Fetching the paper…
Reading the bibliography…
Learning algorithms have shown considerable prowess in simulation by allowing robots to adapt to uncertain environments and improve their performance.
Control system analysis and design via the “second method” of Lyapunov II: Discrete-time systems
R. Kalman and J. Bertram · 1960
Earlier work this paper cites.
Maximal Lyapunov functions and domains of attraction for autonomous nonlinear systems
A. Vannelli and M. Vidyasagar · 1985
Earlier work this paper cites.
Approximation by superpositions of a sigmoidal function
G. Cybenko · 1989
Earlier work this paper cites.
Stability theory for differential/algebraic systems with application to power systems
D. J. Hill and I. M. Y. Mareels · 1990
Earlier work this paper cites.
Essentials of Robust Control
K. Zhou and J. C. Doyle · 1998
Earlier work this paper cites.
Using SeDuMi 1.02, a MATLAB toolbox for optimization over symmetric cones
J. F. Sturm · 1999
Earlier work this paper cites.
Structured semidefinite programs and semialgebraic geometry methods in robustness and optimization
P. A. Parrilo · 2000
Earlier work this paper cites.
Robust stability and domain of attraction of uncertain nonlinear systems
A. Trofino · 2000
Earlier work this paper cites.
Some new results on neural network approximation
K. Hornik · 2001
Earlier work this paper cites.
Nonlinear Systems
H. K. Khalil · 2002
Earlier work this paper cites.
On the construction of Lyapunov functions using the sum of squares decomposition
A. Papachristodoulou and S. Prajna · 2002
Earlier work this paper cites.
Introducing SOSTOOLS: A general purpose sum of squares programming solver
S. Prajna, A. Papachristodoulou, and P. A. Parrilo · 2002
Cited alongside, same era.
Antiwindup design with guaranteed regions of stability: An LMI-based approach
J. M. G. da Silva Jr. and S. Tarbouriech · 2005
Cited alongside, same era.
Scalable analysis of nonlinear systems using convex optimization
A. Papachristodoulou · 2005
Cited alongside, same era.
Gaussian Processes for Machine Learning
C. E. Rasmussen and C. K. I. Williams · 2006
Cited alongside, same era.
Construction of neural network based Lyapunov functions
V. Petridis and S. Petridis · 2006
Cited alongside, same era.
Pattern Recognition and Machine Learning
C. M. Bishop · 2006
Cited alongside, same era.
Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2014
Later among the works it cites.
Concrete problems in AI safety
D. Amodei, C. Olah, J. Steinhardt, P. Christiano, J. Schulman, and D. Mané · 2016
Later among the works it cites.
Review on computational methods for Lyapunov functions
P. Giesl and S. Hafstein · 2016
Later among the works it cites.
A sampling approach to finding Lyapunov functions for nonlinear discrete-time systems
R. Bobiti and M. Lazar · 2016
Later among the works it cites.
Safe learning of regions of attraction for uncertain, nonlinear systems with Gaussian processes
F. Berkenkamp, R. Moriconi, A. P. Schoellig, and A. Krause · 2016
Later among the works it cites.
TensorFlow: A system for large-scale machine learning
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Generation of Lyapunov functions by neural networks
N. Noroozi, P. Karimaghaee, F. Safaei, and H. Javadi · 2008
Cited alongside, same era.
Convex Optimization
S. Boyd and L. Vandenberghe · 2009
Cited alongside, same era.
Robust region-of-attraction estimation
U. Topcu, A. K. Packard, P. Seiler, and G. J. Balas · 2010
Cited alongside, same era.
Optimal Control
F. L. Lewis, D. L. Vrabie, and V. L. Syrmos · 2012
Cited alongside, same era.
Convex computation of the region of attraction of polynomial control systems
D. Henrion and M. Korda · 2014
Cited alongside, same era.
M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, and X. Zheng · 2016
Later among the works it cites.
Safe model-based reinforcement learning with stability guarantees
F. Berkenkamp, M. Turchetta, A. P. Schoellig, and A. Krause · 2017
Later among the works it cites.
Safety verification of deep neural networks
X. Huang, M. Kwiatkowska, S. Wang, and M. Wu · 2017
Later among the works it cites.
Reluplex: An efficient SMT solver for verifying deep neural networks
G. Katz, C. Barrett, D. Dill, K. Julian, and M. Kochenderfer · 2017
Later among the works it cites.
Reinforcement Learning
R. S. Sutton and A. G. Barto · 2018
Closest in time.
Learning-based model predictive control for safe exploration
T. Koller, F. Berkenkamp, M. Turchetta, and A. Krause · 2018
Closest in time.