[PDF] Top 20 Learning to Adaptively Scale Recurrent Neural Networks
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Learning to Adaptively Scale Recurrent Neural Networks
... different scale levels require distinct frequencies to update, they do not always stick to a certain scale and could vary at different time ...models scale patterns through gate struc- tures (Neil, ... See full document
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Multi Module Recurrent Neural Networks with Transfer Learning
... in some vector space, but also to training full mod- els that solve some non-trivial sequential problem, in order to apply them later to another one. Our approach is similar to Conneau et al. (2017) where authors ... See full document
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Reinforcement learning in a large-scale photonic recurrent neural network
... The final step to information processing is to adjust the system such that it performs the desired computation, typically achieved by modifying connection weights according to some learning rou- tine. Inspired by ... See full document
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Gao, Huaien (2009): Distributed learning in sensor networks: an online-trained spiral recurrent neural network, guided by an evolution framework, making duty-cycle reduction more robust. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik
... off-line learning in many ...general recurrent layer structure and theoretically the most suitable structure for dynamics modeling, but it suffers from instability as shown in the simulations with Spike21 ... See full document
183
Image Captioning using Multimodal Embedding
... Convolutional Neural Networks over image regions, bidirectional Recurrent Neural Networks over sentences, and a structured objective that aligns the two modalities through multimodal ... See full document
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Assessing the Corpus Size vs Similarity Trade off for Word Embeddings in Clinical NLP
... deep learning methods in NLP has resulted in a significant num- ber of uses of embeddings to represent ...deep learning models: these models excel with low-dimensional, continuous representations, but offer ... See full document
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The Rise of Deep Learning in Radiology: An Overview of Recent Research
... deep learning techniques in the field of ...deep learning has pervaded every field and the deep learning revolution has opened up new frontiers in artificial ...deep learning techniques are ... See full document
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GROUP OF RECURRENT NEURAL NETWORKS
... cascade-correlation learning tends to produce hidden units that saturate and thus makes it more suitable for classification tasks instead of regression ...on- learning definition of the number of hidden ... See full document
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GROUP OF RECURRENT NEURAL NETWORKS
... Feed – Forward Back propagation neural network (FFBPNN) [14] and Cascade Forward Back propagation neural network (CFBPNN) [15] shown in Figs. [1] are used in this work. A FFBPNN and CFBPNN consists of three ... See full document
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GROUP OF RECURRENT NEURAL NETWORKS
... xiv SRP (Secure Routing Protocol): It is an on demand[50] routing protocol. It can discover all possible paths between two nodes. The sole assumption of the protocol is that at the beginning, all the nodes share a group ... See full document
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Hopfield Neural Networks for Aircrafts’ Enroute Sectoring: KRISHAN-HOPES
... artificial neural networks are biologically ...after learning. Or we can say that artificial neural networks perform computational tasks by modeling the human brain ...the neural ... See full document
8
Deep Learning Based Visual Tracking: A Review
... first neural-network tracker that combines convolutional and recurrent networks with RL algorithm in ...reinforcement learning (RL) agent making target location ... See full document
5
Creating building energy prediction models with convolutional recurrent neural networks
... Being able to create accurate building energy predictions models can allow for more efficient energy production and save resources. Creating accurate building energy predic- tion models is a difficult problem, there are ... See full document
10
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing
... deep learning, recurrent neural networks, probabilistic learning algorithms, natural language processing and manifold ...machine learning and neural ...Statistical ... See full document
76
Scalable Bayesian Learning of Recurrent Neural Networks for Language Modeling
... to recurrent layers, where the same dropout masks are shared along time for encoding, decoding and recurrent weights, respec- ...on recurrent layers, enhancing ...3 Recurrent Neural ... See full document
11
Unified Framework For Deep Learning Based Text Classification
... Deep learning models are based on artificial neural networks, which are inspired by biological brain model made of ...deep learning architecture has three components namely input variables, ... See full document
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Cascade recurring deep networks for audible range prediction
... of neural network that can be applied to signal data in which output variables are closely correlated with each ...of neural networks with many output variables, learning of weight w is ... See full document
10
Impact of Earnings per Share on Market Price of Share with Special Reference to Selected Companies Listed on NSE
... as neural coding which attempts to define a relationship between various stimuli and associated neuronal responses in the ...deep learning architectures such as deep neural networks, ... See full document
5
Deep Neural Models for Medical Concept Normalization in User Generated Texts
... sequence learning problem with powerful neural networks such as recurrent neural networks and contextual- ized word representation models trained to ob- tain semantic ... See full document
7
Statistical Script Learning with Recurrent Neural Networks
... Table 2 gives a subset of results from Pichotta and Mooney (2016b), comparing an event LSTM with a text LSTM. The “Copy/paste” baseline determin- istically predicts a sentence as its own successor. The “Accuracy” metric ... See full document
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