3.获取变量间的条件依赖关系。如p(x2|x1)可用来判别或回归，属于conditional likelihood estimation。4....
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Machine learningMetaheuristic optimizationNaive bayes classificationNeural networksSupport vector machinesThis paper presents a novel Feature Wise Normalization approach for the effective normalization of data. In this approach, each feature is normalized independently with one of the methods from the pool of...
Normalizing flows in PyTorch deep-learningprobabilitytorchgenerative-modeldensity-estimationnormalizing-flows UpdatedOct 13, 2024 Python An extension of LightGBM to probabilistic modelling machine-learninglightgbmgamlssuncertainty-estimationmixture-density-modelnormalizing-flowsprediction-intervalsprobabilistic-forecastingdi...
Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data generative-modelenergy-based-modelporous-media-flownormalizing-flowphysics-constrainedreverse-kld UpdatedOct 18, 2019 Python hongyehu/RG-Flow ...
If this raw data is inputted in our machine learning model, slow convergence will occur. As illustrated left, the steepest gradient is searched, which is somewhat in the correct direction but also possesses quite a large oscillation part. This can be explained by reasoning about t...
row 7~8: 作者猜想加入NF带来的提升是因为Normalizing Flows能够更好地捕捉到多模态的非高斯后验分布,为了验证这一点,作者通过句子级知识蒸馏的方式为每个原句增加了多个翻译(original+distilled data)然后在增强后的数据集上训练模型。Transformer baseline和VNMT+NF均受益于蒸馏后的数据,但VNMT+NF取得的收益更明显...
It becomes especially important when training machine learning models, which can amplify simulation inaccuracies and introduce large discrepancies and systematic uncertainties when the model is applied to data. In this paper, we introduce a method to transform simulated events to better match data using...
Deep Learning for Siri’s Voice: On-device Deep Mixture Density Networks for Hybrid Unit Selection Synthesis Siri is a personal assistant that communicates using speech synthesis. Starting in iOS 10 and continuing with new features in iOS 11, we base Siri voices on deep learning. The resulting ...
NORMALIZING LOCATION IDENTIFIERS FOR PROCESSING IN MACHINE LEARNING ALGORITHMSThe disclosure is related to calculating a relative distance between a first node and a second node in a wireless network. An aspect of the disclosure includes detecting a plurality of transitions of a user device from the ...