February 7th, 2024 
Daniela Lopez de Luise
we would like to let you know that we will close registration on February 8th, 2024 for this year's SOFSEM at Cochem in Germany.
If you want to check out what you might miss, please visit https://www.uni-trier.de/index.php?id=90670&L=2
See you soon at the Moselle river,
Henning Fernau
February 7th, 2024 Daniela Lopez de Luise Time and place: 9-11 September 2024, Venice, Italy
Paper submission deadline: 23 June 2024
S+SSPR 2024 is a joint event organized by Technical Committee 1 (Statistical Pattern Recognition Technique) and Technical Committee 2 (Structural and Syntactical Pattern Recognition) of the International Association of Pattern Recognition (IAPR). Authors are invited to submit papers addressing topics in statistical, structural or syntactic pattern recognition and their applications. For list of topics of interest and more details see https://sspr2024.dais.unive.it/
February 7th, 2024 Daniela Lopez de Luise ===================================
CALL FOR COMPETITION: SAM 2024 – €€€ with prizes! $$$
Layout segmentation of ancient manuscripts
https://ai4ch.uniud.it/udiadscomp/
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We are glad to announce SAM: International Competition on Few-Shot and Many-Shot Layout Segmentation of Ancient Manuscripts, in conjunction with the 18th International Conference on Document Analysis and Recognition ICDAR 2024.
Competition Overview:
Layout segmentation is a critical aspect of Document Image Analysis, particularly when it comes to ancient manuscripts. It consists in decomposing the document in several regions representing title, main text, paratext, etc.. We invite the research community to address this task on U-DIADS-Bib, a novel dataset of fully-labelled ancient manuscripts.
Competition Tasks:
We propose two separate tasks. Participants can try only one of them or both.
Important Dates:
Prizes:
Winners of each task will be eligible for a cash prize of 300 EUR sponsored by CVPL – Italian Association for Computer Vision, Pattern Recognition and Machine Learning, IAPR Italian chapter.
For any additional information, please visit the website: https://ai4ch.uniud.it/udiadscomp/
Organizers:
Silvia Zottin, zottin.silvia@spes.uniud.it
Axel De Nardin, denardin.axel@spes.uniud.it
Claudio Piciarelli, claudio.piciarelli@uniud.it
Gian Luca Foresti, gianluca.foresti@uniud.it
Emanuela Colombi, emanuela.colombi@uniud.it
AI4CH – Artificial Intelligence for Cultural Heritage Lab, University of Udine. https://ai4ch.uniud.it/
February 7th, 2024 Daniela Lopez de Luise The 2nd European Summer School in Artificial Intelligence – ESSAI 2024
July 15-26, 2024
Athens, Greece
IMPORTANT DATES
07 Feb 2024: Course Title submission deadline (mandatory)
14 Feb 2024: Final submission
06 Mar 2024: Notification
ABOUT
ESSAI 2024 is the second edition of the annual summer school on AI held
under the auspices of the European Association for Artificial Intelligence
(EurAI).
ESSAI 2024 will provide an interdisciplinary setting in which courses are
offered in all areas of Artificial Intelligence and also from wider
scientific, historical, and philosophical perspectives. ESSAI is a central
meeting place for students and young researchers in Artificial
Intelligence to discuss current research and share knowledge. Courses will
consist of five 90-minute sessions, offered daily (Monday-Friday) in a
single week, to allow students to develop in-depth knowledge of a topic.
The first edition of ESSAI was held in Ljubljana, Slovenia
(https://essai.si/ ) between the 24th and 28th of July 2023 and was
extremely successful, attracting over 500 participants. We look forward to
a second edition of ESSAI that is at least as successful.
TOPICS AND FORMAT
ESSAI aims to cover all subdisciplines of AI and the interactions between
them.
Proposals for courses at ESSAI 2024 are invited in all areas of Artificial
Intelligence, including but not limited to the following:
* Autonomous Agents and Multi-agent Systems (MAS)
* Causality and Causal Learning (CL)
* Ethical, Legal and Social Aspects of AI (ELS)
* Foundation Models (FM)
* Knowledge Representation and Reasoning (KR)
* Learning Theory (LT)
* Natural Language Processing (NLP)
* Neuro-Symbolic Learning and Reasoning (NSLR)
* Planning and Strategic Reasoning (PLAN)
* Reinforcement Learning (RL)
* Robotics (ROB)
* Safe, Explainable and Trustworthy AI (SET)
* Search and Optimization (SO)
* Supervised and Unsupervised Learning (ML)
* Vision (VIS)
Each course will consist of five 90-minute lectures, offered daily
(Monday-Friday) in a single week.
While introductory courses will typically focus on one subarea of AI,
advanced courses are encouraged to present a broader perspective on AI and
should be of interest beyond one specific area.
CATEGORIES
Each proposal should fall under one of the following categories.
