###### Locally Recurrent Neural N

- Model-Based Fault Detection and Isolation Using Locally Recurrent Neural Networks. (barnesandnoble.com)

###### NETWORKS

- Hardware accelerator templates and design frameworks for implementing recurrent neural networks (RNNs) and variants thereof are described. (patents.com)
- Using time-dependent neural networks for EEG classification. (semanticscholar.org)
- The edited book aims to reflect the latest progresses made in different areas of neural computation, including theoretical neural computation, biologically plausible neural modeling, computational cognitive science, artificial neural networks - architectures and learning algorithms and their applications in real-world problems. (springer.com)
- Title: Biologically plausible deep learning for recurrent spiking neural networks. (berkeley.edu)
- In the same vein, we propose a different model for learning in recurrent neural networks (RNNs), known as McCulloch-Pitts processes. (berkeley.edu)
- Tentative / Confirmed Speakers == '''December 6th 2017''' * Speaker: Joel Makin * Time: 12:00 * Affiliation: UCSF * Host: Bruno * Status: tentative * Title: * Abstract: '''December 13, 2017''' * Speaker: Zhaoping Li * Time: 12:00 * Affiliation: UCL * Host: Bruno/Frederic Theunissen * Status: confirmed * Title: * Abstract: '''December 14, 2017''' * Speaker: * Time: 12:00 * Affiliation: * Host: Chris Hillar * Status: tentative * Title: Biologically plausible deep learning for recurrent spiking neural networks. (berkeley.edu)
- Park, June Ho 2000-07-30 00:00:00 Analysis of patterns of temporal variation in community dynamics was conducted by combining two unsupervised artificial neural networks, the Adaptive Resonance Theory (ART) and the Kohonen network. (deepdyve.com)
- Indeed, the logical possibility of such 'super-powerful' systems has been demonstrated in the form of analog artificial neural networks. (springer.com)
- We present our first results in applications of recurrent neural networks to Russian. (springerprofessional.de)
- We train several recurrent neural networks on a lemmatized news corpus to mitigate the problem of data sparseness. (springerprofessional.de)
- Finally we train the Ranking SVM model and show that combination of recurrent neural networks and morphological information gives better results than 5-gram model with Knesser-Ney discounting. (springerprofessional.de)
- Mikolov, T.: Statistical language models based on neural networks. (springerprofessional.de)
- Computer networks with an emphasis on network programming and applications. (toronto.edu)
- The paper Drug Analogs from Fragment-Based Long Short-Term Memory Generative Neural Networks has been published by the Journal of Chemical Information and Modeling. (gdb.tools)
- Recent applications of recurrent neural networks (RNN) enable training models that sample the chemical space. (gdb.tools)
- 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)
- Rovithakis, G.A., Christodoulou, M.A.: Adaptive Control with Recurrent High-Order Neural Networks: Theory and Industrial Applications. (springer.com)
- 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)
- In: Wang J., Yen G.G., Polycarpou M.M. (eds) Advances in Neural Networks - ISNN 2012. (springer.com)
- Long-term Recurrent Convolutional Networks for Visual Recognition and Description, CVPR 2015. (slideshare.net)
- Video paragraph captioning using hierarchical recurrent neural networks. (slideshare.net)
- Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks. (slideshare.net)
- Developing artificial neural networks for safety critical systems. (rwth-aachen.de)
- Automated product grade transitions, exposing the inherent and latent dangers of neural networks in manufacturing process control: an industrial case study. (rwth-aachen.de)
- A new structure adaptation algorithm for RBF networks and its application. (rwth-aachen.de)
- Reconfigurable hardware for neural networks: binary versus stochastic. (rwth-aachen.de)
- Weekly milk prediction on dairy goats using neural networks. (rwth-aachen.de)
- Evaluation of the performance of backpropagation and radial basis function neural networks in predicting the drill flank wear. (rwth-aachen.de)
