**ResNet-RS** is a family of [ResNet](https://paperswithcode.com/method/resnet) architectures that are 1.7x faster than [EfficientNets](https://paperswithcode.com/method/efficientnet) on TPUs, while achieving similar accuracies on ImageNet. The authors propose two new scaling strategies: (1) scale model depth in regimes where overfitting can occur (width scaling is preferable otherwise); (2) increase image resolution more slowly than previously recommended.

Additional improvements include the use of a [cosine learning rate schedule](https://paperswithcode.com/method/cosine-annealing), [label smoothing](https://paperswithcode.com/method/label-smoothing), [stochastic depth](https://paperswithcode.com/method/stochastic-depth), [RandAugment](https://paperswithcode.com/method/randaugment), decreased [weight decay](https://paperswithcode.com/method/weight-decay), [squeeze-and-excitation](https://paperswithcode.com/method/squeeze-and-excitation-block) and the use of the [ResNet-D](https://paperswithcode.com/method/resnet-d) architecture.

The main control tasks in autonomous vehicles are steering (lateral) and speed (longitudinal) control. PID controllers are widely used in the industry because of their simplicity and good performance, but they are difficult to tune and need additional adaptation to control nonlinear systems with varying parameters. In this paper, the longitudinal control task is addressed by implementing adaptive PID control
using two different approaches: Genetic Algorithms (GA-PID) and then Neural Networks (NN-PID) respectively. The vehicle
nonlinear longitudinal dynamics are modeled using Powertrain blockset library. Finally, simulations are performed to assess
and compare the performance of the two controllers subject to external disturbances.

GA-PID/NN-PID

ResNet-RS

Revisiting ResNets: Improved Training and Scaling Strategies

The area under the receiver operating characteristics (ROC) Curve, referred to as AUC, is a well-known performance measure in the supervised learning domain. Due to its compelling features, it has been employed in a number of studies to evaluate and compare the performance of different classifiers. In this work, we explore AUC as a performance measure in the unsupervised learning domain, more specifically, in the context of cluster analysis. In particular, we elaborate on the use of AUC as an internal/relative measure of clustering quality, which we refer to as Area Under the Curve for Clustering (AUCC). We show that the AUCC of a given candidate clustering solution has an expected value under a null model of random clustering solutions, regardless of the size of the dataset and, more importantly, regardless of the number or the (im)balance of clusters under evaluation. In addition, we elaborate on the fact that, in the context of internal/relative clustering validation as we consider, AUCC is actually a linear transformation of the Gamma criterion from Baker and Hubert (1975), for which we also formally derive a theoretical expected value for chance clusterings. We also discuss the computational complexity of these criteria and show that, while an ordinary implementation of Gamma can be computationally prohibitive and impractical for most real applications of cluster analysis, its equivalence with AUCC actually unveils a much more efficient algorithmic procedure. Our theoretical findings are supported by experimental results. These results show that, in addition to an effective and robust quantitative evaluation provided by AUCC, visual inspection of the ROC curves themselves can be useful to further assess a candidate clustering solution from a broader, qualitative perspective as well.

Source	Revisiting ResNets: Improved Training and Scaling Strategies
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com