Location: Pavillon André-Aisenstadt (UdeM), room AA-3195
Time: Tuesday, October 26, 14:00

Abstract: The need to label, predict, model or otherwise process sequential data arises in many domains. Some obvious examples are speech and handwriting recognition, inferring protein structure from amino acid sequences, and making predictions about the stock market or the weather. Although the tasks and data vary greatly, certain properties seem to be universally beneficial for sequence processing algorithms. In particular the ability to 'remember' the context surrounding each point in the data sequence, and the ability to generalise to distorted versions of sequential patterns appear to be crucial. Recurrent neural networks are a promising architecture in this regard. Furthermore, they can be generalised to treat spatial, as well as temporal, dependencies in a sequential way, expanding the range of applicability to data such as images, video, and medical scans. This talk presents a generic framework for processing spatio-temporal data using recurrent neural networks, and shows how the framework can be tailored to specific tasks.