its Extension to Kernel Methods.- Nonlinear Modeling of Dynamic Cerebral Autoregulation Using Recurrent Neural Networks.- Neural Network Approach to Locate Motifs in Biosequences.- Free-Shaped Object Recognition Method from Partial Views Using Weighted Cone Curvatures.- Automatic Braille Code Translation System.- Automatic Extraction of DNA Profiles in Polyacrilamide Gel Electrophoresis Images.- The Use of Bayesian Framework for Kernel Selection in Vector Machines Classifiers.- Genetic Multivariate Polynomials : An Alternative Tool to Neural Networks.- Non-supervised Classification of 2D Color Images Using Kohonen Networks and a Novel Metric.- Data Dependent Wavelet Filtering for Lossless Image Compression.- A Furthermore , they assumed a maximum of 9 cars per image . Approaches using recurrent neural networks [ reference ][ reference ] can handle a variable number of instances per image by segmenting an instance per time - step , but are currently restricted to only one object category . Our method , on the other hand , is able to handle both an arbitrary number of objects , and multiple object categories in the image with a single forward - pass through the network . However , because of their auto - regressive property of requiring previous hidden states to be computed before the current time step , they can not benefit from parallelization . Variants of recurrent networks that use strided convolutions eschew the traditional time - step based computation kaiser2016can , lei2017training , bradbury2016quasi , gehring2016convenc , gehring2017convs2s , kalchbrenner2016neural . However , in these models , the operations needed to learn dependencies between distant positions can be difficult to learn hochreiter2001gradient , hochreiter1998vanishing . The convolutional networks use window size on either side of the target token and produce a 300 - dimensional context - dependent vector . The recurrent networks use hidden layers of size 200 in either direction . We also added an extra hidden layer of size between each of the composition functions and the output layer – In addition , we devise a metric called Relative Effective Context Length ( RECL ) that aims to perform a fair comparison of the gains brought by increasing the context lengths for different models . In this setting , Transformer - XL learns a RECL of 900 words on WikiText - 103 , while the numbers for recurrent networks and Transformer are only 500 and 128 . section : Related Work Moreover , while all graph kernels have a training complexity at least quadratic in the number of graphs , which is prohibitive for large - scale problems , Patchy - san scales linearly with the number of graphs . Graph neural networks ( GNNs ) are a recurrent neural network architecture defined on graphs . GNNs apply recurrent neural networks for walks on the graph structure , propagating node representations until a fixed point is reached . document : Multiplicative LSTM for sequence modelling We introduce multiplicative LSTM ( mLSTM ) , a recurrent neural network architecture for sequence modelling that combines the long short - term memory ( LSTM ) and multiplicative recurrent neural network architectures . mLSTM is characterised by its ability to have different recurrent transition functions for each possible input , which we argue makes it more expressive for autoregressive density estimation . ’s advantages , we introduce a memory capacity measure , the mean recurrent length , which is more suitable for RNNs with long skip connections than existing measures . We rigorously prove the advantages of the DilatedRNN over other recurrent neural architectures . The code for our method is publicly available . Moreover , the network is end - to - end trainable . Our method is similar in spirit to a recent work , which detects text lines by finding and grouping a sequence of fine - scale text proposals through a CNN coupled with recurrent neural layers . In contrast , we detect oriented segments only using convolutional layers , yielding better flexibility and faster speed . section : Concluding Remarks For visual question answering on COCO dataset , our implementation of a simple baseline achieves comparable performance to several recently proposed recurrent neural network - based approaches . To reach the correct prediction , the baseline captures the correlation between the informative words in the question and the answer , and that between image contents and the answer . [ reference ] . We investigate an alternative recurrent neural network structure for addressing these issues . As shown in Figure 1 , the main idea is to model the hidden states of all words simultaneously at each recurrent step , rather than one word at a time . The |V | dimensional y t models the discrete probability distribution for the next word . Note that the above formulation does not make any assumptions about the specifics of the recurrent neural units , and f could be replaced with a standard recurrent unit , a gated recurrent unit ( GRU ) [ reference ] , a long - short term memory ( LSTM ) unit [ reference ] , etc . For our experiments , we use LSTM units with two layers . HLN is a combination of Lagrangian function and continuous Hopfield neural network where the Lagrangian function is directly used as the energy function for the continuous Hopfield neural network . In the HLN method , its energy function augmented by Hopfield terms from the continuous Hopfield network could damp out oscillation of the conventional Hopfield network during the convergence process . Consequently , the proposed HLN can overcome the disadvantages of the conventional Hopfield network in solving optimization problems for its simpler implementation , better global solution , faster convergence time , and larger scale applications . Abstract Two major “ categories ” of unsupervised learning rules are used in artificial neural networks : ( i ) competitive learning , which is used in the adaptive resonance theory ( ART ) , the self-organizing map , and the neocognitron ; and ( ii ) Hebbian learning without lateral inhibition , which is used in the Hopfield network . Since the competitive learning is essentially Hebbian learning in the presence of lateral inhibition , the author attempts here to discuss general properties of these unsupervised learning rules in a unified paradigm . The solution with the Hopfield algorithm has the complexity of the 4n + 16n algorithm , while the complexity of the Simulated Annealing algorithm is 10n2 . Judging from the use of memory in the implementation , the application of annealing algorithm is better than the Hopfield algorithm . but both are algorithms that " no works in place " . In this approach , each operation is represented by a group of neurons , equal in number to the number of jobs in the problem . In contrast to a similar approach , developed by Foo and Takefuji , for a six jobs and six machines problem , our Hopfield-type model uses 36 neurons and our Boltzmann machine model needs 216 neurons , while their model requires 1332 neurons . Larger number of neurons require longer computer execution times for convergence . We proposed and realized one new type models of neural networks , which takes into account property of anticipation . As the base model , the Hopfield type models with anticipation have been explored . The basic new qualities , discovered at research there is that possible multi-valued solutions of given neural networks . Artificial neural networks are dynamic systems consisting of highly interconnected and parallel nonlinear processing elements that are shown to be extremely effective in computation . This paper presents a modified Hopfield architecture , which has equilibrium points representing the solution of the problems considered , i.e , dynamic programming problems and bipartite graph optimization . The internal parameters of the network have been computed using the valid-subspace technique . In present paper , we describe completely innovation architecture of artificial neural nets based on Hopfield structure for solving of stereo matching problem . Hybrid neural network consists of classical analogue Hopfield neural network and maximal neural network . To effectively decom- pose such mixtures , a fully constrained linear unmixing algorithm based on a multichannel Hopfield neural network ( MHNN ) is proposed in this letter . The proposed MHNN algorithm is actu- ally a Hopfield-based architecture which handles all the pixels in an image synchronously , instead of considering a per-pixel procedure . Due to the synchronous unmixing property of MHNN , a noise energy percentage ( NEP ) stopping criterion which uti- lizes the signal-to-noise ratio is proposed to obtain optimal re- sults for different applications automatically . State-of-the-art hand gesture recognition methods have investigated the spatiotemporal features based on 