Machine learning is increasingly used to discover diagnostic and prognostic biomarkers from high-dimensional molecular data. However, a variety of factors related to experimental design may affect the ability to
when it relies on a small number of data point. See support vectors By using several algorithm in order to make the good decision is also a good solution to avoid over-fitting. In order to avoid overfitting in a algorithm, it is necessary to use additional techniques on parameters: Re...
In order to compare learning algorithms, experimental results reported in the machine learning literature often use statistical tests of significance to su
we'd train on the training data and evaluate on the test data, using the evaluation results on test data to guide choices of and changes to various model hyperparameters like learning rate and features. Is there anything wrong with this approach?
Hybrid modelling reduces the misspecification of expert physical models with a machine learning (ML) component learned from data. Similarly to many ML algorithms, hybrid model performance guarantees are limited to the training distribution. To address this limitation, here we introduce a hybrid data au...
To combat this, the researchers propose using data augmentation to prevent the tools from learning from spurious correlations. “We found that we can greatly improve the robustness of these text models across different settings by making them less sensitive to changes in writing ...
论文利用corrupted label和shuffled pixel等对比实验，测试了几种不同的regularization techniques, 诸如data ...
Active learning differs from “learning from examples” in that the learning algorithm assumes at least some control over what part of the input
Despite the mounting anticipation for the quantum revolution, the success of quantum machine learning (QML) in the noisy intermediate-scale quantum (NISQ)
Paper tables with annotated results for Exploring the Design of Adaptation Protocols for Improved Generalization and Machine Learning Safety