scholarly journals An autoencoder-based neural network model for selectional preference: evidence from pseudo-disambiguation and cloze tasks

2017 ◽  
Vol 8 (2) ◽  
pp. 93-125
Author(s):  
Aki-Juhani Kyröläinen ◽  
Juhani Luotolahti ◽  
Filip Ginter
Keyword(s):  
Neural Network ◽  
Semantic Similarity ◽  
Network Model ◽  
Neural Network Model ◽  
Semantic Information ◽  
Contextual Information ◽  
Neural Model ◽  
The Other ◽  
Excellent Performance

Intuitively, some predicates have a better fit with certain arguments than others. Usage-based models of language emphasize the importance of semantic similarity in shaping the structuring of constructions (form and meaning). In this study, we focus on modeling the semantics of transitive constructions in Finnish and present an autoencoder-based neural network model trained on semantic vectors based on Word2vec. This model builds on the distributional hypothesis according to which semantic information is primarily shaped by contextual information. Specifically, we focus on the realization of the object. The performance of the model is evaluated in two tasks: a pseudo-disambiguation and a cloze task. Additionally, we contrast the performance of the autoencoder with a previously implemented neural model. In general, the results show that our model achieves an excellent performance on these tasks in comparison to the other models. The results are discussed in terms of usage-based construction grammar.Kokkuvõte. Aki-Juhani Kyröläinen, M. Juhani Luotolahti ja Filip Ginter: Autokoodril põhinev närvivõrkude mudel valikulisel eelistamisel. Intuitiivselt tundub, et mõned argumendid sobivad teatud predikaatidega paremini kokku kui teised. Kasutuspõhised keelemudelid rõhutavad konstruktsioonide struktuuri (nii vormi kui tähenduse) kujunemisel tähendusliku sarnasuse olulisust. Selles uurimuses modelleerime soome keele transitiivsete konstruktsioonide semantikat ja esitame närvivõrkude mudeli ehk autokoodri. Mudel põhineb distributiivse semantika hüpoteesil, mille järgi kujuneb semantiline info peamiselt konteksti põhjal. Täpsemalt keskendume uurimuses objektile. Mudelit hindame nii valeühestamise kui ka lünkülesande abil. Kõrvutame autokoodri tulemusi varem välja töötatud neurovõrgumudelitega ja tõestame, et meie mudel töötab võrreldes teiste mudelitega väga hästi. Tulemused esitame kasutuspõhise konstruktsioonigrammatika kontekstis.Võtmesõnad: neurovõrk; autokooder; tähendusvektor; kasutuspõhine mudel; soome keel

BOF Endpoint Prediction Based on Multi-Neural Network Model

2013 ◽  
Vol 441 ◽  
pp. 666-669 ◽  
Author(s):  
You Jun Yue ◽  
Ying Dong Yao ◽  
Hui Zhao ◽  
Hong Jun Wang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Large Data ◽  
Neural Model ◽  
Large Data Sets ◽  
Data Sets ◽  
Control Precision ◽  
Detection Technology ◽  
Multi Neural Network

In order to solve the problem that the small and middle converters unable to introduce the sublance detection technology to improve the control precision of endpoint because of the constraints of economy and technology, a method which combine the pedigree cluster and neural network is studied, the pedigree cluster divide the large data sets into several categories, the degree of similarity will be relatively high in each category after division, then train neural model for every category. Finally make predictions. Simulation results show that the multi-neural network model has better prediction results.

Deep Learning Model for Real-Time Prediction of Intradialytic Hypotension

2021 ◽  
pp. CJN.09280620
Author(s):  
Hojun Lee ◽  
Donghwan Yun ◽  
Jayeon Yoo ◽  
Kiyoon Yoo ◽  
Yong Chul Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Receiver Operating Characteristic ◽  
Network Model ◽  
Recurrent Neural Network ◽  
Neural Network Model ◽  
Operating Characteristic ◽  
The Other ◽  
Intradialytic Hypotension ◽  
Deep Learning Model

Background and objectivesIntradialytic hypotension has high clinical significance. However, predicting it using conventional statistical models may be difficult because several factors have interactive and complex effects on the risk. Herein, we applied a deep learning model (recurrent neural network) to predict the risk of intradialytic hypotension using a timestamp-bearing dataset.Design, setting, participants, & measurementsWe obtained 261,647 hemodialysis sessions with 1,600,531 independent timestamps (i.e., time-varying vital signs) and randomly divided them into training (70%), validation (5%), calibration (5%), and testing (20%) sets. Intradialytic hypotension was defined when nadir systolic BP was <90 mm Hg (termed intradialytic hypotension 1) or when a decrease in systolic BP ≥20 mm Hg and/or a decrease in mean arterial pressure ≥10 mm Hg on the basis of the initial BPs (termed intradialytic hypotension 2) or prediction time BPs (termed intradialytic hypotension 3) occurred within 1 hour. The area under the receiver operating characteristic curves, the area under the precision-recall curves, and F1 scores obtained using the recurrent neural network model were compared with those obtained using multilayer perceptron, Light Gradient Boosting Machine, and logistic regression models.ResultsThe recurrent neural network model for predicting intradialytic hypotension 1 achieved an area under the receiver operating characteristic curve of 0.94 (95% confidence intervals, 0.94 to 0.94), which was higher than those obtained using the other models (P<0.001). The recurrent neural network model for predicting intradialytic hypotension 2 and intradialytic hypotension 3 achieved area under the receiver operating characteristic curves of 0.87 (interquartile range, 0.87–0.87) and 0.79 (interquartile range, 0.79–0.79), respectively, which were also higher than those obtained using the other models (P≤0.001). The area under the precision-recall curve and F1 score were higher using the recurrent neural network model than they were using the other models. The recurrent neural network models for intradialytic hypotension were highly calibrated.ConclusionsOur deep learning model can be used to predict the real-time risk of intradialytic hypotension.