3D convolutional neural networks ( 3DCNNs ) or convolutional long short-term memory ( ConvLSTM ) . However , they often suffer from the inefficiency due to the high computational complexity of their network structures . The key to our design is to learn high-level gesture representations by taking advantages from Convolutional Residual Networks ( ResNets ) for training extremely deep models and Convolutional Long Short-Term Memory Networks ( ConvLSTM ) for dealing with time-series connections . More specifically , we first construct an architecture to simultaneously learn the spatiotemporal features from RGB and depth sequences through 3D ResNets which are then linked to a ConvLSTM to capture the temporal dependencies between them , and we show that they better combine appearance and motion information effectively . Second , to alleviate distractions from background and other variations , we propose a method that encodes the temporal information into a motion representation , while a two-stream architecture based on 2D-ResNets is then employed to extract deep features from this representation . In particular , our ATFM is composed of two progressive Convolutional Long Short-Term Memory ( ConvLSTM \cite{xingjian2015convolutional } ) units connected with a convolutional layer . Specifically , the first ConvLSTM unit takes normal traffic flow features as input and generates a hidden state at each time-step , which is further fed into the connected convolutional layer for spatial attention map inference . The second ConvLSTM unit aims at learning the dynamic spatial-temporal representations from the attentionally weighted traffic flow features . Convolutional long short-term memory ( ConvLSTM ) networks have been widely used for action/gesture recognition , and different attention mechanisms have also been embedded into ConvLSTM networks . This paper explores the redundancy of spatial convolutions and the effects of the attention mechanism in ConvLSTM , based on our previous gesture recognition architectures that combine the 3-D convolutional neural network ( CNN ) and ConvLSTM . Owing to the limitation of FC-LSTM , existing methods have to convert 2-D feature maps into 1-D sequential feature vectors , resulting in severe damages of the valuable spatial and structural information of text images . In this paper , we argue that scene text recognition is essentially a spatiotemporal prediction problem for its 2-D image inputs , and propose a convolution LSTM (ConvLSTM)-based scene text recognizer , namely , FACLSTM , i.e. , focused attention ConvLSTM , where the spatial correlation of pixels is fully leveraged when performing sequential prediction with LSTM . Particularly , the attention mechanism is properly incorporated into an efficient ConvLSTM structure via the convolutional operations and additional character center masks are generated to help focus attention on right feature areas . Through this way , we leverage the attention consistency across different layers to improve the robustness of the network . On the dynamic aspect , we propose a correlationbased ConvLSTM to appropriately balance the influence of the current and preceding frames to the prediction . Experimental results on the DHF1 K , Hollywood2 and UCF-sports datasets show that SalSAC outperforms many existing stateof-the-art methods . First , the video sequences are input into a deep convolutional encoder – decoder network for extracting pixel-wise semantic features . Then , to exploit the temporal context , we propose a novel attention long short-term memory ( Attention ConvLSTM ) to model pixelwise changes over time . A spatial transformer network and a conditional random field layer are finally appended to reduce the sensitivity to camera motion and smooth the foreground boundaries . Thirty-six sets of patient data were used in the study . Experimental results showed that our proposed ConvLSTM neural network produced a mean TRE of 2.85±1.72 mm and a mean Dice of 0.89 . Nevertheless , they mostly focused on predicting air quality in discrete locations or used hand-crafted spatial and temporal features . In this paper , we propose the usage of Convolutional Long Short-Term Memory ( ConvLSTM ) model [ 16 ] , a combination of Convolutional Neural Networks and Long Short-Term Memory , which automatically manipulates both the spatial and temporal features of the data . Specially , we introduce how to transform the air pollution data into sequences of images which leverages the using of ConvLSTM model to interpolate and predict air quality for the entire city at the same time . Aiming at the above phenomena and problems , this paper proposes an improved CLDNN structure . It combines the residual network and ConvLSTM to establish the residual ConvLSTM model , and replaces the fully connected LSTM model in the traditional CLDNN structure . The model structure solves the problems of the traditional CLDNN model , and can increase the model depth by stacking residual ConvLSTM blocks without gradient disappearance , gradient explosion and " degeneration " problems , which makes the speech recognition system perform better . Distinct from previous methods , we aim to temporally integrate pyramidal feature hierarchy using ConvLSTM , and design a novel structure including a high-level ConvLSTM unit as well as a low-level one (HL-LSTM)for multi-scale feature maps . Moreover , we develop a creative temporal analysis unit , namely , ConvLSTM-based attention and attention-based ConvLSTM ( A&CL ) , in which the ConvLSTM-based attention is specially tailored for background suppression and scale suppression while the attention-based ConvLSTM temporally integrates attention-aware features . Finally , our method is evaluated on ImageNet VID dataset . This paper presents one of these challenges and the method to solve it . The method is based on a recurrent artificial neural network that implements the well-known vector control strategy to determine the switching states of the pulse width modulation ( PWM ) based controlled boost converters optimally . Nested in the inner current control loop , the neural network based current controller tries to minimize the circulating current by reducing dq current errors and generates the dq voltage reference to derive PWM converters . Analysis of internal structures of embodied and situated agents may provide insights into the mechanisms underlying adaptive behaviour . This paper is concerned with the evolution and analysis of visually-guided approach behaviour in a simulated robotic agent controlled by a recurrent artificial neural network , whose connection weights have been evolved using evolutionary algorithms . Analysis of the evolved behaviours and their network-internal mechanisms reveals a behavioural structure and organization resembling a Brooksian subsumption architecture . Here , we demonstrate the efficacy of machine learning in predicting chaotic behavior in complex nonlinear mechanical systems . Specifically , we use quasi-recurrent neural networks to predict extremely chaotic time series data obtained from multistable origami systems . Additionally , while machine learning is often viewed as a " black box " , in this study we conduct hidden layer analysis to understand how the neural network can process not only periodic , but also chaotic data in an accurate manner . In the quasi-linear recurrent neural network predictive control , the solution of the control law only need one-step derivation , which can greatly simplify the solution process of control law . At the same time , the quasi-linear recurrent neural network can effectively restrain the over-fitting problem in the identification process . Theoretical analysis and simulations are given to prove the simplicity and effectiveness of the proposed computing method . We explored the performance of our previous single-dataset-trained sentence segmentation architecture in a richer scenario involving three new datasets used to diagnose cognitive impairments , comprising different stories and two types of stimulus presentation for eliciting narratives — visual and oral — via illustrated story-book and sequence of scenes , and by retelling . Also , we proposed and evaluated three modifications to our previous RCNN architecture : ( i ) the inclusion of a Linear Chain CRF ; ( ii ) the inclusion of a self-attention mechanism ; and ( iii ) the replacement of the LSTM recurrent layer by a Quasi-Recurrent Neural Network layer . Our study allowed us to develop two new models for segmenting impaired speech transcriptions , along with an ideal combination of datasets and specific groups of narratives to be used as the training set . document : Quasi - Recurrent Neural Networks Recurrent neural networks are a powerful tool for modeling sequential data , but the dependence of each timestep ’s computation on the previous timestep ’s output limits parallelism