- Technological information extraction of free form surfaces using neural networks. (rwth-aachen.de)
- Integrating Matlab Neural Networks Toolbox functionality in a fully reusable software component library. (rwth-aachen.de)
- Prediction of retail sales of footwear using feedforward and recurrent neural networks. (rwth-aachen.de)
- Hamzacebi, C., Kutay, F.: Electric Consumption Forecasting of Turkey Using Artificial Neural Networks Up to Year 2000. (springer.com)
- Lee, C.-H., Teng, C.-C.: Identification and Control of Dynamic Systems Using Recurrent Fuzzy Neural Networks. (springer.com)
- Training large neural networks on massive datasets exposed significant parallelism that could be readily utilized by existing parallel chips such as GPUs. (forbes.com)
- Computer vision algorithms have a leg up in locality due to their heavy use of convolutional neural networks, but the recurrent neural networks used in speech and language applications will need some changes to improve locality, especially for inference. (forbes.com)
- Another possible direction is to merge ideas from convolutional and recurrent neural networks, but the best approach is yet to be seen. (forbes.com)
- You will: - Understand how to build and train Recurrent Neural Networks (RNNs), and commonly-used variants such as GRUs and LSTMs. (coursera.org)
- 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)
- The sensor networks can be used for various application areas (e.g., health, military, home). (psu.edu)
- Using neural networks to automatically generate text is appealing because they can be trained through examples with no need to manually specify what should be said when. (slideshare.net)
- In this talk, we will provide an overview of the existing algorithms used in neural text generation, such as sequence2sequence models, reinforcement learning, variational methods, and generative adversarial networks. (slideshare.net)
- convert points in that meaning space into language (proto-write) This method uses neural networks, so we call it neural text generation. (slideshare.net)
- A Random Walk Through EMNLP 2017 http://approximatelycorrect.com/2017/09/26/a-random-walk-through-emnlp-2017/ Deep Visual-Semantic Alignments for Generating Image Descriptions http://cs.stanford.edu/people/karpathy/deepimagesent/ The beautiful: as our neural networks get richer, the meaning space will get closer to being able to represent the concepts a small child can understand, and we will get closer to human-level literacy. (slideshare.net)
- The papers are organized in topical sections on neural networks and their applications, fuzzy systems and their applications, evolutionary algorithms and their applications, classification, rule discovery and clustering, image analysis, speech and robotics, bioinformatics and medical applications, various problems of artificial intelligence, and agent systems. (barnesandnoble.com)
- Input Signals Normalization in Kohonen Neural Networks. (barnesandnoble.com)
- WWW-Newsgroup-Document Clustering by Means of Dynamic Self-organizing Neural Networks. (barnesandnoble.com)
- Municipal Creditworthiness Modelling by Kohonen's Self-organizing Feature Maps and LVQ Neural Networks. (barnesandnoble.com)
- Fast and Robust Way of Learning the Fourier Series Neural Networks on the Basis of Multidimensional Discrete Fourier Transform. (barnesandnoble.com)
- Ensemble of Dipolar Neural Networks in Application to Survival Data. (barnesandnoble.com)
- Maximum of Marginal Likelihood Criterion instead of Cross-Validation for Designing of Artificial Neural Networks. (barnesandnoble.com)
- The complete back propagation (BP) algorithm tuning equations used to tune the antecedent and consequent parameters for the interval type-2 fuzzy neural networks (IT2FNNs) are developed to handle the training data corrupted by noise or rule uncertainties for nonlinear system identification involving external disturbances. (igi-global.com)
- Simulation results are obtained for the identification of nonlinear system, which yield more improved performance than those using recurrent type-1 fuzzy neural networks (RT1FNNs). (igi-global.com)

