Non-linear optimization is essential to many areas of geometry processing research. However, when experimenting with different problem formulations or prototyping new algorithms, a major practical obstacle the need figure out derivatives objective functions, especially second-order are required. Deriving and manually implementing gradients Hessians both time-consuming error-prone. Automatic dif...

Recent advancements in pattern recognition and signal processing concern the automatic learning of data representations from labeled training samples. Typical approaches are based on deep learning and convolutional neural networks, which require large amount of labeled training samples. In this work, we propose novel feature extractors that can be used to learn the representation of single prot...

Today, a great deal of data is generated in the medical field. Acquiring useful knowledge from this raw data requires data processing and detection of meaningful patterns and this objective can be achieved through data mining. Using data mining to diagnose and prognose heart diseases has become one of the areas of interest for researchers in recent years. In this study, the literature on the ap...