Model selection is based upon the generalization errors of the models in consideration. To estimate the generalization error of a model from the training data, the method of cross-validation and the asymptotic
Ensemble of averages: Improving model selection and boosting performance in domain generalization 2022 NeurIPS Stability analysis and generalization bounds of adversarial training 2022 NeurIPS The role of permutation invariance in linear mode connectivity of neural networks 2022 ICLR Swad: Domain generali...
However, most model selection criteria that are based on the Kullback鈥揕eibler divergence tend to have reduced performance when the data are contaminated by outliers. In this paper, we derive and investigate a family of criteria that generalize the Akaike information criterion (AIC). When applied...
[2025-02-05] We propose a new data selection method,DaaR, which is theoretically informed, via treating diversity as a reward, achieves better overall performance across 7 benchmarks when post-training SOTA LLMs. See more details inDiversity as a Reward: Fine-Tuning LLMs on a Mixture of ...
and Best Practices in Machine Learning and AI Constantin Aliferis and Gyorgy Simon Abstract Avoiding over and under fitted analyses (OF, UF) and models is critical for ensuring as high generalization performance as possible and is of profound importance for the success of ML/AI ...
In a generalization of this algorithm, the weights are updated by adding the feature vector multiplied by the learning rate, and by the gradient of some loss function (in the specific case described above, the loss is hinge- loss, whose gradient is 1 when it is ...
Chapter 4. Model Training Patterns Machine learning models are usually trained iteratively, and this iterative process is informally called the training loop. In this chapter, we discuss what the … - Selection from Machine Learning Design Patterns [Boo
and produce almost similar accurate predictions, which is referred to asmodel multiplicity[12]. However, the features deemed most important to one model may not be important for another well-performing model [54]. In a scenario involving multiple models and explanations, the selection of a ...
On one hand, an overestimation of this parameter might lead to overfitting and, consequently, to a poor generalization. On the other hand, an underestimation will result in poor predicting performances. Several entropy-based model selection techniques have been proposed in the literature to estimate ...
Selection of the ML model configuration A schematic layout of a typical UED instrument is shown in Fig. 1. The UED instrument has several main components: the photocathode RF gun, the solenoid magnet, the UED sample chamber, and the detector27,28. To build the ML model, an optimal configur...