Foula Vagena
April 13, 4 PM
online (zoom)
Abstract
Recurrent neural networks (RNNs) are a family of specialized neural networks for processing sequential data. They can scale to much longer sequences than would be practical for networks without sequence-based specialization and most of them can also process sequences of variable length. In this tutorial we will first describe the high level RNN architecture and outline its most popular variations. We will then explain the main challenge the handling of data sequentail presents, namely long term dependenceis and summarize the different mechanisms that are employed to tackle it (i.e. gated architectures, attention mechanisms). We will go on to describe applications where RNN have been succesfule employed and we will conclude the tutorial with an illustrative RNN-supported timeseries prediction example.
The Hands-On Workshop will focus on RNN supported timeseries prediction.
Dr Foula Vagena
(Université Paris Cité, diiP)
Zografoula Vagena is a research associate at the Data Intelligence Institute of Paris (diiP) and affiliated with the Université Paris Cité. She has been a data science researcher and practitioner for over ten years. She has worked on different analytics problems including forecasting, image processing, graph analytics, multidimensional data analysis, text processing, recommendation systems, sequential data analysis and optimization within various fields such as transportation, healthcare, retail, finance/insurance and accounting. She has also performed research in the intersection of data management and analytics, and was a primary contributor of the MCDB/SimSQL systems that blended data management with Bayesian statistics. She holds a PhD in data management from the University of California, Riverside.
Click the image to see slide
Other seminars
Shen Liang – Knowledge-guided Data Science
Shen LiangMay 18, 4 PM online (zoom) linkedinAbstract This tutorial presents an overview of knowledge-guided data science, a rising methodology in machine learning which fuses data with domain knowledge. We will present numerous case studies on this methodology...
Shen Liang – Deep Transfer Learning and Multi-task Learning
Shen LiangJune 15, 4 PMonline (zoom) linkedinAbstract This tutorial provides an overview of two important and correlated (in many cases intersectional) topics in deep learning: transfer learning, and multi-task learning. Transfer learning focuses on...
Foula Vagena – Ensemble Learning: Theory and Techniques
Foula VagenaDecember 15, 4pmonline (zoom) Abstract Ensemble learning is the process by which multiple models, such as classifiers or experts, are combined to solve a particular computational intelligence problem. Ensemble learning is primarily used to improve...
Foula Vagena – Graph Based Data Science: Opportunities Challenges and Techniques
Foula VagenaJanuary 19, 4pmonline (zoom) Abstract Graph based data science lets us leverage the power of relationships and structure in data to improve model prediction and answer previously intractable questions. In this tutorial we will first introduce the...