and makes RNNs unwieldy for very long sequences . We introduce quasi - recurrent neural networks ( QRNNs ) , an approach to neural sequence modeling that alternates convolutional layers , which apply in parallel across timesteps , and a minimalist recurrent pooling function that applies in parallel across channels . Despite lacking trainable recurrent layers , stacked QRNNs have better predictive accuracy than stacked LSTMs of the same hidden size . While the motivation and constraints described in that work are different , ’s concepts of “ learnware ” and “ firmware ” parallel our discussion of convolution - like and pooling - like subcomponents . As the use of a fully connected layer for recurrent connections violates the constraint of “ strong typing ” , all strongly - typed RNN architectures ( including the T - RNN , T - GRU , and T - LSTM ) are also quasi - recurrent . However , some QRNN models ( including those with attention or skip - connections ) are not “ strongly typed ” . Long short term memory ( LSTM ) [ reference ] , one of the variants of recurrent neural network ( RNN ) , is inept at remembering past stories because of their small internal memory [ reference ] . To cope with this problem , J. has proposed a new class of memory - augmented model called Memory Network ( MemNN ) . section : The model The Recurrent Neural Network ( RNN ) is a natural generalization of feedforward neural networks to sequences . Given a sequence of inputs , a standard RNN computes a sequence of outputs by iterating the following equation : The RNN can easily map sequences to sequences whenever the alignment between the inputs the outputs is known ahead of time . Similar to ReLUs , DReLUs are less prone to the vanishing gradient problem , they are noise robust , and they induce sparse activations . We independently reproduce the QRNN experiments of Bradbury et al. [ 1 ] and compare our DReLU-based QRNNs with the original tanh-based QRNNs and Long Short-Term Memory networks ( LSTMs ) on sentiment classification and word-level language modeling . Additionally , we evaluate on character-level language modeling , showing that we are able to stack up to eight QRNN layers with DReLUs , thus making it possible to improve the current state-of-the-art in character-level language modeling over shallow architectures based on LSTMs . Nevertheless , their practical application is limited partly due to the long training time of multiple probabilistic forecasting models . Traditional quantile regression neural network ( QRNN ) can train a single model for making quantile forecasts for multiple quantiles at one time . Whereas , the training cost is still unaffordable with large datasets . This paper proposes an improved QRNN ( iQRNN ) to address the issues of traditional QRNN , which incorporates popular techniques in deep learning areas . A case study on a publicly available dataset shows that not only can the proposed iQRNN generate remarkably superior quantile forecasts than state-of-the-art methods , but also the proposed iQRNN is more accurate , stable , and computationally efficient than traditional QRNN . This paper presents a novel , Particle Swarm Optimization (PSO)-trained Quantile Regression Neural Network namely PSOQRNN , to forecast volatility from financial time series . We compared the effectiveness of PSOQRNN with that of the traditional volatility forecasting models , i.e. , Generalized Autoregressive Conditional Heteroskedasticity ( GARCH ) and three Artificial Neural Networks ( ANNs ) including Multi-Layer Perceptron ( MLP ) , General Regression Neural Network ( GRNN ) , Group Method of Data Handling ( GMDH ) , Random Forest ( RF ) and two Quantile Regression (QR)-based hybrids including Quantile Regression Neural Network ( QRNN ) and Quantile Regression Random Forest ( QRRF ) . The results indicate that the proposed PSOQRNN outperformed these models in terms of Mean Squared Error ( MSE ) , on a majority of the eight financial time series including exchange rates of USD versus JPY , GBP , EUR and INR , Gold Price , Crude Oil Price , Standard and Poor 500 ( S&P 500 ) Then based on the optimal weighted Euclidean distance metric method , KNN algorithm is used to find the similar examples more accurately . Finally , QRNN model is established to obtain different quantiles and approximately estimate the probability distribution of solar power output . The data from IEEE Working Group on Energy Forecasting is used to valid ate the effectiveness of proposed method and the experimental results show that the proposed model has reliable and accurate prediction ability . We found that all five neural network models were able to produce feasible models . QRNN model is outperforms with all statistical tests amongst other four models . The prediction accuracy is evaluated through the empirical analyses from the Guangdong province dataset in China and the California dataset in the United States . The simulation results demonstrate that the proposed method provides better performance for electricity consumption forecasting , in comparison with existing quantile regression neural network ( QRNN ) , back-propagation of errors neural network ( BP ) , radial basis function neural network ( RBF ) , quantile regression ( QR ) and nonlinear quantile regression ( NLQR ) . LASSO-QRNN can not only better learn the high-dimensional data in electricity consumption forecasting , but also provide more precise results . The primary results are those over oceans , where the QRNNs show great performance on similar levels as GPROF with respect to point estimate metrics such as the mean squared error . Furthermore , the QRNN retrievals are very fast , taking less than a millisecond per footprint on a standard computer . It turns out that extra spatial information improves the QRNNs , especially on making rain-no rain classifications with fractions of true positives and true negatives exceeding 0.67 and 0.96 respectively . A novel approach to extreme learning machine ( ELM ) ensembles is proposed . It incorporates majority voting into the recently proposed q-generalized random neural network ( QRNN ) to make the final decision for classification problems . Individual ELMs are trained with q-Gaussian activation functions using different values of the parameter q ( called the entropic index ) . We observe that the QRBFc estimator reduces the mean absolute deviation error obtained with other estimators by about 16 % , the introduction of the constraint allowing to lower the number of neurons and therefore the computation time . Finally , using a database of 416 electroencephalograms recorded on preterm infants , we compare the QYJ , QRBFc and QRNN models for the building of brain maturation curves which are based on the dependence of the mean duration of interburst intervals ( called IBIs — periods of quiescence between periods of normal electrical activity ) with the age . The pathological infants represent 12 % of the total population . Subsequently , the fuzzy C-means clustering ( FCM ) algorithm was applied to classify the 83 observations based on the blast parameters . Finally , based on the classification of the blasts , quantile regression neural network ( QRNN ) models were developed . The combination of FCM and QRNN models resulted in a novel , hybrid model ( FCM-QRNN ) for predicting blast-induced ground vibration . The US Bureau of Mines ( USBM ) , random forest ( RF ) , QRNN ( without clustering ) , and artificial neural network ( ANN ) models were also considered and compared with the FCM-QRNN model to obtain a comprehensive assessment of the proposed model . Like RNNs , QRNNs allow the output to depend on the overall order of elements in the sequence . We describe QRNN variants tailored to several natural language tasks , including document - level sentiment classification , language modeling , and character - level machine translation . These models outperform strong LSTM baselines on all three tasks while dramatically reducing computation time . The pooling component , like pooling layers in CNNs , lacks trainable parameters and allows fully parallel computation across minibatch and feature dimensions . Given an input sequence of - dimensional vectors , the convolutional subcomponent of a QRNN performs convolutions in the timestep dimension with a bank of filters , producing a sequence of - dimensional candidate vectors . In order to be useful for tasks that include prediction of the next token , the filters must not allow the computation for any given timestep to access information from future timesteps . Although the recurrent parts of these functions must be calculated for each timestep in sequence , their simplicity and parallelism along feature dimensions means that , in practice , evaluating them over even long sequences requires a negligible amount of computation time . A single QRNN layer thus performs