Results 11 to 20 of about 5,906,664 (269)
Convolutional Self-Attention Networks [PDF]
Self-attention network (SAN) has recently attracted increasing interest due to its fully parallelized computation and flexibility in modeling dependencies. It can be further enhanced with multi-headed attention mechanism by allowing the model to jointly attend to information from different representation subspaces at different positions (Vaswani et al.,
Baosong Yang +4 more
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Self-Attentional Acoustic Models [PDF]
Published at Interspeech ...
Matthias Sperber +4 more
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Lightweight Self-Attentive Sequential Recommendation [PDF]
Modern deep neural networks (DNNs) have greatly facilitated the development of sequential recommender systems by achieving state-of-the-art recommendation performance on various sequential recommendation tasks. Given a sequence of interacted items, existing DNN-based sequential recommenders commonly embed each item into a unique vector to support ...
Yang Li 0140 +3 more
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On the Robustness of Self-Attentive Models [PDF]
This work examines the robustness of self-attentive neural networks against adversarial input perturbations. Specifically, we investigate the attention and feature extraction mechanisms of state-of-the-art recurrent neural networks and self-attentive architectures for sentiment analysis, entailment and machine translation under adversarial attacks.
Yu-Lun Hsieh +5 more
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On The Computational Complexity of Self-Attention
Transformer architectures have led to remarkable progress in many state-of-art applications. However, despite their successes, modern transformers rely on the self-attention mechanism, whose time- and space-complexity is quadratic in the length of the input.
Feyza Duman Keles +2 more
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Self-Attentive Sequential Recommendation [PDF]
Accepted by ICDM'18 as a long ...
Wang-Cheng Kang, Julian J. McAuley
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Switchable Self-attention Module
Attention mechanism has gained great success in vision recognition. Many works are devoted to improving the effectiveness of attention mechanism, which finely design the structure of the attention operator. These works need lots of experiments to pick out the optimal settings when scenarios change, which consumes a lot of time and computational ...
ShanShan Zhong, Wushao Wen, Jinghui Qin
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Self-Attentive Associative Memory
Heretofore, neural networks with external memory are restricted to single memory with lossy representations of memory interactions. A rich representation of relationships between memory pieces urges a high-order and segregated relational memory.
Hung Le 0002 +2 more
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The Lipschitz Constant of Self-Attention
Lipschitz constants of neural networks have been explored in various contexts in deep learning, such as provable adversarial robustness, estimating Wasserstein distance, stabilising training of GANs, and formulating invertible neural networks. Such works have focused on bounding the Lipschitz constant of fully connected or convolutional networks ...
Hyunjik Kim +2 more
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On the Integration of Self-Attention and Convolution
Accepted to ...
Xuran Pan +6 more
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