Results 21 to 30 of about 12,438,139 (288)
Robust Regression via Model Based Methods [PDF]
, 2021 The mean squared error loss is widely used in many applications, including auto-encoders, multi-target regression, and matrix factorization, to name a few. Despite computational advantages due to its differentiability, it is not robust to outliers. In contrast, l_p norms are known to be robust, but cannot be optimized via, e.g., stochastic gradient ...
Armin Moharrer +3 more
openaire +2 more sources
OpenFOAM-avalanche 2312: depth-integrated models beyond dense-flow avalanches [PDF]
Geoscientific Model DevelopmentNumerical simulations have become an important tool for the estimation and mitigation of gravitational mass flows, such as avalanches, landslides, pyroclastic flows, and turbidity currents.
M. Rauter, M. Rauter, J. Kowalski
doaj +1 more source
Numerical methods for a Kohn-Sham density functional model based on optimal transport [PDF]
, 2014 In this paper, we study numerical discretizations to solve density functional models in the "strictly correlated electrons" (SCE) framework. Unlike previous studies our work is not restricted to radially symmetric densities.
Chen, Huajie +2 more
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A state-of-the-art review on wind power converter fault diagnosis
Energy Reports, 2022 The rapid expansion of installed wind energy capacity and the continuous development of wind turbine technology has drawn attention to operation and maintenance issues.
Jinping Liang +4 more
doaj +1 more source
A Real-Time Model-Based Reinforcement Learning Architecture for Robot Control [PDF]
, 2011 Reinforcement Learning (RL) is a method for learning decision-making tasks that could enable robots to learn and adapt to their situation on-line. For an RL algorithm to be practical for robotic control tasks, it must learn in very few actions, while ...
Hester, Todd +2 more
core +3 more sources
Comparing policy gradient and value function based reinforcement learning methods in simulated electrical power trade [PDF]
, 2012 In electrical power engineering, reinforcement learning algorithms can be used to model the strategies of electricity market participants. However, traditional value function based reinforcement learning algorithms suffer from convergence issues when ...
Burt, Graeme +3 more
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Model Reduction by Moment Matching for Linear Switched Systems [PDF]
, 2014 Two moment-matching methods for model reduction of linear switched systems (LSSs) are presented. The methods are similar to the Krylov subspace methods used for moment matching for linear systems.
Bastug, Mert +3 more
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A Review of Root Zone Soil Moisture Estimation Methods Based on Remote Sensing
Remote Sensing, 2023 Root zone soil moisture (RZSM) controls vegetation transpiration and hydraulic distribution processes and plays a key role in energy and water exchange between land surface and atmosphere; hence, accurate estimation of RZSM is crucial for agricultural ...
Ming Li, Hongquan Sun, Ruxin Zhao
doaj +1 more source
IEEE Access, 2018 In this paper, we propose a collaborative filtering method designed to improve the current memory-based prediction times without worsening and even improving the existing accuracy results.
Bo Zhu +3 more
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, 2020 Model-agnostic interpretation techniques allow us to explain the behavior of any predictive model. Due to different notations and terminology, it is difficult to see how they are related. A unified view on these methods has been missing.
A Goldstein +11 more
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