an input - dependent pooling , followed by a gated linear combination of convolutional features . As with convolutional neural networks , two or more QRNN layers should be stacked to create a model with the capacity to approximate more complex functions . As QRNNs lack recurrent weights , the variational inference approach does not apply . Thus we extended zoneout to the QRNN architecture by modifying the pooling function to keep the previous pooling state for a stochastic subset of channels . Conveniently , this is equivalent to stochastically setting a subset of the QRNN ’s gate channels to 1 , or applying dropout on : Thus the pooling function itself need not be modified at all . We note that when using an off - the - shelf dropout layer in this context , it is important to remove automatic rescaling functionality from the implementation if it is present . In many experiments , we also apply ordinary dropout between layers , including between word embeddings and the first QRNN layer . Densely - Connected Layers The experiment uses a standard preprocessed version of the Penn Treebank ( PTB ) by Mikolov2010 . We implemented a gated QRNN model with medium hidden size : 2 layers with 640 units in each layer . Both QRNN layers use a convolutional filter width of two timesteps . Batches consist of 20 examples , each 105 timesteps . Comparing our results on the gated QRNN with zoneout to the results of LSTMs with both ordinary and variational dropout in Table [ reference ] , we see that the QRNN is highly competitive . The QRNN without zoneout strongly outperforms both our medium LSTM and the medium LSTM of Zaremba2014 which do not use recurrent dropout and is even competitive with variational LSTMs . For the QRNN implementation , however , the “ RNN ” layers are no longer the bottleneck . Indeed , there are diminishing returns from further optimization of the QRNN itself as the softmax and optimization overhead take equal or greater time . Note that the softmax , over a vocabulary size of only 10 , 000 words , is relatively small ; for tasks with larger vocabularies , the softmax would likely dominate computation time . We remove training sentences with more than 300 characters in English or German , and use a unified vocabulary of 187 Unicode code points . Our best performance on a development set ( TED.tst2013 ) was achieved using a four - layer encoder – decoder QRNN with 320 units per layer , no dropout or regularization , and gradient rescaling to a maximum magnitude of 5 . Inputs were supplied to the encoder reversed , while the encoder convolutions were not masked . The parallelism of the convolutional , pooling , and highway layers allows training speed comparable to subword - level models without hard - coded text segmentation . The QRNN encoder – decoder model shares the favorable parallelism and path - length properties exhibited by the ByteNet Kalchbrenner2016 , an architecture for character - level machine translation based on residual convolutions over binary trees . Their model was constructed to achieve three desired properties : parallelism , linear - time computational complexity , and short paths between any pair of words in order to better propagate gradient signals .
The first problem is how to include dynamics in a neural net . We have considered recurrent neural networks and dynamical neurons . We have implemented an extended backpropagation to take into account the dynamics in the training phase .
Methods Sampling from 143,003 unique vaccine-related Web pages shared on Twitter between January 2017 and March 2018 , we used a 7-point checklist adapted from validated tools and guidelines to manually appraise the credibility of 474 Web pages . These were used to train several classifiers ( random forests , support vector machines , and recurrent neural networks ) using the text from a Web page to predict whether the information satisfies each of the 7 criteria . Estimating the credibility of all other Web pages , we used the follower network to estimate potential exposures relative to a credibility score defined by the 7-point checklist .
We show that the long-term dependencies problem is lessened for a class of architectures called nonlinear autoregressive models with exogenous ( NARX ) recurrent neural networks , which have powerful representational capabilities . We have previously reported that gradient descent learning can be more effective in NARX networks than in recurrent neural network architectures that have " hidden states " on problems including grammatical inference and nonlinear system identification . Typically , the network converges much faster and generalizes better than other networks .
We discuss the tracking problem in the presence of unmodelled dynamics , for affine in the control nonlinear dynamical systems , whose nonlinearities are assumed unknown , using recurrent neural network structures . Based upon their proven approximation capabilities , Lyapunov stability theory is employed to develop smooth , partial state control and update laws , to guarantee the uniform ultimate boundedness of the tracking error , as well as uniform boundedness of all other signals in the closed loop .
In both cases , the neural network structure is trained with an extended Kalman filter algorithm . Simulation results illustrate the applicability of the recurrent neural network structures to estimate complex states of the biological processes in the presence of disturbances .
Since the behaviour of electric load time series is very much non-linear and seasonal , Neural Networks are best suited model for learning the Non-Linear behaviour within the data and for forecasting purpose . This paper deals with the Recurrent Neural Networks based Models : Long-Short-Term-Memory ( LSTM ) and Gated-Recurrent-Unit ( GRU ) to deal with this challenge . Observations have been made based on the distributed implementation of various configurations of LSTM-RNN & GRU-RNN on spark clusters for hyper parameter tuning purpose and deploying best suited configuration with least RMSE value using apache memos resource management .
Regular Papers.- CT and PET Registration Using Deformations Incorporating Tumor-Based Constraints.- Surface Grading Using Soft Colour-Texture Descriptors.- Support Vector Machines with Huffman Tree Architecture for Multiclass Classification.- Automatic Removal of Impulse Noise from Highly Corrupted Images.- Smoothing of Polygonal Chains for 2D Shape Representation Using a G 2-Continuous Cubic A-Spline.- A Robust Statistical Method for Brain Magnetic Resonance Image Segmentation.- Inference Improvement by Enlarging the Training Set While Learning DFAs.- A Computational Approach to Illusory Contour Perception Based on the Tensor Voting Technique.- A Novel Clustering Technique Based on Improved Noising Method.- Object Recognition in Indoor Video Sequences by Classifying Image Segmentation Regions Using Neural Networks.- Analysis of Directional Reflectance and Surface Orientation Using Fresnel Theory.- Lacunarity as a Texture Measure for Address Block Segmentation.- Measuring the Quality Evaluation for Image Segmentation.- Frame Deformation Energy Matching of On-Line Handwritten Characters.- Nonlinear Civil Structures Identification Using a Polynomial Artificial Neural Network.- A Method of Automatic Speaker Recognition Using Cepstral Features and Vectorial Quantization.- Classification of Boar Spermatozoid Head Images Using a Model Intracellular Density Distribution.- Speech Recognition Using Energy Parameters to Classify Syllables in the Spanish Language.- A Strategy for Atherosclerotic Lesions Segmentation.- Image Scale-Space from the Heat Kernel.- A Naive Solution to the One-Class Problem and Its Extension to Kernel Methods.- Nonlinear Modeling of Dynamic Cerebral Autoregulation Using Recurrent Neural Networks.- Neural Network Approach to Locate Motifs in Biosequences.- Free-Shaped Object Recognition Method from Partial Views Using Weighted Cone Curvatures.- Automatic Braille Code Translation System.- Automatic Extraction of DNA Profiles in Polyacrilamide Gel Electrophoresis Images.- The Use of Bayesian Framework for Kernel Selection in Vector Machines Classifiers.- Genetic Multivariate Polynomials : An Alternative Tool to Neural Networks.- Non-supervised Classification of 2D Color Images Using Kohonen Networks and a Novel Metric.- Data Dependent Wavelet Filtering for Lossless Image Compression.- A
Furthermore , they assumed a maximum of 9 cars per image . Approaches using recurrent neural networks [ reference ][ reference ] can handle a variable number of instances per image by segmenting an instance per time - step , but are currently restricted to only one object category . Our method , on the other hand , is able to handle both an arbitrary number of objects , and multiple object categories in the image with a single forward - pass through the network .
