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История изменения показателей | Авторизация |
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Graham Neubig
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Сайт | Общие фразы | PR | тИЦ | Alexa Rank | Alexa Country | |
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wikipedia.org | 29 | 9 |
0 | 5 | 6 | |
stanford.edu | 21 | 9 |
0 | 885 | 370 | |
youtube.com | 17 | 9 |
0 | 2 | 2 | |
stackoverflow.com | 15 | 7 |
0 | 47 | 44 | |
quora.com | 11 | 7 |
0 | 78 | 47 | |
nltk.org | 11 | 6 |
50 | 54746 | 35488 | |
google.com | 11 | 9 |
0 | 1 | 1 | |
opennlp.sourceforge.net | 10 | 8 |
20 | 268 | 472 | |
vanderbilt.edu | 9 | 7 |
0 | 6633 | 1562 | |
apache.org | 9 | 9 |
0 | 1217 | 460 | |
Еще 40 сайтов после авторизации |
Данные предоставлены сервисом semrush
Данные linkpad ( 10 Февраля 2014 ) | |
Количество ссылок на сайт | 39 |
Количество доменов, которые ссылаются на сайт | 18 |
Количество найденных анкоров | 20 |
Исходящие (внешние) ссылки домена | 61 |
Количество доменов, на которые ссылается сайт | 44 |
Количество исходящих анкоров | 46 |
Внешние ссылки главной страницы ( 25 ) | |
cs.cmu.edu/~neulab/ | NeuLab |
cmu.edu | Carnegie Mellon University |
lti.cs.cmu.edu | Language Technology Institute |
cs.cmu.edu | School of Computer Science |
twitter.com/gneubig | |
sites.google.com/view/iclr2019-drlstructpred/ | structured prediction workshop |
deep-gen-struct.github.io | deep generative models workshop |
slator.com/academia/terminology-assistance-coming-to-a-simul... | Slator |
blackboxnlp.github.io | Black Box NLP Workshop |
conala-corpus.github.io | CoNaLa |
cs.cmu.edu/~neulab//2018/05/31/neulab-presentations-at-naacl... | See the details on my lab site |
mltrain.cc/events/nips-highlights-learn-how-to-code-a-paper-... | How to Code a Paper |
cs.cmu.edu/~neulab//2017/12/04/neulab-presentations-at-nips-... | our 2 papers at NIPS 2017 |
alxndrkalinin.github.io/a2-dlearn-2017/ | a2-dlearn |
cs.cmu.edu/~neulab/2017/07/30/neulab-presentations-at-acl.ht... | total of 10 papers |
github.com/neubig/lxmls-2017 | Simple and Effective Learning with Dynamic Neural Networks |
arxiv.org/pdf/1703.01619.pdf | neural machine translation and sequence to sequence models |
arxiv.org/abs/1701.03980 | technical report |
github.com/clab/dynet | DyNet |
sites.google.com/site/acl17nmt/ | First Workshop on Neural Machine Translation |
ipsj-one.org | IPSJ-ONE |
amazon.co.jp/gp/product/479812852X | Natural Language Processing: Fundamentals and Techniques |
amazon.co.jp/岩波データサイエンス-Vol-2-岩波データサイエンス刊行委員会/dp/4000298526/ | Iwanami data science book series on natural language processing |
ahclab.naist.jp/pseudogen | here |
cs.cmu.edu/~cdyer/ | Chris Dyer |
Внутренние ссылки главной страницы ( 45 ) | |
/index.php | Home |
research.php | research page |
publications.php | publications page |
software.php | software page |
teaching.php | teaching page |
/contact.php | Contact Me |
/index.php?lang=ja | 日本語 |
bio.php | third-person bio |
phontron.com/slides/neubig19neuralgen.pdf | What can Statistical Machine Translation Teach Neural Models about Generation? |
phontron.com/slides/neubig19structured.pdf | What can Statistical Machine Translation Teach Neural Machine Translation about Structured Prediction? |
phontron.com/slides/neubig19generative.pdf | Learning about Language with Normalizing Flows |
phontron.com/slides/neubig18blackbox.pdf | Learning with Latent Linguistic Structure |
phontron.com/slides/neubig18austin.pdf | Towards Open-domain Generation of Programs from Natural Language |
/slides/neubig18nyu.pdf | What Can Neural Networks Teach us about Language? |
/slides/neubig17howtocode.pdf | Simple and Efficient Learning in Dynamic Networks |
/slides/neubig17a2dlearn.pdf | What Can Neural Networks Teach us about Language? |
phontron.com/slides/neubig17mtma.pdf | Softmax Alternatives for Neural MT |
phontron.com/paper/adams17eacl.pdf | cross-lingual word embeddings for language modeling |
phontron.com/paper/gu17eacl.pdf | real-time translation with neural machine translation |
phontron.com/paper/kuncoro17eacl.pdf | learning about syntax with recurrent neural network grammars |
phontron.com/paper/adams16emnlp.pdf | learning lexicons from parallel speech |
phontron.com/paper/neubig16emnlp.pdf | combining neural and n-gram language models |
phontron.com/paper/arthur16emnlp.pdf | incorporating lexicons into neural machine translation |
phontron.com/paper/kikuchi16emnlp.pdf | controlling output length in encoder-decoders |
phontron.com/paper/neubig16wat.pdf | Japanese-English system for the Workshop on Asian Translation |
phontron.com/zhang16acl.pdf | continuous-space rule selection for syntax-based machine translation |
phontron.com/paper/faruqui16naacl.pdf | morphological inflection generation |
phontron.com/paper/miura16naacl.pdf | segment-based active learning for machine translation |
phontron.com/paper/neubig16cl.pdf | optimization for machine translation |
phontron.com/slides/neubig15simultrans.pdf | Simultaneous Speech Translation |
phontron.com/paper/tsunomori16iwsds.pdf | dialog-based deception detection |
phontron.com/paper/hiraoka16iwsds.pdf | active learning for constructing dialog systems |
phontron.com/paper/yoshino16iwsds.pdf | LSTM-based dialog state tracking |
phontron.com/paper/mizukami15asru.pdf | user-adaptive dialogue |
phontron.com/paper/sakti15asru.pdf | sentence compression using LSTMs |
phontron.com/paper/lubis15asru.pdf | affective communication |
phontron.com/paper/do15asru.pdf | our speech recognition system for the MGB challenge |
phontron.com/paper/morishita15iwslt.pdf | self-training for machine translation |
phontron.com/paper/adams15iwslt.pdf | learning lexicons from phoneme transcriptions |
phontron.com/paper/do15iwslt.pdf | translation of prosodic emphasis |
phontron.com/paper/heck15iwslt.pdf | the NAIST speech recognition system |
phontron.com/paper/oda15ase.pdf | generating pseudo-code from source code |
phontron.com/paper/fudaba15asedemo.pdf | the corresponding tool |
phontron.com/paper/neubig15wat.pdf | our paper |
phontron.com/paper/arthur15tacl.pdf | semantic parsing of ambiguous input through paraphrasing and verification |
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