A classifier-guided sampling (CGS) method is introduced for solving engineering design optimization problems with discrete and/or continuous variables and continuous and/or discontinuous responses. The method merges concepts from metamodel-guided sampling and population-based optimization algorithms. The CGS...
采样sampling 的过程(生成过程)为:将有噪声的图像(第一张图像为随机采样的高斯分布噪声)减去模型预测的噪声(噪声前面的其它参数可以由上面加噪的过程反向推导出来)不断把噪声去掉以恢复出原始的图像。 方差项也可以由模型来预测。 参考文献:Improved Denoising Diffusion Probabilistic Models 引导扩散模型(Guided Diffusion...
LGBMClassifier stands for Light Gradient Boosting Machine Classifier. It uses decision tree algorithms for ranking, classification, and other machine-learning tasks. LGBMClassifier uses a novel technique of Gradient-based One-Side Sampling (GOSS) and Exclusive Feature Bundling (EFB) to handle large-sca...
Same random seeds used for sampling in each subfigure. Classifier-free guidance on 128x128 ImageNet. Left: non-guided samples, right: guided samples with w = 3.0. Interestingly, strongly guided samples such as these display saturated colors. 参考资料 【1】Classifier-Free Diffusion Guidance. 相关...
introduced the Multi-Class Radial-Based Oversampling (MC-RBO) approach (Krawczyk et al., 2020). This method generates artificial instances using a latent function, guided by exploring regions with minimal between-class distribution values. MC-RBO effectively handles difficult data distributions and ...
Empirical studies have been taken to compare the effect of over-sampling, under-sampling and threshold-moving by using several base classifiers [3], [4], [5], [6], [7], [8], [9]. Ensemble methods, such as Bagging and boosting, are applied frequently in this field and good results ...
Our exhaled breath collection method was guided by referring to the previous studies15,29. Participants underwent overnight fasting for at least 8 hours and rest in a well-ventilated, separate room for at least 10 minutes before breath sampling in the morning. Subjects were required to have...
(Cz) and recorded at a sampling rate of 250 Hz; also, data acquisition was started only after all the scalp electrode impedances were confirmed to be below 50\(k\Omega\)86, in keeping with the recommendations for the EGI Net Amps 300 high-impedance amplifier87. To eliminate any ...
To estimate performance of a model which did not learn anything, we generate random saliency maps whose pixel values were obtained by uniformly sampling values in the range of [0,1]. We summarize the localization performance of GMIC in Table 4. While GMIC underperformed U-Net, it achieved ...
First, an ensemble SVM classifier exploits the information of the entire dataset, while random under-sampling uses only part of the dataset; On the other hand, it consumes less computation compared to random over-sampling. Second, an ensemble SVM classifier is able to overcome some drawbacks of...