###### artificial neural

- Artificial neural network models for indoor temperature prediction: investigations in two buildings. (rwth-aachen.de)
- Hybrid artificial neural network. (rwth-aachen.de)

###### Probabilistic Neural Network

- Controlling a robot manipulator with fuzzy voice commands using a probabilistic neural network. (rwth-aachen.de)

###### 2016

- 5. 5 (Slides by Marc Bolaños) Pingbo Pan, Zhongwen Xu, Yi Yang,Fei Wu,Yueting Zhuang Hierarchical Recurrent Neural Encoder for Video Representation with Application to Captioning, CVPR 2016. (slideshare.net)

###### Computation

- From different perspectives, neural computation provides an alternative methodology to understand brain functions and cognitive process and to solve challenging real-world problems effectively. (springer.com)
- Trend in Neural Computation includes twenty chapters either contributed from leading experts or formed by extending well selected papers presented in the 2005 International Conference on Natural Computation. (springer.com)
- Researchers, graduate students and industrial practitioners in the broad areas of neural computation would benefit from the state-of-the-art work collected in this book. (springer.com)
- Siegelmann, H.T. 2000 'Finite versus infinite neural computation' Calude, C.S. Paun, G. eds. (springer.com)

###### mapping a neural network

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

###### fuzzy

- Application of a fuzzy time series whose values are linguistic values, can overcome the mentioned weakness of traditional forecasting methods. (springer.com)
- Fuzzy Systems and Their Applications. (barnesandnoble.com)
- An Application of Weighted Triangular Norms to Complexity Reduction of Neuro-fuzzy Systems. (barnesandnoble.com)
- Imprecision Measures for Type-2 Fuzzy Sets: Applications to Linguistic Summarization ofDatabases. (barnesandnoble.com)
- International Journal of Fuzzy System Applications (IJFSA), 1 (3), 66-85. (igi-global.com)
- A fuzzy model, recurrent interval type-2 fuzzy neural network (RIT2FNN), is constructed by using a recurrent neural network which recurrent weights, mean and standard deviation of the membership functions are updated. (igi-global.com)
- In the past decades, fuzzy sets and their associated fuzzy logic have supplanted conventional technologies in many scientific applications and engineering systems, especially in control systems, pattern recognition and system identification. (igi-global.com)

###### Feedforward

- A new neural network architecture involving either local feedforward global feedforward, and/or local recurrent global feedforward structure is proposed. (semanticscholar.org)

###### hierarchical

- A hierarchical neural model with time windows in long-term electrical load forecasting. (rwth-aachen.de)

###### algorithms

- It will discuss sources of parallelism and locality in scientific applications, common parallel algorithms used in large-scale simulations, and fundamental performance bottlenecks in scientific codes. (toronto.edu)
- Thanks to deep learning, sequence algorithms are working far better than just two years ago, and this is enabling numerous exciting applications in speech recognition, music synthesis, chatbots, machine translation, natural language understanding, and many others. (coursera.org)

###### predictive control

- Constrained multi-variable generalized predictive control using a dual neural network. (rwth-aachen.de)
- Efficient Predictive Control Integrated with Economic Optimisation Based on Neural Models. (barnesandnoble.com)

###### backpropagation

- Charting the behavioural state of a person using a backpropagation neural network. (rwth-aachen.de)

###### network based

- Frolov, A.A., Husek, D., Polyakov, P.Y.: Recurrent neural network based Boolean factor analysis and its application to automatic terms and documents categorization. (springer.com)
- Transferring neural network based knowledge into an exemplar-based learner. (rwth-aachen.de)

###### weights

- An important question in neuroevolution is how to gain an advantage from evolving neural network topologies along with weights. (psu.edu)

###### models

- Be able to apply sequence models to audio applications, including speech recognition and music synthesis. (coursera.org)

###### control

- Applications of ultrasound in analysis, processing and quality control of food: A review. (ac.ir)

###### Recognition

- Speech recognition applications include voice user interfaces such as voice dialing (e.g. (kiwix.org)

###### Evaluation

- Evaluation of neural network performance and generalisation using thresholding functions. (rwth-aachen.de)

###### paper

- This choice is application-dependent: in this paper, information related to buildings from the SAR image has been selected, since SAR point-targets strongly characterize urban areas. (peerevaluation.org)

###### Reliability

- Neural Network Device for Reliability and Functional Analysis of Discrete Transport System. (barnesandnoble.com)

###### LEARNING

- Brain-machine interfaces are a unique tool for studying learning, thanks to the direct mapping between neural activity and reward. (berkeley.edu)
- Using evolution to improve neural network learning: pitfalls and solutions. (rwth-aachen.de)
- Parallel Realisation of the Recurrent RTRN Neural Network Learning. (barnesandnoble.com)