Connectionists: ICBINB Monthly Seminar Series Talk: Sebastian Nowozin (Mar 3rd 10am EST)

[Francisco J. Rodríguez Ruiz]

Dear all,

We’re very excited to host *Sebastian Nowozin** (Microsoft Research)* for
the second talk of the newly created* “I Can’t Believe It’s Not Better!”
(ICBINB) virtual seminar series*. More details about this series are below.

The *"I Can't Believe It's Not Better!" (ICBINB) monthly online seminar
series* seeks to shine a light on the "stuck" phase of research. Speakers
will tell us about their most beautiful ideas that didn't "work", about
when theory didn't match practice, or perhaps just when the going got
tough. These talks will let us peek inside the file drawer of unexpected
results and peer behind the curtain to see the real story of *how real
researchers did real research*.

*When: *March 3rd, 2022 at 10am EST / 4pm CET

*Where: *RSVP for the Zoom link here:
https://us02web.zoom.us/meeting/register/tZcucuCvqz4tGta1B5lCos-JGiA5YsZIyD6p

*Title:* *I Can’t Believe Bayesian Deep Learning is not Better*

*Abstract:* *Bayesian deep learning is seductive: it combines the
simplicity, coherence, and beauty of the Bayesian approach to problem
solving together with the expressivity and compositional flexibility of
deep neural networks. Yes, inference can be challenging, but the promises
of improved uncertainty quantification, better out-of-distribution
behaviour, and improved sample efficiency are worth it. Or is it? In this
talk I will tell a personal story of being seduced by, then frustrated
with, and now recovering from Bayesian deep learning. I will present the
context of our work on the cold posterior effect, (Wenzel et al., 2020) and
it’s main findings, as well as some more recent work that tries to explain
the effect. I will also offer some personal reflections on research
practice and narratives that contributed to the lack of progress in
Bayesian deep learning.*

*Bio: **Sebastian Nowozin is a deep learning researcher at Microsoft
Research Cambridge, UK, where he currently leads the Machine Intelligence
research theme.  His research interests are in probabilistic deep learning
and applications of machine learning models to real-world problems. He
completed his PhD in 2009 at the Max Planck Institute in Tübingen, and has
since worked on domains as varied as computer vision, computational
imaging, cloud-based machine learning, and approximate inference.*

*--*

For more information and for ways to get involved, please visit us at
http://icbinb.cc/, Tweet to us @ICBINBWorkhop
<https://twitter.com/ICBINBWorkshop>, or email us at
cant.believe.it.is.not.better at gmail.com.

--
Best wishes,
The ICBINB Organizers
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