Transformer模型 (Chinese Wikipedia)

Analysis of information sources in references of the Wikipedia article "Transformer模型" in Chinese language version.

refsWebsite

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12web.archive.org

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9arxiv.org

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6doi.org

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4googleblog.com

1,272^nd place

2,099^th place

2semanticscholar.org

11^th place

332^nd place

2medium.com

551^st place

572^nd place

1towardsdatascience.com

8,920^th place

7,729^th place

1openai.com

1,559^th place

848^th place

1indico.io

low place

1jalammar.github.io

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1aclweb.org

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1coursera.org

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1worldcat.org

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aclweb.org

Clark, Kevin; Khandelwal, Urvashi; Levy, Omer; Manning, Christopher D. What Does BERT Look at? An Analysis of BERT's Attention. Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP (Florence, Italy: Association for Computational Linguistics). August 2019: 276–286 [2022-06-08]. doi:10.18653/v1/W19-4828 . （原始内容存档于2020-10-21）.

arxiv.org

Vaswani, Ashish; Shazeer, Noam; Parmar, Niki; Uszkoreit, Jakob; Jones, Llion; Gomez, Aidan N.; Kaiser, Lukasz; Polosukhin, Illia. Attention Is All You Need. 2017-06-12. arXiv:1706.03762  [cs.CL].
Devlin, Jacob; Chang, Ming-Wei; Lee, Kenton; Toutanova, Kristina. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. 2018-10-11. arXiv:1810.04805v2  [cs.CL].
Zhai. An Attention Free Transformer. arXiv:2105.14103 .
Tay. Long Range Arena: A Benchmark for Efficient Transformers. arXiv:2011.04006 .
Wang, Alex; Singh, Amanpreet; Michael, Julian; Hill, Felix; Levy, Omer; Bowman, Samuel. GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding. Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP (Stroudsburg, PA, USA: Association for Computational Linguistics). 2018: 353–355. S2CID 5034059. arXiv:1804.07461 . doi:10.18653/v1/w18-5446.
Bertasias; Wang; Torresani. Is Space-Time Attention All You Need for Video Understanding?. 2021. arXiv:2102.05095  [cs.CV].
Noever, David; Ciolino, Matt; Kalin, Josh. The Chess Transformer: Mastering Play using Generative Language Models. 2020-08-21. arXiv:2008.04057  [cs.AI].
Dosovitskiy, Alexey; Beyer, Lucas; Kolesnikov, Alexander; Weissenborn, Dirk; Zhai, Xiaohua; Unterthiner, Thomas; Dehghani, Mostafa; Minderer, Matthias; Heigold, Georg; Gelly, Sylvain; Uszkoreit, Jakob; Houlsby, Neil. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. 2020. arXiv:2010.11929  [cs.CV].
Touvron, Hugo; Cord, Matthieu; Douze, Matthijs; Massa, Francisco; Sablayrolles, Alexandre; Jégou, Hervé. Training data-efficient image transformers & distillation through attention. 2020. arXiv:2012.12877  [cs.CV].

coursera.org

Tasks with Long Sequences – Chatbot. Coursera. [2022-06-08]. （原始内容存档于2020-10-26）.

doi.org

Wolf, Thomas; Debut, Lysandre; Sanh, Victor; Chaumond, Julien; Delangue, Clement; Moi, Anthony; Cistac, Pierric; Rault, Tim; Louf, Remi. Transformers: State-of-the-Art Natural Language Processing. 2020: 38–45. doi:10.18653/v1/2020.emnlp-demos.6.
Clark, Kevin; Khandelwal, Urvashi; Levy, Omer; Manning, Christopher D. What Does BERT Look at? An Analysis of BERT's Attention. Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP (Florence, Italy: Association for Computational Linguistics). August 2019: 276–286 [2022-06-08]. doi:10.18653/v1/W19-4828 . （原始内容存档于2020-10-21）.
Wang, Alex; Singh, Amanpreet; Michael, Julian; Hill, Felix; Levy, Omer; Bowman, Samuel. GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding. Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP (Stroudsburg, PA, USA: Association for Computational Linguistics). 2018: 353–355. S2CID 5034059. arXiv:1804.07461 . doi:10.18653/v1/w18-5446.
Rives, Alexander; Goyal, Siddharth. Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. bioRxiv 10.1101/622803 .
Nambiar, Ananthan; Heflin, Maeve; Liu, Simon; Maslov, Sergei; Hopkins, Mark; Ritz, Anna. Transforming the Language of Life: Transformer Neural Networks for Protein Prediction Tasks.. 2020. S2CID 226283020. doi:10.1145/3388440.3412467 .
Rao, Roshan; Bhattacharya, Nicholas. Evaluating Protein Transfer Learning with TAPE. bioRxiv 10.1101/676825 .