INTRODUCTORY COURSES
Introductory courses are intended to introduce a research field to
students, young researchers, and other non-specialists, and to foster a
sound understanding of its basic methods and techniques. Such courses
should enable researchers from related disciplines to develop some comfort
and competence in the topic considered. Introductory courses in a
cross-disciplinary area may presuppose general knowledge of the related
disciplines.
ADVANCED COURSES
Advanced courses are targeted primarily to graduate students who wish to
acquire a level of comfort and understanding in the current research of a
field.
PROPOSAL GUIDELINES
To be considered, course proposals should closely adhere to the following
guidelines:
* Courses must be presented by lecturers who submitted the proposal. For
courses with more than two lecturers, the role of each lecturer should be
clearly explained and justified in the proposal.
* Course proposals should explicitly state the intended course category.
Proposals for introductory courses should indicate the intended level, for
example, as it relates to standard textbooks and monographs in the area.
Proposals for advanced courses should specify the prerequisites in detail.
Proposals must be submitted in PDF format via:
https://easychair.org/conferences/?conf=essai2024
and include all the following:
1. Personal information for each proposer: Name, affiliation, contact
address, email, homepage (optional)
2. General proposal information: Title, category
3. Information about the course content:
a. Abstract of up to 150 words
b. Motivation and description (up to two pages)
c. Tentative outline
d. Expected level and prerequisites
e. Appropriate references (e.g., textbooks, monographs, proceedings,
surveys)
f. Whether the course will appeal to students outside of the main
discipline of the course
4. Information about the proposer(s):
a. Short CVs of the proposer(s)
b. Evidence that the proposer(s) are excellent lecturers with relevant
teaching experience, in particular in delivering intensive courses in an
interdisciplinary setting.
To keep participation fees to a minimum, all the instructional and
organizational work of ESSAI is performed on a completely voluntary basis.
However, the registration fees of organizers and instructors will be
waived. In addition, and where appropriate, ESSAI will seek to partially
reimburse travel and accommodation expenses associated with delivering a
course. If lecturers can cover their travel and accommodation expenses
from other sources, this is greatly appreciated.
SUBMISSION INFORMATION
By February 7, 2024: Proposers must submit on EasyChair at least the
name(s) of the lecturers(s), the ESSAI area and course level and a short
abstract.
By February 14, 2024: Submission must be completed by uploading a PDF with
the actual proposal as detailed above.
SUBMISSION PORTAL
Please submit your proposals to
https://easychair.org/conferences/?conf=essai2024
ORGANIZING COMMITTEE
Chair: Brian Logan, Utrecht University
Co-chair: Magdalena Ortiz, TU Wien
Local Chair: Manolis Koubarakis, National and Kapodistrian University of
Athens
ESSAI Steering Committee Chair (EurAI Board representative): Giuseppe De
Giacomo, Oxford University
February 7th, 2024 Daniela Lopez de Luise DeepLearn 2024
Porto – Maia, Portugal
July 15-19, 2024
https://deeplearn.irdta.eu/2024/
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Co-organized by:
University of Maia
Institute for Research Development, Training and Advice – IRDTA
Brussels/London
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Early registration: March 3, 2024
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SCOPE:
DeepLearn 2024 will be a research training event with a global scope aiming at updating participants on the most recent advances in the critical and fast developing area of deep learning. Previous events were held in Bilbao, Genova, Warsaw, Las Palmas de Gran Canaria, Guimarães, Las Palmas de Gran Canaria, Luleå, Bournemouth, Bari and Las Palmas de Gran Canaria.
Deep learning is a branch of artificial intelligence covering a spectrum of current frontier research and industrial innovation that provides more efficient algorithms to deal with large-scale data in a huge variety of environments: computer vision, neurosciences, speech recognition, language processing, human-computer interaction, drug discovery, health informatics, medical image analysis, recommender systems, advertising, fraud detection, robotics, games, finance, biotechnology, physics experiments, biometrics, communications, climate sciences, geographic information systems, signal processing, genomics, materials design, video technology, social systems, etc. etc.
The field is also raising a number of relevant questions about robustness of the algorithms, explainability, transparency, and important ethical concerns at the frontier of current knowledge that deserve careful multidisciplinary discussion.
Most deep learning subareas will be displayed, and main challenges identified through 18 four-hour and a half courses, 2 keynote lectures, 1 round table and a few hackathon-type competitions among students, which will tackle the most active and promising topics. Renowned academics and industry pioneers will lecture and share their views with the audience. The organizers are convinced that outstanding speakers will attract the brightest and most motivated students. Face to face interaction and networking will be main ingredients of the event. It will be also possible to fully participate in vivo remotely.
ADDRESSED TO:
Graduate students, postgraduate students and industry practitioners will be typical profiles of participants. However, there are no formal pre-requisites for attendance in terms of academic degrees, so people less or more advanced in their career will be welcome as well.
