[PDF] Top 20 Convolutional Self Attention Networks
Has 10000 "Convolutional Self Attention Networks" found on our website. Below are the top 20 most common "Convolutional Self Attention Networks".
Convolutional Self Attention Networks
... Self-attention networks (SANs) have drawn increasing interest due to their high paral- lelization in computation and flexibility in modeling ...multi-head attention by allowing the model to ... See full document
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Leveraging Local and Global Patterns for Self Attention Networks
... Self-attention networks (SANs) (Parikh et al., 2016; Lin et al., 2017) have shown promising re- sults for a range of NLP tasks, including machine translation (Vaswani et al., 2017), contextualized ... See full document
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Comparing Attention Based Convolutional and Recurrent Neural Networks: Success and Limitations in Machine Reading Comprehension
... Current state-of-the-art deep learning (DL) mod- els outperform other techniques in many tasks including computer vision (Krizhevsky et al., 2012), speech recognition (Hinton et al., 2012) and more recently natural ... See full document
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Bridging the Gap: Attending to Discontinuity in Identification of Multiword Expressions
... We introduce a new method to tag Multiword Expressions (MWEs) using a linguistically in- terpretable language-independent deep learn- ing architecture. We specifically target dis- continuity, an under-explored aspect ... See full document
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The Application of Neural Network in Multiple Object Tracking
... neural networks in MOT, such as feature extraction, model formulation of both appearance and ...neural networks has shown competitive superiority and drawn wide attention from ...of ... See full document
7
A Convolutional Encoder Model for Neural Machine Translation
... Neural machine translation (NMT) is an end-to-end approach to machine translation (Sutskever et al., 2014). The most successful approach to date en- codes the source sentence with a bi-directional re- current neural ... See full document
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Cross Target Stance Classification with Self Attention Networks
... Second, our model is shown to achieve better results than the two baselines in almost all cases (only slightly worse than MITRE on LA under the in-target setting, and the difference is not statis- tically significant), ... See full document
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Constructive Type Logical Supertagging With Self Attention Networks
... of self- attention networks towards grammar induc- ...an attention-based supertag- ger for a refined type-logical grammar, trained on constructing types ... See full document
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Topic Spotting using Hierarchical Networks with Self Attention
... neural networks (Hochreiter and Schmidhuber, 1997) or CNN based neural net- works (Kim, 2014) for learning representation of ...archical Attention Network (HN-ATT) model ... See full document
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Convolutional Attention Networks for Multimodal Emotion Recognition from Speech and Text Data
... the attention matrix requires matrix multiplication between embedded word and spectrogram feature for a given ...calculated attention matrix through temporal embedding, and to the resulting atten- tion ... See full document
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Automatic drum transcription for polyphonic recordings using soft attention mechanisms and convolutional neural networks
... soft attention mechanisms (SA) and an alternative RNN configuration containing ad- ditional peripheral connections ...a convolutional neural network (CNN), which uses a larger set of time-step ... See full document
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Assessing the Ability of Self Attention Networks to Learn Word Order
... Self-attention networks (SAN) have attracted a lot of interests due to their high parallelization and strong performance on a variety of NLP tasks, ...neural networks (RNN), SAN is ascribed to ... See full document
10
CKY based Convolutional Attention for Neural Machine Translation
... neural networks (NNs) is known to provide both high-precision and human-like translation through its simple ...An attention-based NMT (ANMT) is one of the state-of-the-art technologies for MT, which is an ... See full document
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Convolutional Neural Networks Analyzed via Convolutional Sparse Coding
... Deep learning (LeCun et al., 2015), and in particular CNN (LeCun et al., 1990, 1998; Krizhevsky et al., 2012), has gained a copious amount of attention in recent years as it has led to many state-of-the-art ... See full document
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Self-Ensembling Attention Networks: Addressing Domain Shift for Semantic Segmentation
... propose self-ensembling attention networks to address domain shift for semantic ...generate attention-aware features, which are further utilized to guide the calculation of consistency loss in ... See full document
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Speech Emotion Recognition Using Convolutional Recurrent Neural Networks with Attention Model
... a Convolutional Neural Network is firstly applied to the two-dimensional spectral representation of the audio signal to extract emotion-relevant features from short term ...Neural Networks (BRNN) are ... See full document
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Phrase level Self Attention Networks for Universal Sentence Encoding
... ing. A phrase is a group of words that carry a specific idiomatic meaning and function as a con- stituent in the syntax of a sentence. Words in a phrase are syntactically and semantically related to each other. ... See full document
10
Gabor Convolutional Networks
... deep convolutional neural networks (DCNNs) based on convolution filters have attracted significant attention in computer vision due to the amazing capability of learning powerful feature ... See full document
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Attention module based spatial temporal graph convolutional networks for skeleton based action recognition
... The spatial temporal graph convolutional networks ST-GCN automatically learn both the temporal and spatial features from the skeleton data, and achieve remarkable performance for skeleto[r] ... See full document
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Modeling Localness for Self Attention Networks
... of self- attention to neural machine ...the self-attention model with directional masks and multi-dimensional ...standard self-attention model can give more bias toward ... See full document
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