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30 de jan. de 2022 · Extremal Random Forests. Classical methods for quantile regression fail in cases where the quantile of interest is extreme and only few or no training data points exceed it. Asymptotic results from extreme value theory can be used to extrapolate beyond the range of the data, and several approaches exist that use linear regression, kernel ...
29 de ago. de 2018 · Extreme Value Theory for Open Set Classification - GPD and GEV Classifiers. Classification tasks usually assume that all possible classes are present during the training phase. This is restrictive if the algorithm is used over a long time and possibly encounters samples from unknown classes. The recently introduced extreme value machine, a ...
29 de abr. de 2021 · EPP-Net: Extreme-Point-Prediction-Based Object Detection. Object detection can be regarded as a pixel clustering task, and its boundary is determined by four extreme points (leftmost, top, rightmost, and bottom). However, most studies focus on the center or corner points of the object, which are actually conditional results of the extreme points.
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17 de jul. de 2023 · Extreme Image Compression using Fine-tuned VQGAN Models. 07/17/2023. ∙. by Qi Mao, et al. ∙. ∙. Recent advances in generative compression methods have demonstrated remarkable progress in enhancing the perceptual quality of compressed data, especially in scenarios with low bitrates. Nevertheless, their efficacy and applicability in ...
15 de fev. de 2020 · Extreme Classification via Adversarial Softmax Approximation. Training a classifier over a large number of classes, known as 'extreme classification', has become a topic of major interest with applications in technology, science, and e-commerce. Traditional softmax regression induces a gradient cost proportional to the number of classes C ...
XGBoost, which stands for eXtreme Gradient Boosting, is an advanced implementation of gradient boosting algorithms. It has gained popularity and attention for its performance in machine learning competitions and its efficiency in solving a wide range of machine learning problems. XGBoost is an open-source library that provides a high ...
8 de jul. de 2019 · In this paper, we develop Physics Informed Extreme Learning Machine (PIELM), a rapid version of PINNs which can be applied to stationary and time dependent linear partial differential equations. We demonstrate that PIELM matches or exceeds the accuracy of PINNs on a range of problems.
