We are now accepting abstracts for
poster presentations at DeepMath 2020. DeepMath is a two day
conference focused on the theoretical understanding of deep
learning and neural networks (for more information see www.deepmath-conference.com).
A principal goal of this conference is to bring together
theoreticians working on deep learning from various
disciplines and perspectives. We therefore encourage
submissions from researchers from diverse disciplines
including, but not limited to
- Statistics
- Physics
- Computer
science
- Neuroscience
- Mathematics
- Psychology
- Engineering
Topics may address any area of deep
learning research such as
- expressivity
- generalization
- optimization
- representations
- computation
- network
architectures
- recurrent
networks.
To complement the wealth of
conferences with applications and theory the focus for
DeepMath 2020 will be exclusively on the theoretical and
mechanistic understanding of the underlying properties of
neural networks. Invited speakers for DeepMath 2020 include
- Richard
Baraniuk (Rice U)
- Gitta Kutyniok
(TU Berlin)
- Eero Simoncelli
(NYU)
- Rene Vidal
(Johns Hopkins)
- Lenka Zdeborova
(CEA Saclay)
- Demba Ba
(Harvard U)
- Stephanie
Jegelka (MIT)
- Mikhail Belkin
(OSU)
Abstracts will not be made public
(i.e., no official proceedings), and will be doubly-blind
reviewed and selected for quality. All poster submissions
should be properly anonymized in order to allow for blind
refereeing. Submissions should be no more than 1 page although
a second page may be used for references. Authors should
submit a pdf file prepared using the Latex style file
available here (https://storage.googleapis.com/wzukusers/user-23451351/documents/5c90d93c12862PuReXwq/DeepMath_template.zip)
and should adopt all formatting, subject headings, font sizes,
etc. defined therein. Submissions that fail to meet the format
requirements will not be reviewed. The first author listed on
the abstract is considered to be the presenting author. Each
presenting author may submit only one abstract.