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With the rise of neural models across the field of information retrieval, numerous publications have incrementally pushed the envelope of performance for a multitude of IR tasks.
Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
Ronald J. Williams. 1992 · 1992
Earlier work this paper cites.
Reinforcement Learning: An Introduction
R. S. Sutton and A. G. Barto. 1998 · 1998
Earlier work this paper cites.
Reinforcement learning in continuous time and space
Kenji Doya. 2000 · 2000
Earlier work this paper cites.
Learning to rank answers on large online QA collections. In ACL:HLT
Mihai Surdeanu, Massimiliano Ciaramita, and Hugo Zaragoza. 2008 · 2008
Earlier work this paper cites.
On the (Non-)existence of Convex, Calibrated Surrogate Losses for Ranking. In NIPS
Clément Calauzènes, Nicolas Usunier, and Patrick Gallinari. 2012 · 2012
Earlier work this paper cites.
Multiple Testing in Statistical Analysis of Systems-based Information Retrieval Experiments
Benjamin A. Carterette. 2012 · 2012
Earlier work this paper cites.
The Loss Surface of Multilayer Networks
Anna Choromanska, Mikael Henaff, Michaël Mathieu, Gérard Ben Arous, and Yann LeCun. 2014 · 2014
Cited alongside, same era.
Bias in Natural Actor-Critic Algorithms. In ICML 2014, Beijing, China, 21-26 June 2014
Philip Thomas. 2014 · 2014
Cited alongside, same era.
Learning Multiple Tasks with Deep Relationship Networks
Mingsheng Long and Jianmin Wang. 2015 · 2015
Cited alongside, same era.
Learning to Rank Short Text Pairs with Convolutional Deep Neural Networks. In SIGIR
Aliaksei Severyn and Alessandro Moschitti. 2015 · 2015
Cited alongside, same era.
A Deep Relevance Matching Model for Ad-hoc Retrieval. In CIKM ’16
Jiafeng Guo, Yixing Fan, Qingyao Ai, and W. Bruce Croft. 2016 · 2016
Cited alongside, same era.
Proximal Policy Optimization Algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017 · 2017
Later among the works it cites.
IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models. In ACM SIGIR, Shinjuku, Tokyo, Japan, August 7-11, 2017
Jun Wang, Lantao Yu, Weinan Zhang, Yu Gong, Yinghui Xu, Benyou Wang, Peng Zhang, and Dell Zhang. 2017 · 2017
Later among the works it cites.
Reproducing a Neural Question Answering Architecture Applied to the SQuAD Benchmark Dataset: Challenges and Lessons Learned. In ECIR 2018, Grenoble, France, March 26-29, 2018
Alexander Dür, Andreas Rauber, and Peter Filzmoser. 2018 · 2018
Closest in time.
Deep Reinforcement Learning That Matters. In AAAI, New Orleans, Louisiana, USA, February 2-7, 2018
Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, and David Meger. [n. d.] · 2018
Closest in time.
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Learning to match using local and distributed representations of text for web search. In WWW 17
Bhaskar Mitra, Fernando Diaz, and Nick Craswell. 2017 · 2017
Cited alongside, same era.
End to End Long Short Term Memory Networks for Non-Factoid Question Answering. In ICTIR ’16
Daniel Cohen and W. Bruce Croft. [n. d.]
Cited in the paper.
Yi Yang, Wen-tau Yih, and Christopher Meek. 2015 · 2018
Closest in time.