IEM Distinguished Lecturers Seminar: Frugality in machine learning: sparsity, a value for the future?
The seminar will take place in ELA 1 and will be simultaneously broadcasted in Neuchâtel Campus MC B1 273.
Coffee and cookies will be served from 13:00.
Abstract
Sparse vectors and sparse matrices play a transerve role in signal and image processing: they have led to succesful approaches efficiently addressing tasks as diverse as data compression, fast transforms, signal denoising and source separation, or more generally inverse problems.
To what extent can the potential of sparsity be also leveraged to achieve more frugal (deep) learning techniques?
Through an overview of recent explorations around this theme, I will compare and contrast classical sparse regularization for inverse problems with its natural extensions that aim at learning neural networks with sparse connections. During our journey, I will notably highlight the role of rescaling-invariances of modern deep parameterizations, which come with their curses and blessings.
Short biography
Rémi Gribonval is a Research Director (Directeur de Recherche) with Inria and the head of the OCKHAM research group of Laboratoire de l’Informatique du Parallélisme at École Normale Supérieure de Lyon. He is the former scientific leader of the PANAMA research group on sparse audio processing at IRISA, Rennes, France. In 2011, he was awarded the Blaise Pascal Award of the GAMNI-SMAI by the French Academy of Sciences, and a starting investigator grant from the European Research Council in 2011. He is an IEEE fellow and a EURASIP Fellow. He founded the series of international workshops SPARS on Signal Processing with Adaptive/Sparse Representations.
Coffee and cookies will be served from 13:00.
Abstract
Sparse vectors and sparse matrices play a transerve role in signal and image processing: they have led to succesful approaches efficiently addressing tasks as diverse as data compression, fast transforms, signal denoising and source separation, or more generally inverse problems.
To what extent can the potential of sparsity be also leveraged to achieve more frugal (deep) learning techniques?
Through an overview of recent explorations around this theme, I will compare and contrast classical sparse regularization for inverse problems with its natural extensions that aim at learning neural networks with sparse connections. During our journey, I will notably highlight the role of rescaling-invariances of modern deep parameterizations, which come with their curses and blessings.
Short biography
Rémi Gribonval is a Research Director (Directeur de Recherche) with Inria and the head of the OCKHAM research group of Laboratoire de l’Informatique du Parallélisme at École Normale Supérieure de Lyon. He is the former scientific leader of the PANAMA research group on sparse audio processing at IRISA, Rennes, France. In 2011, he was awarded the Blaise Pascal Award of the GAMNI-SMAI by the French Academy of Sciences, and a starting investigator grant from the European Research Council in 2011. He is an IEEE fellow and a EURASIP Fellow. He founded the series of international workshops SPARS on Signal Processing with Adaptive/Sparse Representations.
Practical information
- General public
- Free