A Principal Components Analysis Self-Organizing Neural Network Model and Computational Experiment

2013 ◽  
Vol 756-759 ◽  
pp. 3330-3335
Author(s):  
Ji Fu Nong
Keyword(s):  
Neural Network ◽  
Principal Components Analysis ◽  
Network Model ◽  
Neural Network Model ◽  
Principal Components ◽  
Computational Experiment ◽  
Neural Model ◽  
Self Organizing Map ◽  
Components Analysis ◽  
Self Organizing

We propose a new self-organizing neural model that performs principal components analysis. It is also related to the adaptive subspace self-organizing map (ASSOM) network, but its training equations are simpler. Experimental results are reported, which show that the new model has better performance than the ASSOM network.

scholarly journals Replicate, Walk, and Stop on Syntax: An Effective Neural Network Model for Aspect-Level Sentiment Classification

2020 ◽  
Vol 34 (05) ◽  
pp. 9685-9692
Author(s):  
Yaowei Zheng ◽  
Richong Zhang ◽  
Samuel Mensah ◽  
Yongyi Mao
Keyword(s):  
Neural Network ◽  
Random Walk ◽  
Network Model ◽  
Neural Network Model ◽  
Syntactic Structure ◽  
Contextual Information ◽  
Empirical Studies ◽  
Specific Aspect ◽  
Sentiment Classification ◽  
Benchmark Datasets

Aspect-level sentiment classification (ALSC) aims at predicting the sentiment polarity of a specific aspect term occurring in a sentence. This task requires learning a representation by aggregating the relevant contextual features concerning the aspect term. Existing methods cannot sufficiently leverage the syntactic structure of the sentence, and hence are difficult to distinguish different sentiments for multiple aspects in a sentence. We perceive the limitations of the previous methods and propose a hypothesis about finding crucial contextual information with the help of syntactic structure. For this purpose, we present a neural network model named RepWalk which performs a replicated random walk on a syntax graph, to effectively focus on the informative contextual words. Empirical studies show that our model outperforms recent models on most of the benchmark datasets for the ALSC task. The results suggest that our method for incorporating syntactic structure enriches the representation for the classification.

scholarly journals State-of-Charge Estimation of Lithium-Ion Battery Pack Based on Improved RBF Neural Networks

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Li Zhang ◽  
Min Zheng ◽  
Dajun Du ◽  
Yihuan Li ◽  
Minrui Fei ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Rbf Neural Network ◽  
Lithium Ion ◽  
Neural Model ◽  
State Of Charge ◽  
Battery Pack ◽  
Soc Estimation ◽  
Battery Packs

Lithium-ion batteries have been widely used as energy storage systems and in electric vehicles due to their desirable balance of both energy and power densities as well as continual falling price. Accurate estimation of the state-of-charge (SOC) of a battery pack is important in managing the health and safety of battery packs. This paper proposes a compact radial basis function (RBF) neural model to estimate the state-of-charge (SOC) of lithium battery packs. Firstly, a suitable input set strongly correlated with the package SOC is identified from directly measured voltage, current, and temperature signals by a fast recursive algorithm (FRA). Secondly, a RBF neural model for battery pack SOC estimation is constructed using the FRA strategy to prune redundant hidden layer neurons. Then, the particle swarm optimization (PSO) algorithm is used to optimize the kernel parameters. Finally, a conventional RBF neural network model, an improved RBF neural model using the two stage method, and a least squares support vector machine (LSSVM) model are also used to estimate the battery SOC as a comparative study. Simulation results show that generalization error of SOC estimation using the novel RBF neural network model is less than half of that using other methods. Furthermore, the model training time is much less than the LSSVM method and the improved RBF neural model using the two-stage method.

SENSITIVE RESPONSE OF A CHAOTIC WANDERING STATE TO MEMORY FRAGMENT INPUTS IN A CHAOTIC NEURAL NETWORK MODEL

2004 ◽  
Vol 14 (04) ◽  
pp. 1413-1421 ◽  
Author(s):  
JOUSUKE KUROIWA ◽  
NAOKI MASUTANI ◽  
SHIGETOSHI NARA ◽  
KAZUYUKI AIHARA
Keyword(s):  
Neural Network ◽  
State Space ◽  
Network Model ◽  
Neural Network Model ◽  
Chaotic Dynamics ◽  
External Input ◽  
The Other ◽  
Chaotic Neural Network ◽  
Dynamical Properties ◽  
Memory Pattern

Dynamical properties of a chaotic neural network model in a chaotically wandering state are studied with respect to sensitivity to weak input of a memory fragment. In certain parameter regions, the network shows weakly chaotic wandering, which means that the orbits of network dynamics in the state space are localized around several memory patterns. In the other parameter regions, the network shows highly developed chaotic wandering, that is, the orbits become itinerant through ruins of all the memory patterns. In the latter case, once the external input consisting of a memory fragment is applied to the network, the orbit quickly moves to the vicinity of the corresponding memory pattern including the memory fragment within several iteration steps. Thus, chaotic dynamics in the model is effective for instantaneous search among memory patterns.

scholarly journals Outlet Temperature Prediction of Boiling Heat Transfer in Helical Coils through Artificial Neural Network

Proceedings ◽  
2020 ◽  
Vol 39 (1) ◽  
pp. 16
Author(s):  
Krisana Insom ◽  
Patcharin Kamsing ◽  
Thaweerath Phisannupawong ◽  
Peerapong Torteeka
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Network Model ◽  
Neural Network Model ◽  
Boiling Heat Transfer ◽  
Neural Model ◽  
Outlet Temperature ◽  
Ann Model ◽  
Testing Stage ◽  
Helical Coils

In the present study, deep learning neural network model has been employed in many engineering problems including heat transfer prediction. The main consideration of this document is to predict the performance of the boiling heat transfer in helical coils under terrestrial gravity conditions and compare with actual experimental data. Total of 877 data sample has been used in the present neural model. Artificial new Neural Network (ANN) model developed in Python environment with Multi-layer Perceptron (MLP) using four parameters (helical coils dimensions, mass flow rate, heating power, inlet temperature) and one parameter (outlet temperature) has been used in the input layer and output layer in order. Levenberg-Marquardt (LM) algorithm using L2 Regularization to find out the optimal model. A typical feed-forward neural network model composed of three layers, with 30 numbers of neurons in each hidden layer, has been found as optimal based on statistical error analysis. The 4-30-30-1 neural model predicts the characteristics of the helical coil with the accuracy of 98.16 percent in the training stage and 96.68 percent in the testing stage. The result indicated that the proposed ANN model successfully predicts the heat transfer performance in helical coils and can be applied for others operation concerned with heat transfer prediction for future works

scholarly journals Interactive Learning of Dialog Scenarios from Examples

2020 ◽  
Author(s):  
Daiga Deksne ◽  
Raivis Skadiņš
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Interactive Learning ◽  
Neural Model ◽  
Action Prediction ◽  
The Public ◽  
Dialog System ◽  
The Neural Network ◽  
Model Training

This paper reports on the development of a toolkit that enables collecting dialog corpus for end-to-end goal-oriented dialog system training. The toolkit includes the neural network model that interactively learns to predict the next virtual assistant (VA) action from the conversation history. We start with exploring methods for VA dialog scenario learning from examples after we perform several experiments with the English DSTC dialog sets in order to find the optimal strategy for neural model training. The chosen algorithm is used for training the next action prediction model for the Latvian dialogs in the public transport inquiries domain collected using the platform. The accuracy for the English and the Latvian dialog models is similar – 0.84 and 0.86. This shows that the chosen method for neural network model training is language independent.

scholarly journals Encoding Longer-Term Contextual Information with Predictive Coding and Ego-Motion

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-15
Author(s):  
Junpei Zhong ◽  
Angelo Cangelosi ◽  
Tetsuya Ogata ◽  
Xinzheng Zhang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Contextual Information ◽  
Predictive Coding ◽  
Sensory Information ◽  
Hierarchical Architecture ◽  
Motor Action ◽  
Neural Activities ◽  
Different Levels

Studies suggest that, within the hierarchical architecture, the topological higher level possibly represents the scenarios of the current sensory events with slower changing activities. They attempt to predict the neural activities on the lower level by relaying the predicted information after the scenario of the sensorimotor event has been determined. On the other hand, the incoming sensory information corrects such prediction of the events on the higher level by the fast-changing novel or surprising signal. From this point, we propose a predictive hierarchical artificial neural network model that examines this hypothesis on neurorobotic platforms. It integrates the perception and action in the predictive coding framework. Moreover, in this neural network model, there are different temporal scales of predictions existing on different levels of the hierarchical predictive coding architecture, which defines the temporal memories in recording the events occurring. Also, both the fast- and the slow-changing neural activities are modulated by the motor action. Therefore, the slow-changing neurons can be regarded as the representation of the recent scenario which the sensorimotor system has encountered. The neurorobotic experiments based on the architecture were also conducted.

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