Results 261 to 270 of about 1,466,211 (327)

Data anomaly repair method based on fuzzy voting and multi-segment interpolation. [PDF]

Sci Rep
Lv Y, Han Q, Xue S.
europepmc   +1 more source

Deciphering the stability of two-dimensional III-V semiconductors: Building blocks and their versatile assembly. [PDF]

Sci Adv
Yan Y, Chen K, Sun M, Ma Y, Wang P, Deng J, Zhang X, Liu JZ.   +7 more
europepmc   +1 more source

Knowledge-guided self-learning control strategy for mixed vehicle platoons with delays. [PDF]

Nat Commun
Wang J, Wang H, Song J, Chen X, Guo J, Li K, Li X, Huang B.   +7 more
europepmc   +1 more source

Stability Certificates for Neural Network Learning-based Controllers using Robust Control Theory

2021 American Control Conference (ACC), 2021
Providing stability guarantees for controllers that use neural networks can be challenging. Robust control theoretic tools are used to derive a framework for providing nominal stability guarantees – stability guarantees for a known nominal system ...
Nguyen Hoang Hai, Tim Zieger, Sandra C. Wells, Αναστασία Νικολακοπούλου, Richard D. Braatz, Rolf Findeisen   +5 more
semanticscholar   +4 more sources

STABILITY RESULTS IN LEARNING THEORY [PDF]

Analysis and Applications, 2005
The problem of proving generalization bounds for the performance of learning algorithms can be formulated as a problem of bounding the bias and variance of estimators of the expected error. We show how various stability assumptions can be employed for this purpose.
Alexander Rakhlin, Sayan Mukherjee, Tomaso Poggio   +2 more
semanticscholar   +4 more sources

Principled reward shaping for reinforcement learning via lyapunov stability theory

Neurocomputing, 2020
Abstract Reinforcement learning (RL) suffers from the designation in reward function and the large computational iterating steps until convergence. How to accelerate the training process in RL plays a vital role. In this paper, we proposed a Lyapunov function based approach to shape the reward function which can effectively accelerate the training ...
Yunlong Dong, Xiuchuan Tang, Ye Yuan
semanticscholar   +4 more sources

Predicting the stability of ternary intermetallics with density functional theory and machine learning

The Journal of Chemical Physics, 2018
We use a combination of machine learning techniques and high-throughput density-functional theory calculations to explore ternary compounds with the AB2C2 composition.
Jonathan Schmidt, Liming Chen, Silvana Botti, Miguel A. L. Marques   +3 more
semanticscholar   +5 more sources

Dissipative stability theory for linear repetitive processes with application in iterative learning control [PDF]

, 2009
This paper develops a new set of necessary and sufficient conditions for the stability of linear repetitive processes, based on a dissipative setting for analysis.
Wojuech Paszke, Paolo Rapisarda, Eric Rogers, M. Steinbuch   +3 more
core   +6 more sources

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Predicting the Thermodynamic Stability of Solids Combining Density Functional Theory and Machine Learning

Chemistry of Materials, 2017
We perform a large scale benchmark of machine learning methods for the prediction of the thermodynamic stability of solids. We start by constructing a data set that comprises density functional theory calculations of around 250000 cubic perovskite systems.
Jonathan Schmidt, Jingming Shi, Pedro Borlido, Limin Chen, S. Botti, M. Marques   +5 more
semanticscholar   +3 more sources

Learning theory: stability is sufficient for generalization and necessary and sufficient for consistency of empirical risk minimization

Advances in Computational Mathematics, 2006
Solutions of learning problems by Empirical Risk Minimization (ERM) – and almost-ERM when the minimizer does not exist – need to be consistent, so that they may be predictive. They also need to be well-posed in the sense of being stable, so that they might be used robustly.
Sayan Mukherjee, Partha Niyogi, Tomaso Poggio, Ryan Rifkin   +3 more
semanticscholar   +4 more sources