9th INTERNATIONAL SCHOOL ON DEEP LEARNING
DeepLearn 2023 Spring
Bari, Italy
April 3-7, 2023
https://deeplearn.irdta.eu/2023sp/
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Co-organized by:
Department of Computer Science
University of Bari =E2=80=9CAldo Moro=E2=80=9D
Institute for Research Development, Training and Advice =E2=80=93 IRDTA
Brussels/London
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Early registration: March 13, 2023
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SCOPE:
DeepLearn 2023 Spring 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 and Bournemouth.
Deep learning is a branch of artificial intelligence covering a spectrum of=
current exciting 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, bioinformatics, geographic information systems,=
etc. etc. Renowned academics and industry pioneers will lecture and share =
their views with the audience.
Most deep learning subareas will be displayed, and main challenges identifi=
ed through 22 four-hour and a half courses and 3 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 recruitment profiles.
ADDRESSED TO:
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 Spring 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.
VENUE:
DeepLearn 2023 Spring will take place in Bari, an important economic centre=
on the Adriatic Sea. The venue will be:
Department of Computer Science
University of Bari =E2=80=9CAldo Moro=E2=80=9D
via Edoardo Orabona, 4
70125 Bari
STRUCTURE:
3 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.
Full live online participation will be possible. However, the organizers hi=
ghlight the importance of face to face interaction and networking in this k=
ind of research training event.
KEYNOTE SPEAKERS:
Vipin Kumar (University of Minnesota), Knowledge-Guided Deep Learning: A Fr=
amework for Accelerating Scientific Discovery
William S. Noble (University of Washington), Deep Learning Applications in =
Mass Spectrometry Proteomics and Single-Cell Genomics
Emma Tolley (Swiss Federal Institute of Technology Lausanne), Physics-Infor=
med Deep Learning
PROFESSORS AND COURSES:
Babak Ehteshami Bejnordi (Qualcomm AI Research), [intermediate/advanced] Co=
nditional Computation for Efficient Deep Learning with Applications to Comp=
uter Vision, Multi-Task Learning, and Continual Learning
Patrick Gallinari (Sorbonne University), [intermediate] Physics Aware Deep =
Learning for Modeling Dynamical Systems
Sergei V. Gleyzer (University of Alabama), [introductory/intermediate] Mach=
ine Learning Fundamentals and Their Applications to Very Large Scientific D=
ata: Rare Signal and Feature Extraction, End-to-End Deep Learning, Uncertai=
nty Estimation and Realtime Machine Learning Applications in Software and H=
ardware
Jacob Goldberger (Bar-Ilan University), [introductory/intermediate] Calibra=
tion Methods for Neural Networks
Christoph Lampert (Institute of Science and Technology Austria), [intermedi=
ate] Training with Fairness and Robustness Guarantees
Yingbin Liang (Ohio State University), [intermediate/advanced] Bilevel Opti=
mization and Applications in Deep Learning
Xiaoming Liu (Michigan State University), [intermediate] Deep Learning for =
Trustworthy Biometrics
Michael Mahoney (University of California Berkeley), [intermediate] Practic=
al Neural Network Theory
Liza Mijovic (University of Edinburgh), [introductory/intermediate] Deep Le=
arning & the Higgs Boson: Classification with Fully Connected and Adversari=
al Networks
Bhiksha Raj (Carnegie Mellon University), [introductory] An Introduction to=
Quantum Neural Networks [with Rita Singh, Daniel Justice and Prabh Baweja]
Holger Rauhut (RWTH Aachen University), [intermediate] Gradient Descent Met=
hods for Learning Neural Networks: Convergence and Implicit Bias
Bart ter Haar Romeny (Eindhoven University of Technology), [intermediate/ad=
vanced] Explainable Deep Learning from First Principles
Tara Sainath (Google), [advanced] E2E Speech Recognition
Martin Schultz (Research Centre J=C3=BClich), [intermediate] Deep Learning =
for Air Quality, Weather and Climate
Hao Su (University of California San Diego), [intermediate/advanced] Neural=
Representation for 3D Capturing
Adi Laurentiu Tarca (Wayne State University), [intermediate] Machine Learni=
ng for Cross-Sectional and Longitudinal Omics Studies
Zhi Tian (George Mason University), [intermediate] Communication-Efficient =
and Robust Distributed Learning
Michalis Vazirgiannis (Polytechnic Institute of Paris), [intermediate/advan=
ced] Graph Machine Learning with GNNs and Applications
Atlas Wang (University of Texas Austin), [intermediate] Sparse Neural Netwo=
rks: From Practice to Theory
Guo-Wei Wei (Michigan State University), [introductory/advanced] Discoverin=
g the Mechanisms of SARS-CoV-2 Evolution and Transmission
Lei Xing (Stanford University), [intermediate] Deep Learning for Medical Im=
aging and Genomic Data Processing: from Data Acquisition, Analysis, to Biom=
edical Applications
Xiaowei Xu (University of Arkansas Little Rock), [intermediate/advanced] De=
ep Learning Language Models and Causal Inference
OPEN SESSION:
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 david@irdta.eu by March =
26, 2023.
INDUSTRIAL SESSION:
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=
id@irdta.eu by March 26, 2023.
EMPLOYER SESSION:
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 company and the profil=
es looked for to be circulated among the participants prior to the event. P=
eople in charge of the search must register for the event. Expressions of i=
nterest have to be submitted to david@irdta.eu by March 26, 2023.
ORGANIZING COMMITTEE:
Giuseppina Andresini (Bari, local co-chair)
Graziella De Martino (Bari, local co-chair)
Corrado Loglisci (Bari, local co-chair)
Donato Malerba (Bari, local chair)
Carlos Mart=C3=ADn-Vide (Tarragona, program chair)
Paolo Mignone (Bari, local co-chair)
Sara Morales (Brussels)
Gianvito Pio (Bari, local co-chair)
Francesca Prisciandaro (Bari, local co-chair)
David Silva (London, organization chair)
Gennaro Vessio (Bari, local co-chair)
REGISTRATION:
It has to be done at
https://deeplearn.irdta.eu/2023sp/registration/
The selection of 8 courses requested in the registration template is only t=
entative and non-binding. For the sake of organization, it will be helpful =
to have an estimation of the respective demand for each course. During the =
event, participants will be free to attend the courses they wish.
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:
Fees comprise access to all courses and lunches. There are several early re=
gistration deadlines. Fees depend on the registration deadline. The fees fo=
r on site and for online participation are the same.
ACCOMMODATION:
Accommodation suggestions are available at
https://deeplearn.irdta.eu/2023sp/accommodation/
CERTIFICATE:
A certificate of successful participation in the event will be delivered in=
dicating the number of hours of lectures.
QUESTIONS AND FURTHER INFORMATION:
ACKNOWLEDGMENTS:
University of Bari =E2=80=9CAldo Moro=E2=80=9D
Rovira i Virgili University
Institute for Research Development, Training and Advice =E2=80=93 IRDTA, Br=
ussels/London
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