Machine Learning is an increasingly important technology dealing with the growing
complexity of the digitalised world. Despite the fact, that we live in a 'Big data'world where …

Learning curves provide insight into the dependence of a learner's generalization
performance on the training set size. This important tool can be used for model selection, to …

Analyzing scenes thoroughly is crucial for mobile robots acting in different environments.
Semantic segmentation can enhance various subsequent tasks, such as (semantically …

In this work, we perform a wide variety of experiments with different deep learning
architectures on datasets of limited size. According to our study, we show that model …

Recent years have seen the advent of molecular simulation datasets that are orders of
magnitude larger and more diverse. These new datasets differ substantially in four aspects …

Learning curves are a concept from social sciences that has been adopted in the context of
machine learning to assess the performance of a learning algorithm with respect to a certain …

We propose a new framework for reasoning about generalization in deep learning. The core
idea is to couple the Real World, where optimizers take stochastic gradient steps on the …

Solving image classification tasks given small training datasets remains an open challenge
for modern computer vision. Aggressive data augmentation and generative models are …

Image classification with small datasets has been an active research area in the recent past.
However, as research in this scope is still in its infancy, two key ingredients are missing for …

The optimal mode for ultrasonic welding (USW) of the “PEEK–ED (PEEK)–prepreg (PEI
impregnated CF fabric)–ED (PEEK)–PEEK” lap joint was determined by artificial neural …