REMINDER: TOMORROW JUNE 8TH AT 4 PM
----------------------------------------------------------------------
Please find below the information for the next Colloquium Patavinum at
the Math Department of the University of Padova.
https://www.math.unipd.it/news/the-curse-and-blessing-of-learning-from-non-static-datasetsWe look forward for your participation!
The Colloquia Committee (F.Ancona, L.Ballan, L.Caravenna, R.Colpi, A.Iovita, F.Ranzato, O.Tommasi)
https://www.math.unipd.it/~ranzato/colloquia/colloquia.html
-------- LECTURER
Marc'Aurelio Ranzato, Facebook AI Research Lab, New York
-------- WHEN
June 8th, 2021 - From 4:00 pm to 5:00 pm
-------- WHERE
Online: https://unipd.link/Zoom-Colloquia-Patavina
-------- TITLE
The curse and blessing of learning from non static datasets
-------- ABSTRACT
In the classical empirical risk minimization framework of machine learning,
learners observe samples from a dataset all at once. In practice, datasets
are seldom a static object and data arrives a little bit at the time instead.
New chunks of data may be added over time, and their distribution may be even
non-stationary, as it is typical in robotics applications, for instance.
This new learning setting is clearly more difficult to characterize, but it
offers an unprecedented opportunity to reduce sample complexity. By leveraging
knowledge acquired over the previous chunks of data, the learner has the
potential to more quickly adapt to the new incoming data. Such a learning
setting is related to continual learning and anytime learning, subfields of
machine learning which have seen a recent surge of interest from the research
community interested in learning with limited supervision.
In this talk, I will formalize this learning setting, propose metrics and
benchmarks to test the ability of learning algorithms to transfer knowledge
acquired in the past. I will also introduce a modular architecture that has
proven to be very effective and efficient across a variety of data streams.
This is a hierarchical mixture of experts that adds new experts over time
to automatically adjust its capacity as more and more data is observed. These
promising results indicate that it might be possible to efficiently leverage
past experience to reduce the amount of supervision needed to learn a new task
and that non-static models can be highly effective at learning from non-static
datasets, opening a new and exciting avenue of research.
-------- SHORT BIO SPEAKER
Marc’Aurelio Ranzato is a research scientist at the Facebook AI Research
lab in New York City. His research interests are in the area of unsupervised
learning, continual learning and transfer learning, with applications to vision,
natural language understanding and speech recognition. Marc’Aurelio is
originally from Padova in Italy, where he graduated in Electronics Engineering.
Marc’Aurelio has earned a PhD in Computer Science at New York University under
Yann LeCun’s supervision. After a post-doc with Geoffrey Hinton at University
of Toronto, he joined the Google Brain team in 2011. In 2013 he joined Facebook
and was a founding member of the Facebook AI Research lab. Marc’Aurelio has
served as program chair for ICLR 2017, ICLR 2018 and NeurIPS 2020.
He is the general chair of NeurIPS 2021.
More infos are available here:
https://ranzato.github.io/