ELLIS Delft Research theme - Deep Learning & Representation Learning
Topics: graph representation learning, sequence representation learning, spiking networks
In recent years, significant breakthroughs in deep learning have hailed a new era in machine learning with unprecedented performance improvements. This has led to many initiatives to integrate previously understood domain knowledge (e.g. computer vision, speech processing, multimodal fusion, graph theory etc.) into deep learning systems. Aside from focusing on settings where large labelled datasets are available, this theme also tackles machine learning problems when labelled data are not available. Many important problems exist that do not have resources to collect vast datasets and therefore to train end-to-end models. Fortunately representation learning studies how patterns in unlabelled data can also be exploited. This brings about huge benefits for transferring knowledge between problems. In this theme, many different sources of data are considered from traditional audio-visual modalities, dna sequences, or event cameras. We investigate all different forms of network including properties of spiking neural networks. Finally, the growth of this field has also led to challenges in verifying the quality of research works put in the public domain. We value the importance of reproducible research.