Fetching the paper…
Reading the bibliography…
Learning to walk over a graph towards a target node for a given query and a source node is an important problem in applications such as knowledge base completion (KBC).
A formal basis for the heuristic determination of minimum cost paths
Peter E Hart, Nils J Nilsson, and Bertram Raphael · 1968
Earlier work this paper cites.
Graphs and Their Uses
Oystein Ore · 1990
Earlier work this paper cites.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
Earlier work this paper cites.
Reinforcement Learning: An Introduction
Richard S Sutton and Andrew G Barto · 1998
Earlier work this paper cites.
Policy gradient methods for reinforcement learning with function approximation
Richard S Sutton, David A McAllester, Satinder P Singh, and Yishay Mansour · 2000
Earlier work this paper cites.
A natural policy gradient
Sham M Kakade · 2002
Earlier work this paper cites.
Recurrent policy gradients
Daan Wierstra, Alexander Förster, Jan Peters, and Jürgen Schmidhuber · 2010
Earlier work this paper cites.
A three-way model for collective learning on multi-relational data
Maximilian Nickel, Volker Tresp, and Hans-Peter Kriegel · 2011
Earlier work this paper cites.
Multi-armed bandits with episode context
Christopher D. Rosin · 2011
Earlier work this paper cites.
Translating embeddings for modeling multi-relational data
Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko · 2013
Earlier work this paper cites.
Learning phrase representations using rnn encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
Earlier work this paper cites.
Incorporating vector space similarity in random walk inference over knowledge bases
Matt Gardner, Partha Pratim Talukdar, Jayant Krishnamurthy, and Tom Mitchell · 2014
Earlier work this paper cites.
Traversing knowledge graphs in vector space
Kelvin Guu, John Miller, and Percy Liang · 2015
Earlier work this paper cites.
Deep recurrent Q-learning for partially observable MDPs
Matthew J. Hausknecht and Peter Stone · 2015
Earlier work this paper cites.
Modeling relation paths for representation learning of knowledge bases
Yankai Lin, Zhiyuan Liu, Huanbo Luan, Maosong Sun, Siwei Rao, and Song Liu · 2015
Cited alongside, same era.
Learning entity and relation embeddings for knowledge graph completion
Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, and Xuan Zhu · 2015
Cited alongside, same era.
Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
Cited alongside, same era.
Compositional vector space models for knowledge base completion
Arvind Neelakantan, Benjamin Roth, and Andrew McCallum · 2015
Cited alongside, same era.
Observed versus latent features for knowledge base and text inference
Kristina Toutanova and Danqi Chen · 2015
Cited alongside, same era.
Pointer networks
Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly · 2015
Q-LDA: Uncovering latent patterns in text-based sequential decision processes
Jianshu Chen, Chong Wang, Lin Xiao, Ji He, Lihong Li, and Li Deng · 2017
Later among the works it cites.
Modeling large-scale structured relationships with shared memory for knowledge base completion
Yelong Shen, Po-Sen Huang, Ming-Wei Chang, and Jianfeng Gao · 2017
Later among the works it cites.
Mastering the game of Go without human knowledge
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al · 2017
Later among the works it cites.
The predictron: End-to-end learning and planning
David Silver, Hado van Hasselt, Matteo Hessel, Tom Schaul, Arthur Guez, Tim Harley, Gabriel Dulac-Arnold, David Reichert, Neil Rabinowitz, Andre Barreto, and Thomas Degris · 2017
Later among the works it cites.
Knowledge graph completion via complex tensor factorization
Théo Trouillon, Christopher R Dance, Johannes Welbl, Sebastian Riedel, Éric Gaussier, and Guillaume Bouchard · 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…
Cited alongside, same era.
Embedding entities and relations for learning and inference in knowledge bases
Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng · 2015
Cited alongside, same era.
Neural combinatorial optimization with reinforcement learning
Irwan Bello, Hieu Pham, Quoc V Le, Mohammad Norouzi, and Samy Bengio · 2016
Cited alongside, same era.
Q-prop: Sample-efficient policy gradient with an off-policy critic
Shixiang Gu, Timothy Lillicrap, Zoubin Ghahramani, Richard E Turner, and Sergey Levine · 2016
Cited alongside, same era.
Deep reinforcement learning with a natural language action space
Ji He, Jianshu Chen, Xiaodong He, Jianfeng Gao, Lihong Li, Li Deng, and Mari Ostendorf · 2016
Cited alongside, same era.
Mastering the game of Go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
Cited alongside, same era.
Compositional learning of embeddings for relation paths in knowledge bases and text
Kristina Toutanova, Xi Victoria Lin, Scott Wen tau Yih, Hoifung Poon, and Chris Quirk · 2016
Cited alongside, same era.
Imagination-augmented agents for deep reinforcement learning
Theophane Weber, Sébastien Racanière, David P. Reichert, Lars Buesing, Arthur Guez, Danilo Jimenez Rezende, Adrià Puigdomènech Badia, Oriol Vinyals, Nicolas Heess, Yujia Li, Razvan Pascanu, Peter Battaglia, David Silver, and Daan Wierstra · 2017
Later among the works it cites.
Scalable trust-region method for deep reinforcement learning using kronecker-factored approximation
Yuhuai Wu, Elman Mansimov, Roger B Grosse, Shun Liao, and Jimmy Ba · 2017
Later among the works it cites.
DeepPath: A reinforcement learning method for knowledge graph reasoning
Wenhan Xiong, Thien Hoang, and William Yang Wang · 2017
Later among the works it cites.
Differentiable learning of logical rules for knowledge base reasoning
Fan Yang, Zhilin Yang, and William W Cohen · 2017
Later among the works it cites.
Go for a walk and arrive at the answer: Reasoning over paths in knowledge bases using reinforcement learning
Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, Luke Vilnis, Ishan Durugkar, Akshay Krishnamurthy, Alexander J. Smola, and Andrew McCallum · 2018
Closest in time.
Convolutional 2D knowledge graph embeddings
Tim Dettmers, Minervini Pasquale, Stenetorp Pontus, and Sebastian Riedel · 2018
Closest in time.
Neural approaches to conversational AI
Jianfeng Gao, Michel Galley, and Lihong Li · 2018
Closest in time.
ReinforceWalk: Learning to walk in graph with Monte Carlo tree search
Yelong Shen, Jianshu Chen, Po-Sen Huang, Yuqing Guo, and Jianfeng Gao · 2018
Closest in time.