Sunday, March 12, 2023

[DMANET] DeepLearn 2023 Summer: early registration March 12



DeepLearn 2023 Summer

Las Palmas de Gran Canaria, Spain

July 17-21, 2023


Co-organized by:

University of Las Palmas de Gran Canaria

Institute for Research Development, Training and Advice =E2=80=93 IRDTA


Early registration: March 12, 2023



DeepLearn 2023 Summer is part of a multi-event called Deep&Big 2023 consist=
ing also of BigDat 2023 Summer. DeepLearn 2023 Summer participants will hav=
e the opportunity to attend lectures in the program of BigDat 2023 Summer a=
s well if they are interested.


DeepLearn 2023 Summer will be a research training event with a global scope=
aiming at updating participants on the most recent advances in the critica=
l and fast developing area of deep learning. Previous events were held in B=
ilbao, Genova, Warsaw, Las Palmas de Gran Canaria, Guimar=C3=A3es, Las Palm=
as de Gran Canaria, Lule=C3=A5, Bournemouth and Bari.

Deep learning is a branch of artificial intelligence covering a spectrum of=
current frontier research and industrial innovation that provides more eff=
icient algorithms to deal with large-scale data in a huge variety of enviro=
nments: computer vision, neurosciences, speech recognition, language proces=
sing, human-computer interaction, drug discovery, health informatics, medic=
al image analysis, recommender systems, advertising, fraud detection, robot=
ics, games, finance, biotechnology, physics experiments, biometrics, commun=
ications, climate sciences, geographic information systems, signal processi=
ng, genomics, etc. etc. Renowned academics and industry pioneers will lectu=
re and share their views with the audience.

Most deep learning subareas will be displayed, and main challenges identifi=
ed through 20 four-hour and a half courses and 2 keynote lectures, which wi=
ll tackle the most active and promising topics. The organizers are convince=
d that outstanding speakers will attract the brightest and most motivated s=
tudents. Face to face interaction and networking will be main ingredients o=
f the event. It will be also possible to fully participate in vivo remotely=

An open session will give participants the opportunity to present their own=
work in progress in 5 minutes. Moreover, there will be two special session=
s with industrial and employment profiles.


Graduate students, postgraduate students and industry practitioners will be=
typical profiles of participants. However, there are no formal pre-requisi=
tes for attendance in terms of academic degrees, so people less or more adv=
anced in their career will be welcome as well. Since there will be a variet=
y of levels, specific knowledge background may be assumed for some of the c=
ourses. Overall, DeepLearn 2023 Summer is addressed to students, researcher=
s and practitioners who want to keep themselves updated about recent develo=
pments and future trends. All will surely find it fruitful to listen to and=
discuss with major researchers, industry leaders and innovators.


DeepLearn 2023 Summer will take place in Las Palmas de Gran Canaria, on the=
Atlantic Ocean, with a mild climate throughout the year, sandy beaches and=
a renowned carnival. The venue will be:

Instituci=C3=B3n Ferial de Canarias
Avenida de la Feria, 1
35012 Las Palmas de Gran Canaria


2 courses will run in parallel during the whole event. Participants will be=
able to freely choose the courses they wish to attend as well as to move f=
rom one to another.

Also, if interested, participants will be able to attend courses developed =
in BigDat 2023 Summer, which will be held in parallel and at the same venue=

Full live online participation will be possible. The organizers highlight, =
however, the importance of face to face interaction and networking in this =
kind of research training event.


Alex Voznyy (University of Toronto), Comparison of Graph Neural Network Arc=
hitectures for Predicting the Electronic Structure of Molecules and Solids

Aidong Zhang (University of Virginia), Concept-Based Models for Robust and =
Interpretable Deep Learning


Eneko Agirre (University of the Basque Country), [introductory/intermediate=
] Natural Language Processing in the Large Language Model Era

Pierre Baldi (University of California Irvine), [intermediate/advanced] Dee=
p Learning in Science

Nat=C3=A1lia Cordeiro (University of Porto), [introductory/intermediate] Mu=
lti-Tasking Machine Learning in Drug and Materials Design

Daniel Cremers (Technical University of Munich), [intermediate] Deep Networ=
ks for 3D Computer Vision

Stefano Giagu (Sapienza University of Rome), [introductory/intermediate] Qu=
antum Machine Learning on Parameterized Quantum Circuits

