Fetching the paper…
Reading the bibliography…
The smallest eigenvectors of the graph Laplacian are well-known to provide a succinct representation of the geometry of a weighted graph.
Grundzüge einer allgemeinen theorie der linearen integralgleichungen. vierte mitteilung
David Hilbert · 1906
Earlier work this paper cites.
Untersuchungen über systeme integrierbarer funktionen
Friedrich Riesz · 1910
Earlier work this paper cites.
Simplified neuron model as a principal component analyzer
Erkki Oja · 1982
Earlier work this paper cites.
On stochastic approximation of the eigenvectors and eigenvalues of the expectation of a random matrix
Erkki Oja · 1985
Earlier work this paper cites.
Markov decision processes
Martin L Puterman · 1990
Earlier work this paper cites.
Spectral graph theory
Fan RK Chung and Fan Chung Graham · 1997
Earlier work this paper cites.
Automorphic Forms and Representations
D. Bump · 1998
Earlier work this paper cites.
On spectral clustering: Analysis and an algorithm
Andrew Y Ng, Michael I Jordan, and Yair Weiss · 2002
Earlier work this paper cites.
On spectral graph drawing
Yehuda Koren · 2003
Earlier work this paper cites.
Proto-value functions: Developmental reinforcement learning
Sridhar Mahadevan · 2005
Cited alongside, same era.
Using predictive representations to improve generalization in reinforcement learning
Eddie J Rafols, Mark B Ring, Richard S Sutton, and Brian Tanner · 2005
Cited alongside, same era.
Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
Cited alongside, same era.
Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
Cited alongside, same era.
Design principles of the hippocampal cognitive map
Kimberly L Stachenfeld, Matthew Botvinick, and Samuel J Gershman · 2014
Cited alongside, same era.
Scale up nonlinear component analysis with doubly stochastic gradients
Bo Xie, Yingyu Liang, and Le Song · 2015
Cited alongside, same era.
# exploration: A study of count-based exploration for deep reinforcement learning
Haoran Tang, Rein Houthooft, Davis Foote, Adam Stooke, OpenAI Xi Chen, Yan Duan, John Schulman, Filip DeTurck, and Pieter Abbeel · 2017
Later among the works it cites.
Online principal component analysis in high dimension: Which algorithm to choose?
Hervé Cardot and David Degras · 2018
Closest in time.
Investigating human priors for playing video games
Rachit Dubey, Pulkit Agrawal, Deepak Pathak, Thomas L Griffiths, and Alexei A Efros · 2018
Closest in time.
Data-efficient hierarchical reinforcement learning
Ofir Nachum, Shane Gu, Honglak Lee, and Sergey Levine · 2018
Closest in time.
Spectral inference networks: Unifying spectral methods with deep learning
David Pfau, Stig Petersen, Ashish Agarwal, David Barrett, and Kim Stachenfeld · 2018
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Hindsight experience replay
Marcin Andrychowicz, Filip Wolski, Alex Ray, Jonas Schneider, Rachel Fong, Peter Welinder, Bob McGrew, Josh Tobin, OpenAI Pieter Abbeel, and Wojciech Zaremba · 2017
Cited alongside, same era.
Curiosity-driven exploration by self-supervised prediction
Deepak Pathak, Pulkit Agrawal, Alexei A Efros, and Trevor Darrell · 2017
Cited alongside, same era.
A memoir on the theory of matrices
Arthur Cayley
Cited in the paper.
A laplacian framework for option discovery in reinforcement learning
Marlos C Machado, Marc G Bellemare, and Michael Bowling
Cited in the paper.
Eigenoption discovery through the deep successor representation
Marlos C Machado, Clemens Rosenbaum, Xiaoxiao Guo, Miao Liu, Gerald Tesauro, and Murray Campbell
Cited in the paper.
Multi-goal reinforcement learning: Challenging robotics environments and request for research
Matthias Plappert, Marcin Andrychowicz, Alex Ray, Bob McGrew, Bowen Baker, Glenn Powell, Jonas Schneider, Josh Tobin, Maciek Chociej, Peter Welinder, et al · 2018
Closest in time.
Temporal difference models: Model-free deep rl for model-based control
Vitchyr Pong, Shixiang Gu, Murtaza Dalal, and Sergey Levine · 2018
Closest in time.
Spectralnet: Spectral clustering using deep neural networks
Uri Shaham, Kelly Stanton, Henry Li, Boaz Nadler, Ronen Basri, and Yuval Kluger · 2018
Closest in time.