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A Deep Learning-Based Approach to Uncertainty Quantification for Polysilicon MEMS
2021
Engineering Proceedings
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A complexity in the mentioned model is represented by the need to assess the stochastic (local) stiffness of polysilicon, depending on its unknown (local) microstructure. ...
This property of the model looks to indeed be necessary to prove the generalization capability of the learning process, and to next feed Monte Carlo simulations resting on the model of the entire device ...
A complexity in the mentioned model is represented by the need to assess the stochastic (local) stiffness of polysilicon, depending on its unknown (local) microstructure. ...
doi:10.3390/micromachines2021-09556
fatcat:kjqtgefr7jdh7bxl3zhvjwhrle
Related Work on Image Quality Assessment
[article]
2022
arXiv
pre-print
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2021 pre-print arXiv:2111.06291v1 fatcat:a3wrhqws7bg5thadvagtqvwl3q
Due to the existence of quality degradations introduced in various stages of visual signal acquisition, compression, transmission and display, image quality assessment (IQA) plays a vital role in image-based ...
This article will review the state-of-the-art image quality assessment algorithms. ...
However, the BIQA models learned by opinion-aware methods often have weak generalization capability, hereby limiting their usability in practice. Zhang L et al. ...
arXiv:2111.06291v2
fatcat:bcmfvfz2x5e4jitgzxj5t3fqyy
DeepVS: An Efficient and Generic Approach for Source Code Modeling Usage
[article]
2019
arXiv
pre-print
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In this work, we propose a novel general framework that combines cloud computing and deep learning in an integrated development environment (IDE) to assist software developers in various source code modeling ...
The DeepVS tool is capable of providing source code suggestions instantly in an IDE by using a pre-trained source code model. ...
Conclusion In this work, we propose a novel general framework which helps in using machine or deep learning-based source code models for various software engineering tasks. ...
arXiv:1910.06500v1
fatcat:iphm5icpvfhi3o2ssaxqo7hf2q
Tide Predictions in Shelf and Coastal Waters: Status and Prospects
[chapter]
2010
Coastal Altimetry
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As a rough assessment of current capabilities, Table I gives the rms differences (in cm) between both our deep-ocean and shallow-water test stations and the altimeter-based model GOTOO.2,an update to ...
'r Table 1 emphasizes the differences in our capabilities for 40 "'~tidal prediction in deep versus shallow water. ...
doi:10.1007/978-3-642-12796-0_8
fatcat:5pielkeckneqdesdpvdktppgfe
Ensemble Learning Based on Policy Optimization Neural Networks for Capability Assessment
2021
Sensors
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Capability assessment plays a crucial role in the demonstration and construction of equipment. ...
To improve the accuracy and stability of capability assessment, we study the neural network learning algorithms in the field of capability assessment and index sensitivity. ...
Use the ensemble method to construct the equipment capability assessment model, as shown in Figure 4 . ...
doi:10.3390/s21175802
pmid:34502693
pmcid:PMC8434125
fatcat:d532mkmzgfbnnpzb7y6ryabm74
Configuration and intercomparison of deep learning neural models for statistical downscaling
2020
Geoscientific Model Development
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In this paper we undertake a comprehensive assessment of deep learning techniques for continental-scale statistical downscaling, building on the VALUE validation framework. ...
As a result, these models are usually seen as black boxes, generating distrust among the climate community, particularly in climate change applications. ...
They also acknowledge the E-OBS dataset from the EU-FP6 project UERRA (http://www.uerra.eu, last access: 23 April 2020) and the Copernicus Climate Change Service, and the data providers in the ECA&D project ...
doi:10.5194/gmd-13-2109-2020
fatcat:kvur5zfog5aqzap7lqlmft6rdm
Stepwise Feature Fusion: Local Guides Global
[article]
2022
arXiv
pre-print
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The SSFormer achieves statet-of-the-art performance in both learning and generalization assessment. ...
To address this, we propose a new State-Of-The-Art model for medical image segmentation, the SSFormer, which uses a pyramid Transformer encoder to improve the generalization ability of models. ...
Acknowledgments This work was supported by the Key Program Special Fund in XJTLU (KSF-A-22). ...
arXiv:2203.03635v1
fatcat:36oge5kua5b7lfplfpnu64kzky
Explainable Artificial Intelligence for Process Mining: A General Overview and Application of a Novel Local Explanation Approach for Predictive Process Monitoring
[article]
2020
arXiv
pre-print
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2020 pre-print arXiv:2009.02098v1 fatcat:hhp24epfpbgxnixw7zntfna5qu
The generated local explanations are also visualized and presented with relevant evaluation measures that are expected to increase the users' trust in the black-box-model. ...
to facilitate the domain experts in justifying the model decisions. ...