googleblog.com

ai.googleblog.com

Open Sourcing BERT: State-of-the-Art Pre-training for Natural Language Processing. Google AI Blog. [2019-11-27]. （原始内容存档于2021-01-13）（英语）.
Open Sourcing BERT: State-of-the-Art Pre-training for Natural Language Processing. Google AI Blog. [2019-08-25]. （原始内容存档于2021-01-13）.
Constructing Transformers For Longer Sequences with Sparse Attention Methods. Google AI Blog. [2021-05-28]. （原始内容存档于2021-09-18）（英语）.
Reformer: The Efficient Transformer. Google AI Blog. [2020-10-22]. （原始内容存档于2020-10-22）（英语）.

indico.io

Sequence Modeling with Neural Networks (Part 2): Attention Models. Indico. 2016-04-18 [2019-10-15]. （原始内容存档于2020-10-21）.

jalammar.github.io

Alammar, Jay. The Illustrated Transformer. jalammar.github.io. [2019-10-15]. （原始内容存档于2020-10-18）.

medium.com

Allard, Maxime. What is a Transformer?. Medium. 2019-07-01 [2019-10-21]. （原始内容存档于2020-10-17）（英语）.
Monsters, Data. 10 Applications of Artificial Neural Networks in Natural Language Processing. Medium. 2017-09-26 [2019-10-21]. （原始内容存档于2020-10-17）（英语）.

openai.com

Better Language Models and Their Implications. OpenAI. 2019-02-14 [2019-08-25]. （原始内容存档于2020-12-19）.

semanticscholar.org

api.semanticscholar.org

Wang, Alex; Singh, Amanpreet; Michael, Julian; Hill, Felix; Levy, Omer; Bowman, Samuel. GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding. Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP (Stroudsburg, PA, USA: Association for Computational Linguistics). 2018: 353–355. S2CID 5034059. arXiv:1804.07461 . doi:10.18653/v1/w18-5446.
Nambiar, Ananthan; Heflin, Maeve; Liu, Simon; Maslov, Sergei; Hopkins, Mark; Ritz, Anna. Transforming the Language of Life: Transformer Neural Networks for Protein Prediction Tasks.. 2020. S2CID 226283020. doi:10.1145/3388440.3412467 .

towardsdatascience.com

He, Cheng. Transformer in CV. Transformer in CV. Towards Data Science. 31 December 2021 [2022-06-08]. （原始内容存档于2023-04-16）.

web.archive.org

He, Cheng. Transformer in CV. Transformer in CV. Towards Data Science. 31 December 2021 [2022-06-08]. （原始内容存档于2023-04-16）.
Open Sourcing BERT: State-of-the-Art Pre-training for Natural Language Processing. Google AI Blog. [2019-11-27]. （原始内容存档于2021-01-13）（英语）.
Open Sourcing BERT: State-of-the-Art Pre-training for Natural Language Processing. Google AI Blog. [2019-08-25]. （原始内容存档于2021-01-13）.
Better Language Models and Their Implications. OpenAI. 2019-02-14 [2019-08-25]. （原始内容存档于2020-12-19）.
Sequence Modeling with Neural Networks (Part 2): Attention Models. Indico. 2016-04-18 [2019-10-15]. （原始内容存档于2020-10-21）.
Alammar, Jay. The Illustrated Transformer. jalammar.github.io. [2019-10-15]. （原始内容存档于2020-10-18）.
Clark, Kevin; Khandelwal, Urvashi; Levy, Omer; Manning, Christopher D. What Does BERT Look at? An Analysis of BERT's Attention. Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP (Florence, Italy: Association for Computational Linguistics). August 2019: 276–286 [2022-06-08]. doi:10.18653/v1/W19-4828 . （原始内容存档于2020-10-21）.
Constructing Transformers For Longer Sequences with Sparse Attention Methods. Google AI Blog. [2021-05-28]. （原始内容存档于2021-09-18）（英语）.
Tasks with Long Sequences – Chatbot. Coursera. [2022-06-08]. （原始内容存档于2020-10-26）.
Reformer: The Efficient Transformer. Google AI Blog. [2020-10-22]. （原始内容存档于2020-10-22）（英语）.
Allard, Maxime. What is a Transformer?. Medium. 2019-07-01 [2019-10-21]. （原始内容存档于2020-10-17）（英语）.
Monsters, Data. 10 Applications of Artificial Neural Networks in Natural Language Processing. Medium. 2017-09-26 [2019-10-21]. （原始内容存档于2020-10-17）（英语）.

worldcat.org

Yang, Zhilin Dai, Zihang Yang, Yiming Carbonell, Jaime Salakhutdinov, Ruslan Le, Quoc V. XLNet: Generalized Autoregressive Pretraining for Language Understanding. 2019-06-19. OCLC 1106350082.