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Attention networks, a deep neural network architecture inspired by humans' attention mechanism, have seen significant success in image captioning, machine translation, and many other applications.
Nodetrix: a hybrid visualization of social networks
N. H. Riche, J.-D. Fekete, and M. J. McGuffin · 2007
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ACL 2014 Ninth Workshop on Statistical Machine Translation
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Neural machine translation by jointly learning to align and translate
D. Bahdanau, K. Cho, and Y. Bengio · 2014
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The stanford corenlp natural language processing toolkit
C. Manning, M. Surdeanu, J. Bauer, J. Finkel, S. Bethard, and D. McClosky · 2014
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Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2014
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Small multipiles: Piling time to explore temporal patterns in dynamic networks
B. Bach, N. H. Riche, T. Dwyer, T. M. Madhyastha, J.-D. Fekete, and T. J. Grabowski · 2015
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Effective approaches to attention-based neural machine translation
T. Luong, H. Pham, and C. D. Manning · 2015
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Show, attend and tell: Neural image caption generation with visual attention
K. Xu, J. Ba, R. Kiros, K. Cho, A. Courville, R. Salakhudinov, R. Zemel, and Y. Bengio · 2015
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Revacnn: Steering convolutional neural network via real-time visual analytics
S. Chung, C. Park, S. Suh, K. Kang, J. Choo, and B. C. Kwon · 2016
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Towards better analysis of deep convolutional neural networks
M. Liu, J. Shi, Y. Li, C. Li, J. Zhu, and S. Liu · 2016
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Opennmt: Open-source toolkit for neural machine translation
G. Klein, Y. Kim, Y. Deng, J. Senellart, and A. M. Rush · 2017
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Understanding hidden memories of recurrent neural networks
Y. Ming, S. Cao, R. Zhang, Z. Li, Y. Chen, Y. Song, and H. Qu · 2017
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Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. u. Kaiser, and I. Polosukhin · 2017
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Do convolutional neural networks learn class hierarchy?
B. Alsallakh, A. Jourabloo, M. Ye, X. Liu, and L. Ren · 2018
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RNNbow: Visualizing Learning Via Backpropagation Gradients in RNNs
D. Cashman, G. Patterson, A. Mosca, N. Watts, S. Robinson, and R. Chang · 2018
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Extreme adaptation for personalized neural machine translation
P. Michel and G. Neubig · 2018
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Deepeyes: Progressive visual analytics for designing deep neural networks
N. Pezzotti, T. Höllt, J. V. Gemert, B. P. F. Lelieveldt, E. Eisemann, and A. Vilanova · 2018
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Seq2seq-vis: A visual debugging tool for sequence-to-sequence models
H. Strobelt, S. Gehrmann, M. Behrisch, A. Perer, H. Pfister, and A. M. Rush · 2018
What does bert look at? an analysis of bert’s attention
K. Clark, U. Khandelwal, O. Levy, and C. D. Manning · 2019
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BERT: pre-training of deep bidirectional transformers for language understanding
J. Devlin, M. Chang, K. Lee, and K. Toutanova · 2019
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GAN Lab: Understanding Complex Deep Generative Models using Interactive Visual Experimentation
M. Kahng, N. Thorat, D. H. Chau, F. Viégas, and M. Wattenberg · 2019
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RetainVis: Visual Analytics with Interpretable and Interactive Recurrent Neural Networks on Electronic Medical Records
B. C. Kwon, M. Choi, J. T. Kim, E. Choi, Y. B. Kim, S. Kwon, J. Sun, and J. Choo · 2019
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DeepTracker: Visualizing the Training Process of Convolutional Neural Networks
D. Liu, W. Cui, K. Jin, Y. Guo, and H. Qu · 2019
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Lstmvis: A tool for visual analysis of hidden state dynamics in recurrent neural networks
H. Strobelt, S. Gehrmann, H. Pfister, and A. M. Rush · 2018
Cited alongside, same era.
Linguistically-informed self-attention for semantic role labeling
E. Strubell, P. Verga, D. Andor, D. Weiss, and A. McCallum · 2018
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Ganviz: A visual analytics approach to understand the adversarial game
J. Wang, L. Gou, H. Yang, and H.-W. Shen · 2018
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Non-local neural networks
X. Wang, R. B. Girshick, A. Gupta, and K. He · 2018
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Self-attention generative adversarial networks
H. Zhang, I. J. Goodfellow, D. N. Metaxas, and A. Odena · 2018
Cited alongside, same era.
The effectiveness of lloyd-type methods for the k-means problem
R. Ostrovsky, Y. Rabani, L. J. Schulman, and C. Swamy
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Language models are unsupervised multitask learners
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, and I. Sutskever · 2019
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SEQ2seq-VIS : A Visual Debugging Tool for Sequence-to-Sequence Models
H. Strobelt, S. Gehrmann, M. Behrisch, A. Perer, H. Pfister, and A. M. Rush · 2019
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Analyzing the structure of attention in a transformer language model
J. Vig and Y. Belinkov · 2019
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Analyzing multi-head self-attention: Specialized heads do the heavy lifting, the rest can be pruned
E. Voita, D. Talbot, F. Moiseev, R. Sennrich, and I. Titov · 2019
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Dqnviz: A visual analytics approach to understand deep q-networks
J. Wang, L. Gou, H. Shen, and H. Yang · 2019
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Xlnet: Generalized autoregressive pretraining for language understanding
Z. Yang, Z. Dai, Y. Yang, J. G. Carbonell, R. Salakhutdinov, and Q. V. Le · 2019
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