Designing a controller for autonomous vehicles capable of providing adequate performance
in all driving scenarios is challenging due to the highly complex environment and inability to …

In the last few years, deep learning has led to very good performance on a variety of
problems, such as visual recognition, speech recognition and natural language processing …

It is common practice in deep learning to use overparameterized networks and train for as
long as possible; there are numerous studies that show, both theoretically and empirically …

Abstract Machine learning algorithms can be trained to estimate age from brain structural
MRI. The difference between an individual's predicted and chronological age, predicted age …

We consider the prediction of interfaces between proteins, a challenging problem with
important applications in drug discovery and design, and examine the performance of …

In this paper, we propose Patience-based Early Exit, a straightforward yet effective inference
method that can be used as a plug-and-play technique to simultaneously improve the …

Most bias correction algorithms used in climatology, for example quantile mapping, are
applied to univariate time series. They neglect the dependence between different variables …

Generalization performance is a central goal in machine learning, particularly when learning
representations with large neural networks. A common strategy to improve generalization …

Many problems in machine learning involve bilevel optimization (BLO), including
hyperparameter optimization, meta-learning, and dataset distillation. Bilevel problems …

Improving forecasting especially time series forecasting accuracy is an important yet often
difficult task facing decision makers in many areas. Both theoretical and empirical findings …