However , because of their auto - regressive property of requiring previous hidden states to be computed before the current time step , they can not benefit from parallelization . Variants of recurrent networks that use strided convolutions eschew the traditional time - step based computation kaiser2016can , lei2017training , bradbury2016quasi , gehring2016convenc , gehring2017convs2s , kalchbrenner2016neural . However , in these models , the operations needed to learn dependencies between distant positions can be difficult to learn hochreiter2001gradient , hochreiter1998vanishing .
The convolutional networks use window size on either side of the target token and produce a 300 - dimensional context - dependent vector . The recurrent networks use hidden layers of size 200 in either direction . We also added an extra hidden layer of size between each of the composition functions and the output layer –
In addition , we devise a metric called Relative Effective Context Length ( RECL ) that aims to perform a fair comparison of the gains brought by increasing the context lengths for different models . In this setting , Transformer - XL learns a RECL of 900 words on WikiText - 103 , while the numbers for recurrent networks and Transformer are only 500 and 128 . section : Related Work
Moreover , while all graph kernels have a training complexity at least quadratic in the number of graphs , which is prohibitive for large - scale problems , Patchy - san scales linearly with the number of graphs . Graph neural networks ( GNNs ) are a recurrent neural network architecture defined on graphs . GNNs apply recurrent neural networks for walks on the graph structure , propagating node representations until a fixed point is reached .
document : Multiplicative LSTM for sequence modelling We introduce multiplicative LSTM ( mLSTM ) , a recurrent neural network architecture for sequence modelling that combines the long short - term memory ( LSTM ) and multiplicative recurrent neural network architectures . mLSTM is characterised by its ability to have different recurrent transition functions for each possible input , which we argue makes it more expressive for autoregressive density estimation .
’s advantages , we introduce a memory capacity measure , the mean recurrent length , which is more suitable for RNNs with long skip connections than existing measures . We rigorously prove the advantages of the DilatedRNN over other recurrent neural architectures . The code for our method is publicly available .
Moreover , the network is end - to - end trainable . Our method is similar in spirit to a recent work , which detects text lines by finding and grouping a sequence of fine - scale text proposals through a CNN coupled with recurrent neural layers . In contrast , we detect oriented segments only using convolutional layers , yielding better flexibility and faster speed .
section : Concluding Remarks For visual question answering on COCO dataset , our implementation of a simple baseline achieves comparable performance to several recently proposed recurrent neural network - based approaches . To reach the correct prediction , the baseline captures the correlation between the informative words in the question and the answer , and that between image contents and the answer .
[ reference ] . We investigate an alternative recurrent neural network structure for addressing these issues . As shown in Figure 1 , the main idea is to model the hidden states of all words simultaneously at each recurrent step , rather than one word at a time .
The |V | dimensional y t models the discrete probability distribution for the next word . Note that the above formulation does not make any assumptions about the specifics of the recurrent neural units , and f could be replaced with a standard recurrent unit , a gated recurrent unit ( GRU ) [ reference ] , a long - short term memory ( LSTM ) unit [ reference ] , etc . For our experiments , we use LSTM units with two layers .
HLN is a combination of Lagrangian function and continuous Hopfield neural network where the Lagrangian function is directly used as the energy function for the continuous Hopfield neural network . In the HLN method , its energy function augmented by Hopfield terms from the continuous Hopfield network could damp out oscillation of the conventional Hopfield network during the convergence process . Consequently , the proposed HLN can overcome the disadvantages of the conventional Hopfield network in solving optimization problems for its simpler implementation , better global solution , faster convergence time , and larger scale applications .
Abstract Two major “ categories ” of unsupervised learning rules are used in artificial neural networks : ( i ) competitive learning , which is used in the adaptive resonance theory ( ART ) , the self-organizing map , and the neocognitron ; and ( ii ) Hebbian learning without lateral inhibition , which is used in the Hopfield network . Since the competitive learning is essentially Hebbian learning in the presence of lateral inhibition , the author attempts here to discuss general properties of these unsupervised learning rules in a unified paradigm .
The solution with the Hopfield algorithm has the complexity of the 4n + 16n algorithm , while the complexity of the Simulated Annealing algorithm is 10n2 . Judging from the use of memory in the implementation , the application of annealing algorithm is better than the Hopfield algorithm . but both are algorithms that " no works in place " .
In this approach , each operation is represented by a group of neurons , equal in number to the number of jobs in the problem . In contrast to a similar approach , developed by Foo and Takefuji , for a six jobs and six machines problem , our Hopfield-type model uses 36 neurons and our Boltzmann machine model needs 216 neurons , while their model requires 1332 neurons . Larger number of neurons require longer computer execution times for convergence .
We proposed and realized one new type models of neural networks , which takes into account property of anticipation . As the base model , the Hopfield type models with anticipation have been explored . The basic new qualities , discovered at research there is that possible multi-valued solutions of given neural networks .
Artificial neural networks are dynamic systems consisting of highly interconnected and parallel nonlinear processing elements that are shown to be extremely effective in computation . This paper presents a modified Hopfield architecture , which has equilibrium points representing the solution of the problems considered , i.e , dynamic programming problems and bipartite graph optimization . The internal parameters of the network have been computed using the valid-subspace technique .
In present paper , we describe completely innovation architecture of artificial neural nets based on Hopfield structure for solving of stereo matching problem . Hybrid neural network consists of classical analogue Hopfield neural network and maximal neural network .
To effectively decom- pose such mixtures , a fully constrained linear unmixing algorithm based on a multichannel Hopfield neural network ( MHNN ) is proposed in this letter . The proposed MHNN algorithm is actu- ally a Hopfield-based architecture which handles all the pixels in an image synchronously , instead of considering a per-pixel procedure . Due to the synchronous unmixing property of MHNN , a noise energy percentage ( NEP ) stopping criterion which uti- lizes the signal-to-noise ratio is proposed to obtain optimal re- sults for different applications automatically .
State-of-the-art hand gesture recognition methods have investigated the spatiotemporal features based on 3D convolutional neural networks ( 3DCNNs ) or convolutional long short-term memory ( ConvLSTM ) . However , they often suffer from the inefficiency due to the high computational complexity of their network structures .
