Priberam Machine Learning Lunch Seminars (T11) - 3 - "A Biologically Plausible Learning Algorithm for Artificial Neural Networks", Matilde Farinha (IT/IST)
Zita Marinho
Hello all,
Hope you are all safe and healthy, the Priberam Machine Learning Seminars will continue to take place remotely via zoom every 2 weeks on Tuesdays at 1 p.m.
Next Tuesday, March 31st, Matilde Tristany Farinha, an M.Sc. student from IST will present her work on "A Biologically Plausible Learning Algorithm for Artificial Neural Networks" at 13:00h (zoom link: https://zoom.us/j/370783393 ).
You can register for this event and keep watch on future seminars below:
Best regards,
Zita Marinho,
Priberam Labs
http://labs.priberam.com/
Priberam is hiring!
If you are interested in working with us please consult the available positions at priberam.com/careers.
PRIBERAM SEMINARS -- Zoom 370783393
__________________________________________________
Priberam Machine Learning Lunch Seminar
Speaker: Matilde Tristany Farinha (IST)
Venue: https://zoom.us/j/370783393
Date: Tuesday, March 31st, 2020
Time: 13:00
Abstract:
Artificial neural networks, one of the most successful approaches to supervised learning, were originally inspired by their biological counterparts. However, the most successful learning algorithm for artificial neural networks, backpropagation, is considered biologically implausible. Many believe that the next generation of artificial neural networks should be built upon a better understanding of biological learning. So, for decades, neuroscience and machine learning communities have been trying to bridge the gap between biological and artificial learning, taking advantage of the ever-growing amount of data on the brain and its activity. We contribute to the topic of biologically plausible neuronal learning by building upon and extending the equilibrium propagation learning framework, which has been previously proposed as a more biologically plausible alternative to backpropagation. Specifically, we introduce a new neuronal dynamics and learning rule for arbitrary network architectures; a sparsity-inducing method able to prune irrelevant connections; a dynamical-systems characterization of the models, using Lyapunov theory.
Short Bio:
Matilde Tristany is a researcher at GAIPS (Intelligent Agents and Synthetic Characters Group) who is interested in bridging the gap between the biological and the artificial learning frameworks in order to obtain better learning algorithms for biologically plausible artificial neural networks. She obtained an M.Sc. degree in the Mathematics Department of IST, in 2019.
More info: http://labs.priberam.com/Academia-Partnerships/Seminars.aspx
Eventbrite: