Results 11 to 20 of about 1,988,212 (277)
Educazione Linguistica Language Education, 2019 Data-Driven Learning (DDL) is almost non-existent at the university level in Serbia when it comes to using DDL in foreign language teaching. Having analysed the curricula at a number of universities, we concluded that DDL is dealt with on a very small
Vitaz, Milica, Poletanović, Milica
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Data-driven Economic NMPC using Reinforcement Learning [PDF]
IEEE Transactions on Automatic Control, 2019 Reinforcement Learning (RL) is a powerful tool to perform data-driven optimal control without relying on a model of the system. However, RL struggles to provide hard guarantees on the behavior of the resulting control scheme. In contrast, Nonlinear Model
Gros, Sébastien, Zanon, Mario
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Hyperactive learning for data-driven interatomic potentials
npj Computational Materials, 2023 Data-driven interatomic potentials have emerged as a powerful tool for approximating ab initio potential energy surfaces. The most time-consuming step in creating these interatomic potentials is typically the generation of a suitable training database ...
Cas van der Oord +4 more
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Data-Driven Learning of Nonautonomous Systems [PDF]
SIAM Journal on Scientific Computing, 2021 We present a numerical framework for recovering unknown non-autonomous dynamical systems with time-dependent inputs. To circumvent the difficulty presented by the non-autonomous nature of the system, our method transforms the solution state into piecewise integration of the system over a discrete set of time instances.
Tong Qin +3 more
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Data-Driven Learning of Q-Matrix. [PDF]
Appl Psychol Meas, 2012 The recent surge of interests in cognitive assessment has led to developments of novel statistical models for diagnostic classification. Central to many such models is the well-known Q-matrix, which specifies the item–attribute relationships. This article proposes a data-driven approach to identification of the Q-matrix and estimation of related model
Liu J, Xu G, Ying Z.
europepmc +4 more sources
Data-driven invariant learning for probabilistic programs
Formal Methods in System Design, 2022 AbstractMorgan and McIver’s weakest pre-expectation framework is one of the most well-established methods for deductive verification of probabilistic programs. Roughly, the idea is to generalize binary state assertions to real-valued expectations, which can measure expected values of probabilistic program quantities.
Jialu Bao +4 more
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Data-driven planning via imitation learning [PDF]
The International Journal of Robotics Research, 2018 Robot planning is the process of selecting a sequence of actions that optimize for a task=specific objective. For instance, the objective for a navigation task would be to find collision-free paths, whereas the objective for an exploration task would be to map unknown areas.
Choudhury, Sanjiban +6 more
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Bias learning improves data driven models for streamflow prediction
Journal of Hydrology: Regional Studies, 2023 Study region: Andun river basin of Southern China and 273 watersheds across the continental United States. Study focus: Owing to the data incompleteness and the changing environment, there is bias existing in the data driven models for streamflow ...
Yongen Lin +8 more
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Data-Driven Learning for Data Rights, Data Pricing, and Privacy Computing
Engineering, 2023 In recent years, data has become one of the most important resources in the digital economy. Unlike traditional resources, the digital nature of data makes it difficult to value and contract.
Jimin Xu +13 more
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Performance-oriented model learning for data-driven MPC design [PDF]
, 2019 Model Predictive Control (MPC) is an enabling technology in applications requiring controlling physical processes in an optimized way under constraints on inputs and outputs.
Bemporad, Alberto +3 more
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