Results 11 to 20 of about 12,788,874 (304)
Non-adaptive adaptive sampling on turnstile streams [PDF]
Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing, 2020 To appear at STOC ...
Mahabadi, Sepideh +3 more
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AUV Adaptive Sampling Methods: A Review
Applied Sciences, 2019 Autonomous underwater vehicles (AUVs) are unmanned marine robots that have been used for a broad range of oceanographic missions. They are programmed to perform at various levels of autonomy, including autonomous behaviours and intelligent behaviours ...
Jimin Hwang, Neil Bose, Shuangshuang Fan
doaj +2 more sources
A Data-Driven Adaptive Sampling Method Based on Edge Computing
Sensors, 2020 The rise of edge computing has promoted the development of the industrial internet of things (IIoT). Supported by edge computing technology, data acquisition can also support more complex and perfect application requirements in industrial field.
Ping Lou +4 more
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A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks [PDF]
Computer Methods in Applied Mechanics and Engineering, 2022 Physics-informed neural networks (PINNs) have shown to be an effective tool for solving forward and inverse problems of partial differential equations (PDEs).
Chen-Chun Wu +4 more
semanticscholar +1 more source
Adaptive Sampling Methods for Molecular Dynamics in the Era of Machine Learning. [PDF]
Journal of Physical Chemistry B, 2023 Molecular dynamics (MD) simulations are fundamental computational tools for the study of proteins and their free energy landscapes. However, sampling protein conformational changes through MD simulations is challenging due to the relatively long time ...
D. Kleiman, Hassan Nadeem, D. Shukla
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Failure-Informed Adaptive Sampling for PINNs, Part II: Combining with Re-sampling and Subset Simulation [PDF]
Communication on Applied Mathematics and Computation, 2023 This is the second part of our series works on failure-informed adaptive sampling for physic-informed neural networks (PINNs). In our previous work (SIAM J. Sci. Comput.
Zhi-Hao Gao +3 more
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Failure-informed adaptive sampling for PINNs [PDF]
SIAM Journal on Scientific Computing, 2022 Physics-informed neural networks (PINNs) have emerged as an effective technique for solving PDEs in a wide range of domains. It is noticed, however, the performance of PINNs can vary dramatically with different sampling procedures.
Zhiwei Gao, Liang Yan, Tao Zhou
semanticscholar +1 more source
PointASNL: Robust Point Clouds Processing Using Nonlocal Neural Networks With Adaptive Sampling [PDF]
Computer Vision and Pattern Recognition, 2020 Raw point clouds data inevitably contains outliers or noise through acquisition from 3D sensors or reconstruction algorithms. In this paper, we present a novel end-to-end network for robust point clouds processing, named PointASNL, which can deal with ...
Xu Yan +4 more
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AdaNeRF: Adaptive Sampling for Real-time Rendering of Neural Radiance Fields [PDF]
European Conference on Computer Vision, 2022 Novel view synthesis has recently been revolutionized by learning neural radiance fields directly from sparse observations. However, rendering images with this new paradigm is slow due to the fact that an accurate quadrature of the volume rendering ...
A. Kurz +4 more
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Implicitly adaptive importance sampling [PDF]
Statistics and Computing, 2021 AbstractAdaptive importance sampling is a class of techniques for finding good proposal distributions for importance sampling. Often the proposal distributions are standard probability distributions whose parameters are adapted based on the mismatch between the current proposal and a target distribution.
Topi Paananen +3 more
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