Non-recurrent architectures for temporal processing
Discovering gated recurrent neural network architectures
... 2.1 Vanishing Gradients in Recurrent Neural Networks One major limitation of RNNs is that they are not able to maintain contexts for longer time sequences. This occurs because of two reasons. First, the memory ...
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Spatio-temporal Clustering for Non-Recurrent Traffic Congestion Detection on Urban Road Networks
... 89 of an incident being detected is higher. Secondly, traffic activity in Westminster is higher than most other regions due to its touristic, political and economic importance, which increases the likelihood of an ...
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Spatio-temporal clustering for non-recurrent traffic congestion detection on urban road networks
... Therefore, research is required to gain a better understanding of the causes of NRC events and how they are related to inci- dents. Such an understanding will provide valuable information for traffic operation centres as ...
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An Investigation of Recurrent Neural Architectures for Drug Name Recognition
... Geoffrey Hinton, Li Deng, Dong Yu, George E Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew Se- nior, Vincent Vanhoucke, Patrick Nguyen, Tara N Sainath, et al. 2012. Deep neural networks for acous- tic modeling in ...
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Recurrent Neural Network Architectures Toward Intrusion Detection
... 3.2 Recurrent Neural Network (RNN) In this research RNN was selected due to its powerful features to learn from previously data, then adapt its response to ...2) Non- linear dynamics that allow them to ...
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Deep Architectures for Speech Processing: Survey
... Recurrent neural network (RNNs) can be regarded as a class of deep generative architectures. RNNs are powerful in modeling sequential data of speech or text format. Depth of RNN can be as large as the ...
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Learning and Representing Temporal Knowledge in Recurrent Networks
... A. System Implementation We have implemented the above algorithms as part of a unified neural-symbolic system. The system allows the translation of SCTL knowledge into NARX networks, learning from examples and ...
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Learning and Representing Temporal Knowledge in Recurrent Networks
... A. System Implementation We have implemented the above algorithms as part of a unified neural-symbolic system. The system allows the translation of SCTL knowledge into NARX networks, learning from examples and ...
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The Recurrent Temporal Discriminative Restricted Boltzmann Machines
... Modelling sequences is an important research topic with various applications in audio/music infor- matics, natural language processing and computer vision. While some work focus on synthesising time-series events ...
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On the induction of temporal structure by recurrent neural networks
... Nevertheless, nowadays, NLP systems are a key area of interest in the field of connectionism and much work has been conducted on how linguistic representatio ns and descriptions can be used for processing. That is ...
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Long Short Term Memory Recurrent Neural Network Architectures
... NEURAL specification 1. Fully perennial Neural Network Fully perennial neural network (FRNN) developed one of the Eighties, which can learn temporal sequences, either in batch mode or on-line. FRNN includes two ...
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Recurrent spatio-temporal structures in presence of continuous symmetries
... until it starts folding back to itself. Parameterizing the intersection of a manifold with the Poincar´e section by Euclidean length along it, a forward map from section to section will be constructed and convolution of ...
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The Diagnostic Accuracy of Non Echo Planar Diffusion Weighted Imaging in the Detection of Residual and/or Recurrent Cholesteatoma of the Temporal Bone
... time, non-EPI DWI is becoming an alterna- tive to invasive second-look ...and/or recurrent cholesteatomas after primary cholesteatoma surgery are very accurately detected by in- creased DW signal intensity ...
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Improving String Processing for Temporal Relations
... ↵ i or ↵ i+1 are deleted. For example, the string { a }{ a }{ a, b }{ b }{ b } is equivalent in interpretation to { a }{ a, b }{ b }. A string featuring these repetitions is said to stutter, and the process of removing ...
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7 Architectures and Implementations of Spatio-temporal Database Management Systems
... The following subsections present these variants in more detail. 7.2.1 The Layered Architecture A traditional way of designing an information system for advanced data types and operations is to use an off-the-shelf DBMS ...
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EVALUATION OF MULTI-CORE ARCHITECTURES FOR IMAGE PROCESSING ALGORITHMS
... mid level. Each tracking iteration constructs a linear system of equations in two unknowns for each interest point and directly solves them to update the estimated displacement (see 3.3). The first thread bilinearly ...
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Designing Regularizers and Architectures for Recurrent Neural Networks
... on recurrent neural networks, one of the most popular and powerful families of deep learning models, and the subject of my highlighted research contribu- tions: norm-stabilization, a successful and novel approach ...
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Sequence Processing with Recurrent Networks
... • Recurrent neural language models process sequences one word at a time, as seen in the previous slides. • This means that they avoid constraining the context size[r] ...
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Stream Processing on Demand for Lambda Architectures
... stream processing technologies in the Apache Hadoop environment – Summingbird 2 is an open source library to write algorithms that can be used for batch as well as stream ...
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Hardware Architectures for Image Processing Acceleration
... image processing field. Even though many useful image processing algorithms can be described quite compactly with few operations, these operations must be repeated over large amounts of data and usually ...
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