Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Polosukhin, Illia"'
Autor:
Kwiatkowski, Tom, Palomaki, Jennimaria, Redfield, Olivia, Collins, Michael, Parikh, Ankur, Alberti, Chris, Epstein, Danielle, Polosukhin, Illia, Devlin, Jacob, Lee, Kenton, Toutanova, Kristina, Jones, Llion, Kelcey, Matthew, Chang, Ming-Wei, Dai, Andrew M., Uszkoreit, Jakob, Le, Quoc, Petrov, Slav
Publikováno v:
Transactions of the Association for Computational Linguistics, Vol 7, Pp 453-466 (2019)
We present the Natural Questions corpus, a question answering data set. Questions consist of real anonymized, aggregated queries issued to the Google search engine. An annotator is presented with a question along with a Wikipedia page from the top 5
Externí odkaz:
https://doaj.org/article/8650fdc04d7944c4893d0b995b6de6f7
We present a program synthesis-oriented dataset consisting of human written problem statements and solutions for these problems. The problem statements were collected via crowdsourcing and the program solutions were extracted from human-written solut
Externí odkaz:
http://arxiv.org/abs/1807.03168
Autor:
Polosukhin, Illia, Skidanov, Alexander
We present a Neural Program Search, an algorithm to generate programs from natural language description and a small number of input/output examples. The algorithm combines methods from Deep Learning and Program Synthesis fields by designing rich doma
Externí odkaz:
http://arxiv.org/abs/1802.04335
TensorFlow Estimators: Managing Simplicity vs. Flexibility in High-Level Machine Learning Frameworks
Autor:
Cheng, Heng-Tze, Haque, Zakaria, Hong, Lichan, Ispir, Mustafa, Mewald, Clemens, Polosukhin, Illia, Roumpos, Georgios, Sculley, D, Smith, Jamie, Soergel, David, Tang, Yuan, Tucker, Philipp, Wicke, Martin, Xia, Cassandra, Xie, Jianwei
We present a framework for specifying, training, evaluating, and deploying machine learning models. Our focus is on simplifying cutting edge machine learning for practitioners in order to bring such technologies into production. Recognizing the fast
Externí odkaz:
http://arxiv.org/abs/1708.02637
Autor:
Vaswani, Ashish, Shazeer, Noam, Parmar, Niki, Uszkoreit, Jakob, Jones, Llion, Gomez, Aidan N., Kaiser, Lukasz, Polosukhin, Illia
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose
Externí odkaz:
http://arxiv.org/abs/1706.03762
Autor:
Choi, Eunsol, Hewlett, Daniel, Lacoste, Alexandre, Polosukhin, Illia, Uszkoreit, Jakob, Berant, Jonathan
We present a framework for question answering that can efficiently scale to longer documents while maintaining or even improving performance of state-of-the-art models. While most successful approaches for reading comprehension rely on recurrent neur
Externí odkaz:
http://arxiv.org/abs/1611.01839
Autor:
Hewlett, Daniel, Lacoste, Alexandre, Jones, Llion, Polosukhin, Illia, Fandrianto, Andrew, Han, Jay, Kelcey, Matthew, Berthelot, David
Publikováno v:
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2016, pp. 1535-1545
We present WikiReading, a large-scale natural language understanding task and publicly-available dataset with 18 million instances. The task is to predict textual values from the structured knowledge base Wikidata by reading the text of the correspon
Externí odkaz:
http://arxiv.org/abs/1608.03542
Autor:
Polosukhin, Illia (AUTHOR)
Publikováno v:
Wall Street Journal - Online Edition. 3/24/2022, pN.PAG-N.PAG. 1p.