Fetching the paper…
Reading the bibliography…
Typically, loss functions, regularization mechanisms and other important aspects of training parametric models are chosen heuristically from a limited set of options.
Evolutionary principles in self-referential learning, or on learning how to learn: the meta-meta-… hook. Institut für Informatik, Technische Universität München, 1987
J. Schmidhuber · 1987
Earlier work this paper cites.
Learning a synaptic learning rule
Y. Bengio and S. Bengio · 1990
Earlier work this paper cites.
Efficient memory-based learning for robot control
A. Moore · 1990
Earlier work this paper cites.
Efficient training of artificial neural networks for autonomous navigation
D. A. Pomerleau · 1991
Earlier work this paper cites.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
R. J. Williams · 1992
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
Earlier work this paper cites.
Algorithms for inverse reinforcement learning
A. Y. Ng, S. J. Russell, et al · 2000
Earlier work this paper cites.
Policy gradient methods for reinforcement learning with function approximation
R. Sutton, D. McAllester, S. Singh, and Y. Mansour · 2000
Earlier work this paper cites.
Apprenticeship learning via inverse reinforcement learning
P. Abbeel and A. Y. Ng · 2004
Earlier work this paper cites.
Robot programming by demonstration
A. Billard, S. Calinon, R. Dillmann, and S. Schaal · 2008
Earlier work this paper cites.
Learning to learn
S. Thrun and L. Pratt · 2012
Earlier work this paper cites.
Rigid body dynamics algorithms
R. Featherstone · 2014
Earlier work this paper cites.
Control-limited differential dynamic programming
Y. Tassa, N. Mansard, and E. Todorov · 2014
Cited alongside, same era.
Gradient-based hyperparameter optimization through reversible learning
D. Maclaurin, D. Duvenaud, and R. Adams · 2015
Cited alongside, same era.
Gradient estimation using stochastic computation graphs
J. Schulman, N. Heess, T. Weber, and P. Abbeel · 2015
Cited alongside, same era.
Learning to learn by gradient descent by gradient descent
M. Andrychowicz, M. Denil, S. G. Colmenarejo, M. W. Hoffman, D. Pfau, T. Schaul, and N. de Freitas · 2016
Cited alongside, same era.
Rl 2 : Fast reinforcement learning via slow reinforcement learning
Y. Duan, J. Schulman, X. Chen, P. L. Bartlett, I. Sutskever, and P. Abbeel · 2016
Cited alongside, same era.
Unsupervised learning via meta-learning
K. Hsu, S. Levine, and C. Finn · 2018
Later among the works it cites.
Online learning of a memory for learning rates
F. Meier, D. Kappler, and S. Schaal · 2018
Later among the works it cites.
Learning to teach with dynamic loss functions
L. Wu, F. Tian, Y. Xia, Y. Fan, T. Qin, J.-H. Lai, and T.-Y. Liu · 2018
Later among the works it cites.
Meta-gradient reinforcement learning
Z. Xu, H. van Hasselt, and D. Silver · 2018
Later among the works it cites.
One-shot imitation from observing humans via domain-adaptive meta-learning
T. Yu, C. Finn, A. Xie, S. Dasari, T. Zhang, P. Abbeel, and S. Levine · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
K. Li and J. Malik · 2016
Cited alongside, same era.
Model-agnostic meta-learning for fast adaptation of deep networks
C. Finn, P. Abbeel, and S. Levine · 2017
Cited alongside, same era.
Forward and reverse gradient-based hyperparameter optimization
L. Franceschi, M. Donini, P. Frasconi, and M. Pontil · 2017
Cited alongside, same era.
Taming the waves: sine as activation function in deep neural networks
G. Parascandolo, H. Huttunen, and T. Virtanen · 2017
Cited alongside, same era.
Learning to learn: Meta-critic networks for sample efficient learning
F. Sung, L. Zhang, T. Xiang, T. Hospedales, and Y. Yang · 2017
Cited alongside, same era.
Meta-reinforcement learning of structured exploration strategies
A. Gupta, R. Mendonca, Y. Liu, P. Abbeel, and S. Levine · 2018
Cited alongside, same era.
Evolved policy gradients
R. Houthooft, Y. Chen, P. Isola, B. C. Stadie, F. Wolski, J. Ho, and P. Abbeel · 2018
Cited alongside, same era.
E. Grefenstette, B. Amos, D. Yarats, P. M. Htut, A. Molchanov, F. Meier, D. Kiela, K. Cho, and S. Chintala · 2019
Closest in time.
Deep lagrangian networks: Using physics as model prior for deep learning
M. Lutter, C. Ritter, and J. Peters · 2019
Closest in time.
R. Mendonca, A. Gupta, R. Kralev, P. Abbeel, S. Levine, and C. Finn · 2019
Closest in time.
Learning unsupervised learning rules
L. Metz, N. Maheswaranathan, B. Cheung, and J. Sohl-Dickstein · 2019
Closest in time.
OpenAI Gym, 2019
2019
Closest in time.
A framework for elegantly configuring complex applications
O. Yadan · 2019
Closest in time.
Reward shaping via meta-learning
H. Zou, T. Ren, D. Yan, H. Su, and J. Zhu · 2019
Closest in time.