Fetching the paper…
Reading the bibliography…
Text embedding representing natural language documents in a semantic vector space can be used for document retrieval using nearest neighbor lookup.
Similarity metric learning for a variable-kernel classifier
D. G. Lowe · 1995
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
Distance metric learning, with application to clustering with side-information
Eric P. Xing, Andrew Y. Ng, Michael I. Jordan, and Stuart Russell · 2002
Earlier work this paper cites.
A neural probabilistic language model
Yoshua Bengio, Réjean Ducharme, Pascal Vincent, and Christian Janvin · 2003
Earlier work this paper cites.
Learning a similarity metric discriminatively, with application to face verification
S. Chopra, R. Hadsell, and Y. LeCun · 2005
Earlier work this paper cites.
Dimensionality reduction by learning an invariant mapping
Raia Hadsell, Sumit Chopra, and Yann LeCun · 2006
Earlier work this paper cites.
Distance metric learning for large margin nearest neighbor classification
Kilian Q. Weinberger and Lawrence K. Saul · 2009
Earlier work this paper cites.
Large scale online learning of image similarity through ranking
Gal Chechik, Varun Sharma, Uri Shalit, and Samy Bengio · 2010
Earlier work this paper cites.
Similarity metric learning for face recognition
Q. Cao, Y. Ying, and P. Li · 2013
Earlier work this paper cites.
One billion word benchmark for measuring progress in statistical language modeling
Ciprian Chelba, Tomas Mikolov, Mike Schuster, Qi Ge, Thorsten Brants, Phillipp Koehn, and Tony Robinson · 2013
Earlier work this paper cites.
Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Distributed representations of sentences and documents
Quoc V. Le and Tomas Mikolov · 2014
Cited alongside, same era.
Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning · 2014
Cited alongside, same era.
Deep learning face representation by joint identification-verification
Yi Sun, Yuheng Chen, Xiaogang Wang, and Xiaoou Tang · 2014
Cited alongside, same era.
Deepface: Closing the gap to human-level performance in face verification
Yaniv Taigman, Ming Yang, Marc’Aurelio Ranzato, and Lior Wolf · 2014
Cited alongside, same era.
Learning fine-grained image similarity with deep ranking
J. Wang, Y. Song, T. Leung, C. Rosenberg, J. Wang, J. Philbin, B. Chen, and Y. Wu · 2014
Cited alongside, same era.
Deep unordered composition rivals syntactic methods for text classification
Mohit Iyyer, Varun Manjunatha, Jordan L. Boyd-Graber, and Hal Daumé III · 2015
Enriching word vectors with subword information
Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov · 2017
Later among the works it cites.
Reading wikipedia to answer open-domain questions
Danqi Chen, Adam Fisch, Jason Weston, and Antoine Bordes · 2017
Later among the works it cites.
Supervised learning of universal sentence representations from natural language inference data
Alexis Conneau, Douwe Kiela, Holger Schwenk, Loïc Barrault, and Antoine Bordes · 2017
Later among the works it cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin · 2017
Later among the works it cites.
Deep metric learning with angular loss
Jian Wang, Feng Zhou, Shilei Wen, Xiao Liu, and Yuanqing Lin · 2017
Later among the works it cites.
Universal sentence encoder
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Skip-thought vectors
Ryan Kiros, Yukun Zhu, Ruslan R Salakhutdinov, Richard Zemel, Raquel Urtasun, Antonio Torralba, and Sanja Fidler · 2015
Cited alongside, same era.
Facenet: A unified embedding for face recognition and clustering
F. Schroff, D. Kalenichenko, and J. Philbin · 2015
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Learning distributed representations of sentences from unlabelled data
Felix Hill, Kyunghyun Cho, and Anna Korhonen · 2016
Cited alongside, same era.
Squad: 100, 000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang · 2016
Cited alongside, same era.
Improved deep metric learning with multi-class n-pair loss objective
Kihyuk Sohn · 2016
Cited alongside, same era.
Daniel Cer, Yinfei Yang, Sheng yi Kong, Nan Hua, Nicole Lyn Untalan Limtiaco, Rhomni St. John, Noah Constant, Mario Guajardo-Céspedes, Steve Yuan, Chris Tar, Yun hsuan Sung, Brian Strope, and Ray Kurzweil · 2018
Closest in time.
Unsupervised learning of sentence embeddings using compositional n-gram features
Matteo Pagliardini, Prakhar Gupta, and Martin Jaggi · 2018
Closest in time.
Evaluation of sentence embeddings in downstream and linguistic probing tasks
C. S. Perone, R. Silveira, and T. S. Paula · 2018
Closest in time.
Deep contextualized word representations
Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer · 2018
Closest in time.
Concatenated p-mean word embeddings as universal cross-lingual sentence representations
Andreas Rücklé, Steffen Eger, Maxime Peyrard, and Iryna Gurevych · 2018
Closest in time.
Qanet: Combining local convolution with global self-attention for reading comprehension
Adams Wei Yu, David Dohan, Minh-Thang Luong, Rui Zhao, Kai Chen, Mohammad Norouzi, and Quoc V Le · 2018
Closest in time.