The key to our design is to learn high-level gesture representations by taking advantages from Convolutional Residual Networks ( ResNets ) for training extremely deep models and Convolutional Long Short-Term Memory Networks ( ConvLSTM ) for dealing with time-series connections . More specifically , we first construct an architecture to simultaneously learn the spatiotemporal features from RGB and depth sequences through 3D ResNets which are then linked to a ConvLSTM to capture the temporal dependencies between them , and we show that they better combine appearance and motion information effectively . Second , to alleviate distractions from background and other variations , we propose a method that encodes the temporal information into a motion representation , while a two-stream architecture based on 2D-ResNets is then employed to extract deep features from this representation .
In particular , our ATFM is composed of two progressive Convolutional Long Short-Term Memory ( ConvLSTM \cite{xingjian2015convolutional } ) units connected with a convolutional layer . Specifically , the first ConvLSTM unit takes normal traffic flow features as input and generates a hidden state at each time-step , which is further fed into the connected convolutional layer for spatial attention map inference . The second ConvLSTM unit aims at learning the dynamic spatial-temporal representations from the attentionally weighted traffic flow features .
Convolutional long short-term memory ( ConvLSTM ) networks have been widely used for action/gesture recognition , and different attention mechanisms have also been embedded into ConvLSTM networks . This paper explores the redundancy of spatial convolutions and the effects of the attention mechanism in ConvLSTM , based on our previous gesture recognition architectures that combine the 3-D convolutional neural network ( CNN ) and ConvLSTM .
Owing to the limitation of FC-LSTM , existing methods have to convert 2-D feature maps into 1-D sequential feature vectors , resulting in severe damages of the valuable spatial and structural information of text images . In this paper , we argue that scene text recognition is essentially a spatiotemporal prediction problem for its 2-D image inputs , and propose a convolution LSTM (ConvLSTM)-based scene text recognizer , namely , FACLSTM , i.e. , focused attention ConvLSTM , where the spatial correlation of pixels is fully leveraged when performing sequential prediction with LSTM . Particularly , the attention mechanism is properly incorporated into an efficient ConvLSTM structure via the convolutional operations and additional character center masks are generated to help focus attention on right feature areas .
Through this way , we leverage the attention consistency across different layers to improve the robustness of the network . On the dynamic aspect , we propose a correlationbased ConvLSTM to appropriately balance the influence of the current and preceding frames to the prediction . Experimental results on the DHF1 K , Hollywood2 and UCF-sports datasets show that SalSAC outperforms many existing stateof-the-art methods .
First , the video sequences are input into a deep convolutional encoder – decoder network for extracting pixel-wise semantic features . Then , to exploit the temporal context , we propose a novel attention long short-term memory ( Attention ConvLSTM ) to model pixelwise changes over time . A spatial transformer network and a conditional random field layer are finally appended to reduce the sensitivity to camera motion and smooth the foreground boundaries .
Thirty-six sets of patient data were used in the study . Experimental results showed that our proposed ConvLSTM neural network produced a mean TRE of 2.85±1.72 mm and a mean Dice of 0.89 .
Nevertheless , they mostly focused on predicting air quality in discrete locations or used hand-crafted spatial and temporal features . In this paper , we propose the usage of Convolutional Long Short-Term Memory ( ConvLSTM ) model [ 16 ] , a combination of Convolutional Neural Networks and Long Short-Term Memory , which automatically manipulates both the spatial and temporal features of the data . Specially , we introduce how to transform the air pollution data into sequences of images which leverages the using of ConvLSTM model to interpolate and predict air quality for the entire city at the same time .
Aiming at the above phenomena and problems , this paper proposes an improved CLDNN structure . It combines the residual network and ConvLSTM to establish the residual ConvLSTM model , and replaces the fully connected LSTM model in the traditional CLDNN structure . The model structure solves the problems of the traditional CLDNN model , and can increase the model depth by stacking residual ConvLSTM blocks without gradient disappearance , gradient explosion and " degeneration " problems , which makes the speech recognition system perform better .
Distinct from previous methods , we aim to temporally integrate pyramidal feature hierarchy using ConvLSTM , and design a novel structure including a high-level ConvLSTM unit as well as a low-level one (HL-LSTM)for multi-scale feature maps . Moreover , we develop a creative temporal analysis unit , namely , ConvLSTM-based attention and attention-based ConvLSTM ( A&CL ) , in which the ConvLSTM-based attention is specially tailored for background suppression and scale suppression while the attention-based ConvLSTM temporally integrates attention-aware features . Finally , our method is evaluated on ImageNet VID dataset .
This paper presents one of these challenges and the method to solve it . The method is based on a recurrent artificial neural network that implements the well-known vector control strategy to determine the switching states of the pulse width modulation ( PWM ) based controlled boost converters optimally . Nested in the inner current control loop , the neural network based current controller tries to minimize the circulating current by reducing dq current errors and generates the dq voltage reference to derive PWM converters .
Analysis of internal structures of embodied and situated agents may provide insights into the mechanisms underlying adaptive behaviour . This paper is concerned with the evolution and analysis of visually-guided approach behaviour in a simulated robotic agent controlled by a recurrent artificial neural network , whose connection weights have been evolved using evolutionary algorithms . Analysis of the evolved behaviours and their network-internal mechanisms reveals a behavioural structure and organization resembling a Brooksian subsumption architecture .
Here , we demonstrate the efficacy of machine learning in predicting chaotic behavior in complex nonlinear mechanical systems . Specifically , we use quasi-recurrent neural networks to predict extremely chaotic time series data obtained from multistable origami systems . Additionally , while machine learning is often viewed as a " black box " , in this study we conduct hidden layer analysis to understand how the neural network can process not only periodic , but also chaotic data in an accurate manner .
In the quasi-linear recurrent neural network predictive control , the solution of the control law only need one-step derivation , which can greatly simplify the solution process of control law . At the same time , the quasi-linear recurrent neural network can effectively restrain the over-fitting problem in the identification process . Theoretical analysis and simulations are given to prove the simplicity and effectiveness of the proposed computing method .
We explored the performance of our previous single-dataset-trained sentence segmentation architecture in a richer scenario involving three new datasets used to diagnose cognitive impairments , comprising different stories and two types of stimulus presentation for eliciting narratives — visual and oral — via illustrated story-book and sequence of scenes , and by retelling . Also , we proposed and evaluated three modifications to our previous RCNN architecture : ( i ) the inclusion of a Linear Chain CRF ; ( ii ) the inclusion of a self-attention mechanism ; and ( iii ) the replacement of the LSTM recurrent layer by a Quasi-Recurrent Neural Network layer . Our study allowed us to develop two new models for segmenting impaired speech transcriptions , along with an ideal combination of datasets and specific groups of narratives to be used as the training set .
document : Quasi - Recurrent Neural Networks Recurrent neural networks are a powerful tool for modeling sequential data , but the dependence of each timestep ’s computation on the previous timestep ’s output limits parallelism and makes RNNs unwieldy for very long sequences . We introduce quasi - recurrent neural networks ( QRNNs ) , an approach to neural sequence modeling that alternates convolutional layers , which apply in parallel across timesteps , and a minimalist recurrent pooling function that applies in parallel across channels . Despite lacking trainable recurrent layers , stacked QRNNs have better predictive accuracy than stacked LSTMs of the same hidden size .