Acknowledgment This research was funded in part by the German Federal Ministry of Education and Research under grant number 01IS18021B (project MES4SME) and 01IS19082A (project KOSMOX). ...
arXiv:2009.02098v2
fatcat:7nf3r24e5redrbnnlayez2t5ke
XDeep: An Interpretation Tool for Deep Neural Networks
[article]
2019
arXiv
pre-print
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With the well-documented API designed in XDeep, end-users can easily obtain the interpretations for their deep models at hand with several lines of codes, and compare the results among different algorithms ...
From the functionality perspective, XDeep integrates a wide range of interpretation algorithms from the state-of-the-arts, covering different types of methodologies, and is capable of providing both local ...
Package Properties In order to effectively bridge the gap between human developers and deep models in practice, XDeep focuses on the following three aspects in generating interpretations for DNN. ...
arXiv:1911.01005v1
fatcat:b5rk5jkq4be4hazuofj5ye7egy
Image data assessment approach for deep learning-based metal surface defect-detection systems
2021
IEEE Access
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ACKNOWLEDGMENT This work was supported in part by the Ministry of Science and Technology under Grant MOST 108-2221-E-027-115-MY3. Special thanks go to B. Y. Lin for preparing the experimental work. ...
that is commonly used in deep learning models. ...
weight coefficients for the assessment of model recognition capabilities. ...
doi:10.1109/access.2021.3068256
fatcat:5nhgnxuwqjhrrpsgayygcu5xb4
Evaluating Explanation Without Ground Truth in Interpretable Machine Learning
[article]
2019
arXiv
pre-print
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Having a sense of explanation quality not only matters for assessing system boundaries, but also helps to realize the true benefits to human users in practical settings. ...
However, due to the diversified scenarios and subjective nature of explanations, we rarely have the ground truth for benchmark evaluation in IML on the quality of generated explanations. ...
deep teacher model. ...
arXiv:1907.06831v2
fatcat:ia7bclmeufe77gkpdwl3zqyjky
Power System Resiliency and Wide Area Control Employing Deep Learning Algorithm
2021
Computers Materials & Continua
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Resilience assessment based on failure probability, financial impact, and time-series data in grid failure management determine the norm H 2 . ...
The obtained results validate the proposed deep learning algorithm's efficiency on damping inter-area and local oscillation on the 2-channel attack as well. ...
Moreover, the simulation results have highlighted the proposed deep learning algorithm's capability to assess and quantify the resilience of power systems on various attacks. ...
doi:10.32604/cmc.2021.015128
fatcat:fiee64d7ira37gcnlpvxiopocq
Visual Mechanisms Inspired Efficient Transformers for Image and Video Quality Assessment
[article]
2022
arXiv
pre-print
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Perceptual mechanisms in the human visual system (HVS) play a crucial role in generation of quality perception. ...
Such module can represent appropriate perceptual mechanisms in image quality assessment (IQA) to build an accurate IQA model. ...
work to evaluate the generalization capabilities of different models. ...
arXiv:2203.14557v2
fatcat:cmx7scbfqbhslhhnlkj6sst6v4
Assessing cognitive mental workload via EEG signals and an ensemble deep learning classifier based on denoising autoencoders
2019
Computers in Biology and Medicine
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To determine personalized properties in high dimensional EEG indicators, we introduce a feature mapping layer in stacked denoising autoencoder (SDAE) that is capable of preserving the local information ...
To estimate the reliability and cognitive states of operator performance in a human-machine collaborative environment, we propose a novel human mental workload (MW) recognizer based on deep learning principles ...
However, there are two issues that limit the generalization capability for implementing a deep learning model. ...
doi:10.1016/j.compbiomed.2019.04.034
pmid:31059900
fatcat:jmcwu4s5prebtgpblqqajda6ja
Deep Learning for Image Search and Retrieval in Large Remote Sensing Archives
[article]
2020
arXiv
pre-print
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Then, we focus our attention on the advances in RS CBIR systems for which deep learning (DL) models are at the forefront. ...
After discussing their strengths and limitations, we present the deep hashing based CBIR systems that have high time-efficient search capability within huge data archives. ...
The use of aggregated deep local features for RS image retrieval is proposed in [20] . ...
arXiv:2004.01613v2
fatcat:d4fjt3vzybbbrejxzobaluqsoq
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