Quantum machine learningGeneralization error boundNISQ devicesQuantum circuitsDespite the mounting anticipation for the quantum revolution, the success of quantum machine learning (QML) in the noisy intermediate
Despite the mounting anticipation for the quantum revolution, the success of quantum machine learning (QML) in the noisy intermediate-scale quantum (NISQ)
Deep Learning Problems in the Use of Machine-Learning (ML) Technology One of the problems with the use of ML technology like ANNs is the tendency for the solutions to get trapped in a local minimum in the error minimization process. Optimizing the momentum of the neural net is one way to...
for some Hermitian observableOxi,yiloss. As is common in classical learning theory, the prediction error bounds will depend on the largest (absolute) value that the loss function can attain. In our case, we therefore assumeCloss:=supx,y||Ox,yloss||<∞, i.e., the spectral norm can ...
In this problem the learning algo- rithmuses ˆ f (x) = 1 n 1 ∑ n 1 i=1 X i = ¯ X S j as a decision rule and L( ¯ X S j , X i ) =( ¯ X S j −X i ) 2 , X i ∈S c j , the square error loss, as a loss function. Other typical choices of ...
–泛化误差(generalization error):是模型在未知样本上的期望误差。 泛化误差( ):是模型在未知样本上的期望误差 ): … wenku.baidu.com|基于23个网页 2. 泛化错误 接近于泛化错误(generalization error)。这里测试集的比例一般占全部数据的1/4-1/3。
2019 Mathematics of Machine Learning Summer School Statistical Learning V Robert Schapire Microsoft Research video: YouTubeWeak learnerRecall the basic setup in learning problems: assume (x,y)\sim…
There are two different factors to control thegeneralization abilityof theSVMmethod: (1) the error in the training and (2) the capacity of the learning machine. By changing the features in the classifications, the error rate can be controlled. It is clear from the results obtained from the ...
Exploring Empty Spaces: Human-in-the-Loop Data Augmentation March 26, 2025|research areaHuman-Computer Interaction,research areaTools, Platforms, Frameworks|conferenceCHI Data augmentation is crucial to make machine learning models more robust and safe. However, augmenting data can be challenging as it...
Considerations for Distribution Shift Robustness in Health May 2, 2023 | research area Health, research area Methods and Algorithms | conference ICLR *=Equal Contributors This paper was accepted at the workshop "Trustworthy Machine Learning for Healthcare Workshop" at the conference ICLR 2023. When...