While the motivation and constraints described in that work are different , ’s concepts of “ learnware ” and “ firmware ” parallel our discussion of convolution - like and pooling - like subcomponents . As the use of a fully connected layer for recurrent connections violates the constraint of “ strong typing ” , all strongly - typed RNN architectures ( including the T - RNN , T - GRU , and T - LSTM ) are also quasi - recurrent . However , some QRNN models ( including those with attention or skip - connections ) are not “ strongly typed ” .
Long short term memory ( LSTM ) [ reference ] , one of the variants of recurrent neural network ( RNN ) , is inept at remembering past stories because of their small internal memory [ reference ] . To cope with this problem , J. has proposed a new class of memory - augmented model called Memory Network ( MemNN ) .
section : The model The Recurrent Neural Network ( RNN ) is a natural generalization of feedforward neural networks to sequences . Given a sequence of inputs , a standard RNN computes a sequence of outputs by iterating the following equation : The RNN can easily map sequences to sequences whenever the alignment between the inputs the outputs is known ahead of time .
Similar to ReLUs , DReLUs are less prone to the vanishing gradient problem , they are noise robust , and they induce sparse activations . We independently reproduce the QRNN experiments of Bradbury et al. [ 1 ] and compare our DReLU-based QRNNs with the original tanh-based QRNNs and Long Short-Term Memory networks ( LSTMs ) on sentiment classification and word-level language modeling . Additionally , we evaluate on character-level language modeling , showing that we are able to stack up to eight QRNN layers with DReLUs , thus making it possible to improve the current state-of-the-art in character-level language modeling over shallow architectures based on LSTMs .
Nevertheless , their practical application is limited partly due to the long training time of multiple probabilistic forecasting models . Traditional quantile regression neural network ( QRNN ) can train a single model for making quantile forecasts for multiple quantiles at one time . Whereas , the training cost is still unaffordable with large datasets . This paper proposes an improved QRNN ( iQRNN ) to address the issues of traditional QRNN , which incorporates popular techniques in deep learning areas . A case study on a publicly available dataset shows that not only can the proposed iQRNN generate remarkably superior quantile forecasts than state-of-the-art methods , but also the proposed iQRNN is more accurate , stable , and computationally efficient than traditional QRNN .
This paper presents a novel , Particle Swarm Optimization (PSO)-trained Quantile Regression Neural Network namely PSOQRNN , to forecast volatility from financial time series . We compared the effectiveness of PSOQRNN with that of the traditional volatility forecasting models , i.e. , Generalized Autoregressive Conditional Heteroskedasticity ( GARCH ) and three Artificial Neural Networks ( ANNs ) including Multi-Layer Perceptron ( MLP ) , General Regression Neural Network ( GRNN ) , Group Method of Data Handling ( GMDH ) , Random Forest ( RF ) and two Quantile Regression (QR)-based hybrids including Quantile Regression Neural Network ( QRNN ) and Quantile Regression Random Forest ( QRRF ) . The results indicate that the proposed PSOQRNN outperformed these models in terms of Mean Squared Error ( MSE ) , on a majority of the eight financial time series including exchange rates of USD versus JPY , GBP , EUR and INR , Gold Price , Crude Oil Price , Standard and Poor 500 ( S&P 500 )
Then based on the optimal weighted Euclidean distance metric method , KNN algorithm is used to find the similar examples more accurately . Finally , QRNN model is established to obtain different quantiles and approximately estimate the probability distribution of solar power output . The data from IEEE Working Group on Energy Forecasting is used to valid ate the effectiveness of proposed method and the experimental results show that the proposed model has reliable and accurate prediction ability .
We found that all five neural network models were able to produce feasible models . QRNN model is outperforms with all statistical tests amongst other four models .
The prediction accuracy is evaluated through the empirical analyses from the Guangdong province dataset in China and the California dataset in the United States . The simulation results demonstrate that the proposed method provides better performance for electricity consumption forecasting , in comparison with existing quantile regression neural network ( QRNN ) , back-propagation of errors neural network ( BP ) , radial basis function neural network ( RBF ) , quantile regression ( QR ) and nonlinear quantile regression ( NLQR ) . LASSO-QRNN can not only better learn the high-dimensional data in electricity consumption forecasting , but also provide more precise results .
The primary results are those over oceans , where the QRNNs show great performance on similar levels as GPROF with respect to point estimate metrics such as the mean squared error . Furthermore , the QRNN retrievals are very fast , taking less than a millisecond per footprint on a standard computer . It turns out that extra spatial information improves the QRNNs , especially on making rain-no rain classifications with fractions of true positives and true negatives exceeding 0.67 and 0.96 respectively .
A novel approach to extreme learning machine ( ELM ) ensembles is proposed . It incorporates majority voting into the recently proposed q-generalized random neural network ( QRNN ) to make the final decision for classification problems . Individual ELMs are trained with q-Gaussian activation functions using different values of the parameter q ( called the entropic index ) .
We observe that the QRBFc estimator reduces the mean absolute deviation error obtained with other estimators by about 16 % , the introduction of the constraint allowing to lower the number of neurons and therefore the computation time . Finally , using a database of 416 electroencephalograms recorded on preterm infants , we compare the QYJ , QRBFc and QRNN models for the building of brain maturation curves which are based on the dependence of the mean duration of interburst intervals ( called IBIs — periods of quiescence between periods of normal electrical activity ) with the age . The pathological infants represent 12 % of the total population .
Subsequently , the fuzzy C-means clustering ( FCM ) algorithm was applied to classify the 83 observations based on the blast parameters . Finally , based on the classification of the blasts , quantile regression neural network ( QRNN ) models were developed . The combination of FCM and QRNN models resulted in a novel , hybrid model ( FCM-QRNN ) for predicting blast-induced ground vibration . The US Bureau of Mines ( USBM ) , random forest ( RF ) , QRNN ( without clustering ) , and artificial neural network ( ANN ) models were also considered and compared with the FCM-QRNN model to obtain a comprehensive assessment of the proposed model .
Like RNNs , QRNNs allow the output to depend on the overall order of elements in the sequence . We describe QRNN variants tailored to several natural language tasks , including document - level sentiment classification , language modeling , and character - level machine translation . These models outperform strong LSTM baselines on all three tasks while dramatically reducing computation time .
The pooling component , like pooling layers in CNNs , lacks trainable parameters and allows fully parallel computation across minibatch and feature dimensions . Given an input sequence of - dimensional vectors , the convolutional subcomponent of a QRNN performs convolutions in the timestep dimension with a bank of filters , producing a sequence of - dimensional candidate vectors . In order to be useful for tasks that include prediction of the next token , the filters must not allow the computation for any given timestep to access information from future timesteps .
