Fetching the paper…
Reading the bibliography…
In this work, we investigate the performance of untrained randomly initialized encoders in a general class of sequence to sequence models and compare their performance with that of fully-trained encoders on the task of abstractive summarization.
No training required: Exploring random encoders for sentence classification
John Wieting and Douwe Kiela. 2019 · 1901
Earlier work this paper cites.
Learning in random nets
Marvin Minsky and Oliver G. Selfridge. 1961 · 1961
Earlier work this paper cites.
Feedforward neural networks with random weights
W. F. Schmidt, M. A. Kraaijveld, and R. P. W. Duin. 1992 · 1992
Earlier work this paper cites.
Learning long-term dependencies with gradient descent is difficult
Y. Bengio, P. Simard, and P. Frasconi. 1994 · 1994
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
Earlier work this paper cites.
Gradient flow in recurrent nets: the difficulty of learning long-term dependencies
Sepp Hochreiter, Yoshua Bengio, and Paolo Frasconi. 2001 · 2001
Earlier work this paper cites.
The” echo state” approach to analysing and training recurrent neural networks-with an erratum note’
Herbert Jaeger. 2001 · 2001
Earlier work this paper cites.
Extreme learning machine: a new learning scheme of feedforward neural networks
Zhu Q.-Y. Huang G.-B. and Siew C.-K. 2004 · 2004
Earlier work this paper cites.
Looking for a few good metrics: Automatic summarization evaluation-how many samples are enough?
Chin-Yew Lin. 2004 · 2004
Earlier work this paper cites.
Universal approximation using incremental constructive feedforward networks with random hidden nodes
Guang-Bin Huang, Lei Chen, and Chee-Kheong Siew. 2006 · 2006
Earlier work this paper cites.
What is the best multi-stage architecture for object recognition?
Kevin Jarrett, Koray Kavukcuoglu, Marc’Aurelio Ranzato, and Yann LeCun. 2009 · 2009
Earlier work this paper cites.
Adaptive subgradient methods for online learning and stochastic optimization
John Duchi, Elad Hazan, and Yoram Singer. 2011 · 2011
Cited alongside, same era.
Comparison of echo state network and extreme learning machine on nonlinear prediction
Bin Li, Yibin Li, and Xuewen Rong. 2011 · 2011
Cited alongside, same era.
On random weights and unsupervised feature learning
Andrew M. Saxe, Pang Wei Koh, Zhenghao Chen, Maneesh Bhand, Bipin Suresh, and Andrew Y. Ng. 2011 · 2011
Cited alongside, same era.
Teaching machines to read and comprehend
Karl Moritz Hermann, Tomáš Kočiský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
Cited alongside, same era.
Skip-thought vectors
Ryan Kiros, Yukun Zhu, Ruslan R Salakhutdinov, Richard Zemel, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. 2015 · 2015
Cited alongside, same era.
Fine-grained analysis of sentence embeddings using auxiliary prediction tasks
Supervised learning of universal sentence representations from natural language inference data
Alexis Conneau, Douwe Kiela, Holger Schwenk, Loic Barrault, and Antoine Bordes. 2017 · 2017
Later among the works it cites.
Understanding and improving morphological learning in the neural machine translation decoder
Fahim Dalvi, Nadir Durrani, Hassan Sajjad, Yonatan Belinkov, and Stephan Vogel. 2017 · 2017
Later among the works it cites.
Dieuwke Hupkes, Sara Veldhoen, and Willem H. Zuidema. 2017 · 2017
Later among the works it cites.
Learning visually grounded sentence representations
Douwe Kiela, Alexis Conneau, Allan Jabri, and Maximilian Nickel. 2017 · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Yossi Adi, Einat Kermany, Yonatan Belinkov, Ofer Lavi, and Yoav Goldberg. 2016 · 2016
Cited alongside, same era.
Probing for semantic evidence of composition by means of simple classification tasks
Allyson Ettinger, Ahmed Elgohary, and Philip Resnik. 2016 · 2016
Cited alongside, same era.
Assessing the ability of lstms to learn syntax-sensitive dependencies
Tal Linzen, Emmanuel Dupoux, and Yoav Goldberg. 2016 · 2016
Cited alongside, same era.
Abstractive text summarization using sequence-to-sequence rnns and beyond
Ramesh Nallapati, Bowen Zhou, Caglar Gulcehre, Bing Xiang, et al. 2016 · 2016
Cited alongside, same era.
Using the output embedding to improve language models
Ofir Press and Lior Wolf. 2016 · 2016
Cited alongside, same era.
What do neural machine translation models learn about morphology?
Yonatan Belinkov, Nadir Durrani, Fahim Dalvi, Hassan Sajjad, and James R. Glass. 2017 · 2017
Cited alongside, same era.
Brenden M. Lake and Marco Baroni. 2017 · 2017
Later among the works it cites.
Get to the point: Summarization with pointer-generator networks
Abigail See, Peter J. Liu, and Christopher D. Manning. 2017 · 2017
Later among the works it cites.
A discourse-aware attention model for abstractive summarization of long documents
Arman Cohan, Franck Dernoncourt, Doo Soon Kim, Trung Bui, Seokhwan Kim, Walter Chang, and Nazli Goharian. 2018 · 2018
Later among the works it cites.
Senteval: An evaluation toolkit for universal sentence representations
Alexis Conneau and Douwe Kiela. 2018 · 2018
Later among the works it cites.
Reservoir computing echo state network classifier training
V Krylov and S Krylov. 2018 · 2018
Later among the works it cites.
Syntax helps ELMo understand semantics: Is syntax still relevant in a deep neural architecture for SRL?
Emma Strubell and Andrew McCallum. 2018 · 2018
Later among the works it cites.
Recent trends in deep learning based natural language processing
Tom Young, Devamanyu Hazarika, Soujanya Poria, and Erik Cambria. 2018 · 2018
Later among the works it cites.