Several image processing tasks, such as classification and object detection, have been significantly improved using Convolutional Neural Networks (CNN). Like ResNet EfficientNet, many architectures achieved outstanding results in at least one dataset by the time of their creation. A critical factor training concerns network's regularization, which prevents structure from overfitting. This work ...

Overfitting is one of the fundamental challenges when training convolutional neural networks and usually identified by a diverging test loss. The underlying dynamics how flow activations induce overfitting however poorly understood. In this study we introduce perplexity-based sparsity definition to derive visualise layer-wise activation measures. These novel explainable AI strategies reveal sur...

Adversarial training is widely used to improve the robustness of deep neural networks adversarial attack. However, prone overfitting, and cause far from clear. This work sheds light on mechanisms underlying overfitting through analyzing loss landscape w.r.t. input. We find that robust results standard training, specifically minimization clean loss, can be mitigated by regularization gradients. ...

This chapter explores the issue of overfitting in grammar-based Genetic Programming. Tools such as Genetic Programming are well suited to problems in finance where we seek to learn or induce a model from the data. Models that overfit the data upon which they are trained prevent model generalisation, which is an important goal of learning algorithms. Early stopping is a technique that is frequen...

There is an increasing interest in categorizing texts using learning algorithms. While the majority of approaches rely on learning linear classifiers, there is also some interest in describing document categories by text patterns. We introduce a model for learning patterns for text categorization (the LPT-model) that does not rely on an attribute-value representation of documents but represents...

Motivation: A popular approach for predicting RNA secondary structure is the thermodynamic nearest neighbor model that finds a thermodynamically most stable secondary structure with the minimum free energy (MFE). For further improvement, an alternative approach that is based on machine learning techniques has been developed. The machine learning based approach can employ a fine-grained model th...