Although the recurrent parts of these functions must be calculated for each timestep in sequence , their simplicity and parallelism along feature dimensions means that , in practice , evaluating them over even long sequences requires a negligible amount of computation time . A single QRNN layer thus performs an input - dependent pooling , followed by a gated linear combination of convolutional features . As with convolutional neural networks , two or more QRNN layers should be stacked to create a model with the capacity to approximate more complex functions .
As QRNNs lack recurrent weights , the variational inference approach does not apply . Thus we extended zoneout to the QRNN architecture by modifying the pooling function to keep the previous pooling state for a stochastic subset of channels . Conveniently , this is equivalent to stochastically setting a subset of the QRNN ’s gate channels to 1 , or applying dropout on : Thus the pooling function itself need not be modified at all . We note that when using an off - the - shelf dropout layer in this context , it is important to remove automatic rescaling functionality from the implementation if it is present . In many experiments , we also apply ordinary dropout between layers , including between word embeddings and the first QRNN layer . Densely - Connected Layers
The experiment uses a standard preprocessed version of the Penn Treebank ( PTB ) by Mikolov2010 . We implemented a gated QRNN model with medium hidden size : 2 layers with 640 units in each layer . Both QRNN layers use a convolutional filter width of two timesteps .
Batches consist of 20 examples , each 105 timesteps . Comparing our results on the gated QRNN with zoneout to the results of LSTMs with both ordinary and variational dropout in Table [ reference ] , we see that the QRNN is highly competitive . The QRNN without zoneout strongly outperforms both our medium LSTM and the medium LSTM of Zaremba2014 which do not use recurrent dropout and is even competitive with variational LSTMs .
For the QRNN implementation , however , the “ RNN ” layers are no longer the bottleneck . Indeed , there are diminishing returns from further optimization of the QRNN itself as the softmax and optimization overhead take equal or greater time . Note that the softmax , over a vocabulary size of only 10 , 000 words , is relatively small ; for tasks with larger vocabularies , the softmax would likely dominate computation time .
We remove training sentences with more than 300 characters in English or German , and use a unified vocabulary of 187 Unicode code points . Our best performance on a development set ( TED.tst2013 ) was achieved using a four - layer encoder – decoder QRNN with 320 units per layer , no dropout or regularization , and gradient rescaling to a maximum magnitude of 5 . Inputs were supplied to the encoder reversed , while the encoder convolutions were not masked .
The parallelism of the convolutional , pooling , and highway layers allows training speed comparable to subword - level models without hard - coded text segmentation . The QRNN encoder – decoder model shares the favorable parallelism and path - length properties exhibited by the ByteNet Kalchbrenner2016 , an architecture for character - level machine translation based on residual convolutions over binary trees . Their model was constructed to achieve three desired properties : parallelism , linear - time computational complexity , and short paths between any pair of words in order to better propagate gradient signals .
Process control with neural networks: an example Automatically Appraising the Credibility of Vaccine-Related Web Pages Shared on Social Media: A Twitter Surveillance Study Learning long-term dependencies in NARX recurrent neural networks Nonlinear adaptive control in the presence of unmodelled dynamics using neural networks Artificial Neural Networks Based on Nonlinear Bioprocess Models for Predicting Wastewater Organic Compounds and Biofuel Production Energy Load Forecasting using Deep Learning Approach-LSTM and GRU in Spark Cluster Progress in Pattern Recognition, Image Analysis and Applications, 10th Iberoamerican Congress on Pattern Recognition, CIARP 2005, Havana, Cuba, November 15-18, 2005, Proceedings Holistic, Instance-Level Human Parsing Weighted Transformer Network for Machine Translation Compositional Sequence Labeling Models for Error Detection in Learner Writing Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context Learning Convolutional Neural Networks for Graphs Multiplicative LSTM for sequence modelling Dilated Recurrent Neural Networks Detecting Oriented Text in Natural Images by Linking Segments Simple Baseline for Visual Question Answering Sentence-State LSTM for Text Representation Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling Hopfield Lagrange Network for Economic Load Dispatch Distributed memory and localized memory in neural networks Comparison of the Traveling Salesman Problem Analysis Using Neural Network Method A neural network approach for job shop scheduling problems Neural Networks with Strong Anticipation and Some Related Problems of Complexity Theory The modified Hopfield architecture applied in dynamic programming problems and bipartite graph optimization Hybrid-Maximum Neural Network for Depth Analysis from Stereo-Image Mixture Analysis by Multichannel High Performance Gesture Recognition via Effective and Efficient Temporal Modeling MultiD-CNN: A multi-dimensional feature learning approach based on deep convolutional networks for gesture recognition in RGB-D image sequences Dynamic Spatial-Temporal Representation Leaning for Traffic Flow Prediction. Redundancy and Attention in Convolutional LSTM for Gesture Recognition FACLSTM: ConvLSTM with focused attention for scene text recognition SalSAC: A Video Saliency Prediction Model with Shuffled Attentions and Correlation-Based ConvLSTM Pixelwise Deep Sequence Learning for Moving Object Detection Weekly supervised convolutional long short-term memory neural networks for MR-TRUS registration Spatiotemporal Deep Learning Model for Citywide Air Pollution Interpolation and Prediction End-to-end speech recognition system based on improved CLDNN structure TSSD: Temporal Single-Shot Detector Based on Attention and LSTM Intelligent control of power sharing between parallel-connected boost converters in micro-girds Evolution of visually-guided approach behaviour in recurrent artificial neural network robot controllers Data-driven prediction and analysis of chaotic origami dynamics. Quasi-Linear Recurrent Neural Network based Identification and Predictive Control Evaluating Sentence Segmentation in Different Datasets of Neuropsychological Language Tests in Brazilian Portuguese Quasi-Recurrent Neural Networks Finding ReMO (Related Memory Object): A Simple Neural Architecture for Text based Reasoning Sequence to Sequence Learning with Neural Networks Dual Rectified Linear Units (DReLUs): A replacement for tanh activation functions in Quasi-Recurrent Neural Networks An Improved Quantile Regression Neural Network for Probabilistic Load Forecasting Forecasting financial time series volatility using Particle Swarm Optimization trained Quantile Regression Neural Network Photovoltaic power generation probabilistic prediction based on a new dynamic weighting method and quantile regression neural network Performance Analysis Of Neural Network Models For Oxazolines And Oxazoles Derivatives Descriptor Dataset Electricity consumption probability density forecasting method based on LASSO-Quantile Regression Neural Network A Bayesian machine learning approach to passive microwave precipitation retrievals Voting based q-generalized extreme learning machine Reference Curves Estimation Using Conditional Quantile and Radial Basis Function Network with Mass Constraint Prediction of Blast-Induced Ground Vibration Intensity in Open-Pit Mines Using Unmanned Aerial Vehicle and a Novel Intelligence System Quasi-Recurrent Neural Networks
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BMVC ArXiv ACL ACL ICML ICLR NIPS 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ArXiv ACL ICLR ICAISC (2) IJCAI AAAI IJCNN LREC ICLR ArXiv NIPS ICLR
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