fast learning neural networks
Fast image recognition of transmission tower based on big data
... in fast image recognition of transmission towers which are obtained using fixed-wing unmanned aerial vehicle (UAV) by large range tilt photography are ...using fast region-based convolutional neural ...
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Convolutional Neural Networks and Hash Learning for Feature Extraction and of Fast Retrieval of Pulmonary Nodules
... Abstract. With a widespread use of digital imaging data in hospitals, the size of medical image repositories is increasing rapidly. This causes difficulty in managing and querying these large databases leading to the ...
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A Very Fast Learning Method for Neural Networks Based on Sensitivity Analysis
... a learning method for two-layer feedforward neural networks based on sen- sitivity analysis, which uses a linear training algorithm for each of the two ...Linear Learning Method, can also be ...
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A Fast and Accurate Dependency Parser using Neural Networks
... of learning a neural network classifier for use in a greedy, transition-based depen- dency ...very fast, while achiev- ing an about 2% improvement in unla- beled and labeled attachment scores on both ...
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Fast relational learning using bottom clause propositionalization with artificial neural networks
... Relational learning can be described as the task of learning first-order logic rules from ...machine learning applications, ...relational learning either directly by manipulating first-order ...
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Fast Adaptation of Neural Networks
... Prototypical Networks and Model-Agnostic Meta-Learning (MAML) that enables ma- chines to learn to recognize new objects with very little supervision from the ...few-shot learning scenario, where the ...
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Deep Learning as a Frontier of Machine Learning: A Review
... through learning from the lower level by exploiting the hierarchical exploratory ...deep learning methods avoid feature engineering in supervised learning ...unsupervised learning where ...
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Fast and Accurate Preordering for SMT using Neural Networks
... There is a strong research and commercial in- terest in preordering, as reflected by the exten- sive previous work on the subject (Collins et al., 2005; Xu et al., 2009; DeNero and Uszkor- eit, 2011; Neubig et al., ...
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Fast Homomorphic Evaluation of Deep Discretized Neural Networks
... Most interestingly, our FHE–DiNN framework is flexible and can be adapted to more generic cognitive architectures: we leave this as an interesting open prob- lem. In particular, excellent results have been obtained by ...
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Deep Belief Networks Using Convolution Neural Networks Algorithm
... deep learning is not new to higher educatio n. However, deep learning has drawn more attention in recent years as institutions attempt to tap their student’s full learning ...a learning model, ...
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A STUDY ON USING DEEP LEARNING TECHNOLOGIES IN CONVOLUTIONAL NEURAL NETWORKS FOR MULTIPLE OBJECTS IDENTIFICATION
... and fast RCNN is that the RCNN employs selective searching algorithms to initiate the pro- posed regions while faster RCNN uses RPN to initiate the proposed ...
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Collaborative Multi Agent Dialogue Model Training Via Reinforcement Learning
... (NLG) neural networks for each agent and then use multi-agent reinforcement learning, namely the Win or Lose Fast Policy Hill Climbing (WoLF-PHC) algorithm (Bowling and Veloso, 2001), to learn ...
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Modal Learning Neural Networks
... The E-learning system has been designed and built using the JavaServer Faces Technology (JSF), which is a component-based web application framework that enables rapid development. The JSF follows the Model- ...
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Fast and Efficient Asynchronous Neural Computation with Adapting Spiking Neural Networks
... To explain how real neurons are able to efficiently encode a signal with few spikes, alternative spike- based neural coding schemes are being considered. A recent line of work in computational neuroscience ...
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Application of Convolutional Neural Networks for Automated Ulcer Detection in Wireless Capsule Endoscopy Images.
... deep learning algorithms. This indicated that deep learning has covered almost every aspect of medical image ...pre-trained networks were used as feature extractors and that various CNN architectures ...
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Training Algorithms for Supervised Machine Learning: Comparative Study
... supervised learning algorithms used like technique machine that build a decision tree from a set of class labeled training sample during the machine learning ...
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Comparative Study of different methods used for GPS GDOP Approximation
... 30000 learning iterations. The learning rate is chosen as ...30000 learning iterations, the neural network method shows better GDOP accuracy than the matrix inversion and closed loop ...
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Structured prediction and generative modeling using neural networks
... LSTM networks to learn accompaniment for blues music, and was a direct precursor to many of the approaches and methods used in this ...LSTM networks and deep belief networks (DBN) with improved ...
107
Distributional Reinforcement Learning with Quantum Neural Networks
... Machine learning is teaching computer models how to learn from ...machine learning, reinforcement learning (RL) aims to learn sequen- tial decision making from data [1] [2] ...Google. Learning ...
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Time Series Forecasting with Long Short-Term Memory Neural Networks on the Stock Market
... Long short-term memory (LSTM) was invented as the answer to the problem of the vanishing gradient problem. It is a type of recurrent neural network which can learn with long-term dependencies without a vanishing ...
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