time representation in neural networks
Representation Learning With Convolutional Neural Networks
... Discussion In this study, we proposed a novel CNN-based method, DCNN-CL-ATT, for evalu- ating how deep learning of in-vivo DTI trajectory can accurately detect eloquent functional areas determined by the gold starndard ...
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Knowledge Representation and Reasoning with Deep Neural Networks
... test time are never seen during training, so learning requires adaptation at test time to unseen column ...Standard neural network models like sequence-to-sequence and pointer networks do not ...
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Theory of representation learning in cortical neural networks
... a representation from complex high-dimensional inputs typically involves tuning a large number of parameters and requires vast amounts of ...convolutional neural networks and computer vision, but a ...
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Time Series Prediction and Neural Networks
... Neural Networks have been widely used as time series forecasters: most often these are feed-forward networks which employ a sliding window over the input ...feed-forward neural network ...
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Injecting Relational Structural Representation in Neural Networks for Question Similarity
... Finally, in the last row of the table, we report the result of a voting approach using a combi- nation of the normalized scores of TK-10k and CNN-10k. The accuracy is almost the same than CNN(TK-10k)*. This shows that ...
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The representation of object drawings and sketches in deep convolutional neural networks
... convolutional neural networks trained on large databases of natural images have recently gained popularity in visual neuroscience [4], due to their - at times - close correspondence to brain activity in ...
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Discourse Representation Structure Parsing with Recurrent Neural Networks and the Transformer Model
... 4 Conclusions In this paper, we describe the system for the IWCS-2019 Shared Task of DRS parsing. We found that the character-level LSTM and the word-level transformer are competitive in the task. The training ...
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Graphical representation of data for a multiprocessor platform emulating spiking neural networks
... The tools developed so far are useful for debugging by simulation, but in order to evaluate the behavior of SNN being emulated, two needs arose: real- time monitoring of the network evolution and a higher-level, ...
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Representation of features as images with neighborhood dependencies for compatibility with convolutional neural networks
... the time complexity of the two approaches, we selected one cell line with a fixed CNN architecture and de fi ned a grid for hyperparameters ...the time complexity and similar performance of the two ...
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Hybrid Neural Networks for Learning the Trend in Time Series
... in time series data play an important role in many real applica- tions, ranging from resource allocation in data cen- ters, load schedule in smart grid, and so ...of neural networks, in this paper we ...
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Multi-Channel Distributed Representation for Classifying Tweets by using Convolutional Neural Networks
... data, time series numeric data, and multimodal ...multiple-layer neural network that consists of convolutional layers, pooling layers, and fully connected layers as hidden ...
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From Line Drawings to Human Actions: Deep Neural Networks for Visual Data Representation
... four time points relative to the contact point described above and was constructed from the same hand patches used in the computational ...all time offsets were equally ...
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Multi task Attention based Neural Networks for Implicit Discourse Relationship Representation and Identification
... attention-based neural network to address implic- it discourse relationship representation and recog- ...of representation learning at the same ...one neural network framework and work ...
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Color Representation in Deep Neural Networks
... deep neural networks have achieved state-of-the-art performance on many and various bench- ...Convolutional Neural Networks (CNNs) have shown top performance on image classification ...the ...
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Learning Topic Representation for SMT with Neural Networks
... Another important factor is the length of hid- den layers L in the network. In deep learning, this parameter is often empirically tuned with human efforts. As shown in Figure 3, the translation accu- racy is better when ...
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Representation of Linguistic Form and Function in Recurrent Neural Networks
... recurrent neural models rely on a so-called attention mechanism, first introduced by Bahdanau, Cho, and Bengio (2015) under the name of soft ...these networks attention is explicitly represented, and it ...
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Neural Networks and Prediction of Time Series
... Testovány a srovnány byly výsledky měření počtu neuronů skryté vrstvy, počtu vstupů sítě, velikosti koeficientu učení, počtu skrytých vrstev a počtu skrytých neuronů při použití diferenc[r] ...
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Neural Networks For Financial Time Series
... being neural networks. Neural networks is one of the main and effective methods which has been and can be used in this finance analysis to predict cash ...
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Bridge Correlational Neural Networks for Multilingual Multimodal Representation Learning
... Existing approaches to common representation learning (Ngiam et al., 2011; Klementiev et al., 2012; Chandar et al., 2013; Chandar et al., 2014; Andrew et al., 2013; Wang et al., 2015) except (Her- mann and ...
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Fine-grained Visual Representation Learning with Deep Neural Networks
... 5.3 Discussion To the best of our knowledge, our proposed SegAN is the first GAN-inspired frame- work adapted specifically for the segmentation task that produces superior segmen- tation accuracy. While there are very ...
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