Machine Learning Seminars by SimulaMet
SimulaMet has the pleasure to invite everyone to the monthly Machine Learning Seminar Series. The seminars are organized by Machine Intelligence Department at SimulaMet.
The seminars are open for everyone and provide a great opportunity to meet and discuss with renowned international and national experts in the field, as well as colleagues interested in machine learning research.
Next seminar 18 February: Tradeoffs between Speed and Accuracy in Inverse Problems
Solving a linear system of the type Ax + n = y with many more unknowns than equations is a fundamental ingredient in a plethora of applications.
The classical approach to this inverse problem is by formulating an optimization problem consisting a data fidelity term and a signal prior, and minimizing it using an iterative algorithm. Imagine we have a wonderful iterative algorithm but real-time limitations allows only to execute five iterations thereof. Will it achieve the best accuracy within this budget?
Imagine another setting in which an iterative algorithm pursues a certain data model, which is known to be accurate only to a certain amount. Can this knowledge be used to design faster iterations?
In this talk, Professor Alexander Bronstein will try to answer these questions by showing how the introduction of smartly controlled inaccuracy can significantly increase the convergence speed of iterative algorithms used to solve various inverse problems.
He will also elucidate connections to deep learning an provide a theoretical justification of the very successful LISTA networks. Examples of applications in computational imaging and audio processing will be provided.
Speaker: Alexander Bronstein, Professor at Tel Aviv University, Israel
Join our e-mail group MLSeminars_at_SimulaMet or visit our website for more information about the upcoming seminars.
For more information, please contact Valeriya Naumova.