Georgios Giannakis (University of Minnesota), [intermediate/advanced] Learn=
ing from Unreliable Labels via Crowdsourcing

Tae-Kyun Kim (Korea Advanced Institute of Science and Technology), [interme=
diate/advanced] Deep 3D Pose Estimation

Marcus Liwicki (Lule=C3=A5 University of Technology), [intermediate/advance=
d] Methods for Learning with Few Data

Chen Change Loy (Nanyang Technological University), [introductory/intermedi=
ate] Image and Video Restoration

Ivan Oseledets (Skolkovo Institute of Science and Technology), [introductor=
y/intermediate] Tensor Methods for Approximation of High-Dimensional Arrays=
and Their Applications in Machine Learning

Deepak Pathak (Carnegie Mellon University), [intermediate/advanced] Continu=
ally Improving Agents for Generalization in the Wild

Kaushik Roy (Purdue University), [introductory/advanced] Neuromorphic Compu=

Bj=C3=B6rn Schuller (Imperial College London), [introductory/intermediate] =
Deep Multimedia Processing

Amos Storkey (University of Edinburgh), [intermediate] Meta-Learning and Co=
ntrastive Learning for Robust Representations

Ponnuthurai N. Suganthan (Qatar University), [introductory/intermediate] Ra=
ndomization-Based Deep and Shallow Learning Algorithms and Architectures

Jiliang Tang (Michigan State University), [introductory/advanced] Deep Lear=
ning on Graphs: Methods, Advances and Applications

Savannah Thais (Columbia University), [intermediate] Applications of Graph =
Neural Networks: Physical and Societal Systems

Z. Jane Wang (University of British Columbia), [introductory/intermediate] =
Adversarial Deep Learning in Digital Image Security & Forensics

Li Xiong (Emory University), [introductory] Deep Learning and Privacy Enhan=
cing Technology

Lihi Zelnik-Manor (Technion - Israel Institute of Technology), [introductor=
y] Introduction to Computer Vision and the Ethical Questions It Raises


An open session will collect 5-minute voluntary presentations of work in pr=
ogress by participants. They should submit a half-page abstract containing =
the title, authors, and summary of the research to by July 9=
, 2023.


A session will be devoted to 10-minute demonstrations of practical applicat=
ions of deep learning in industry. Companies interested in contributing are=
welcome to submit a 1-page abstract containing the program of the demonstr=
ation and the logistics needed. People in charge of the demonstration must =
register for the event. Expressions of interest have to be submitted to dav= by July 9, 2023.


Organizations searching for personnel well skilled in deep learning will ha=
ve a space reserved for one-to-one contacts. It is recommended to produce a=
1-page .pdf leaflet with a brief description of the organization and the p=
rofiles looked for to be circulated among the participants prior to the eve=
nt. People in charge of the search must register for the event. Expressions=
of interest have to be submitted to by July 9, 2023.


Carlos Mart=C3=ADn-Vide (Tarragona, program chair)
Sara Morales (Brussels)
David Silva (London, organization chair)


It has to be done at

The selection of 8 courses requested in the registration template is only t=
entative and non-binding. For logistical reasons, it will be helpful to hav=
e an estimation of the respective demand for each course. During the event,=
participants will be free to attend the courses they wish as well as event=
ually courses in BigDat 2023 Summer.

Since the capacity of the venue is limited, registration requests will be p=
rocessed on a first come first served basis. The registration period will b=
e closed and the on-line registration tool disabled when the capacity of th=
e venue will have got exhausted. It is highly recommended to register prior=
to the event.


Fees comprise access to all courses and lunches. There are several early re=
gistration deadlines. Fees depend on the registration deadline.

The fees for on site and for online participation are the same.


Accommodation suggestions will be available in due time at


A certificate of successful participation in the event will be delivered in=
dicating the number of hours of lectures.



Cabildo de Gran Canaria

Universidad de Las Palmas de Gran Canaria - Fundaci=C3=B3n Parque Cient=C3=
=ADfico Tecnol=C3=B3gico

Universitat Rovira i Virgili

Institute for Research Development, Training and Advice =E2=80=93 IRDTA, Br=
* Contributions to be spread via DMANET are submitted to
* Replies to a message carried on DMANET should NOT be
* addressed to DMANET but to the original sender. The
* original sender, however, is invited to prepare an
* update of the replies received and to communicate it
* via DMANET.