Since there will be a variety of levels, specific knowledge background may be assumed for some of the courses.
Overall, DeepLearn 2024 is addressed to students, researchers and practitioners who want to keep themselves updated about recent developments and future trends. All will surely find it fruitful to listen to and discuss with major researchers, industry leaders and innovators.
VENUE:
DeepLearn 2024 will take place in Porto, the second largest city in Portugal, recognized by UNESCO in 1996 as a World Heritage Site. The venue will be:
University of Maia
Avenida Carlos de Oliveira Campos – Castêlo da Maia
4475-690 Maia
Porto, Portugal
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 from one to another.
All lectures will be videorecorded. Participants will be able to watch them again for 45 days after the event.
An open session will give participants the opportunity to present their own work in progress in 5 minutes. Also companies will be able to present their technical developments for 10 minutes.
This year’s edition of the school will schedule hands-on activities including mini-hackathons, where participants will work in teams to tackle several machine learning challenges.
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.
KEYNOTE SPEAKERS:
Jiawei Han (University of Illinois Urbana-Champaign), How Can Large Language Models Contribute to Effective Text Mining?
Katia Sycara (Carnegie Mellon University), Effective Multi Agent Teaming
PROFESSORS AND COURSES:
Luca Benini (Swiss Federal Institute of Technology Zurich), [intermediate/advanced] Open Hardware Platforms for Edge Machine Learning
Gustau Camps-Valls (University of València), [intermediate] AI for Earth, Climate, and Sustainability
Nitesh Chawla (University of Notre Dame), [introductory/intermediate] Introduction to Representation Learning on Graphs
Daniel Cremers (Technical University of Munich), [introductory/advanced] Deep Networks for 3D Computer Vision
Peng Cui (Tsinghua University), [intermediate/advanced] Stable Learning for Out-of-Distribution Generalization: Invariance, Causality and Heterogeneity
Sergei V. Gleyzer (University of Alabama), [introductory/intermediate] Machine Learning Fundamentals and Their Applications to Very Large Scientific Data: Rare Signal and Feature Extraction, End-to-End Deep Learning, Uncertainty Estimation and Realtime Machine Learning Applications in Software and Hardware
Yulan He (King’s College London), [introductory/intermediate] Machine Reading Comprehension with Large Language Models
Frank Hutter (University of Freiburg), [intermediate/advanced] AutoML
George Karypis (University of Minnesota), [intermediate] Deep Learning Models and Systems for Real-World Graph Machine Learning
Hermann Ney (RWTH Aachen University / AppTek), [intermediate/advanced] Machine Learning and Deep Learning for Speech & Language Technology: A Probabilistic Perspective
Massimiliano Pontil (Italian Institute of Technology), [intermediate/advanced] Operator Learning for Dynamical Systems
Elisa Ricci (University of Trento), [intermediate] Continual and Adaptive Learning in Computer Vision
Xinghua Mindy Shi (Temple University), [intermediate] Trustworthy Artificial Intelligence for Health and Medicine
Michalis Vazirgiannis (École Polytechnique), [intermediate/advanced] Graph Machine Learning and Multimodal Graph Generative AI
James Zou (Stanford University), [introductory/intermediate] Large Language Models and Biomedical Applications
OPEN SESSION:
An open session will collect 5-minute voluntary presentations of work in progress by participants.
They should submit a half-page abstract containing the title, authors, and summary of the research to david@irdta.eu by July 7, 2024.
INDUSTRIAL SESSION:
A session will be devoted to 10-minute demonstrations of practical applications of deep learning in industry.
Companies interested in contributing are welcome to submit a 1-page abstract containing the program of the demonstration and the logistics needed. People in charge of the demonstration must register for the event.
Expressions of interest have to be submitted to david@irdta.eu by July 7, 2024.
HACKATHONS:
Hackathons will take place, where participants will work in teams to tackle several machine learning challenges. They will be coordinated by Professor Sergei V. Gleyzer. The challenges will be released 2 weeks before the beginning of the school. A jury will judge the submissions and the winners of each challenge will be announced on the final day. The winning teams will receive a small prize and the runners-up will get a certificate.
EMPLOYERS:
Organizations searching for personnel well skilled in deep learning will be provided a space for one-to-one contacts.
It is recommended to produce a 1-page .pdf leaflet with a brief description of the organization and the profiles looked for to be circulated among the participants prior to the event. People in charge of the search must register for the event.
Expressions of interest have to be submitted to david@irdta.eu by July 7, 2024.
SPONSORS:
Companies/institutions/organizations willing to be sponsors of the event can download the sponsorship leaflet from
https://deeplearn.irdta.eu/2024/sponsoring/
ORGANIZING COMMITTEE:
José Paulo Marques dos Santos (Maia, local chair)
Carlos Martín-Vide (Tarragona, program chair)
Sara Morales (Brussels)
José Luís Reis (Maia)
Luís Paulo Reis (Porto)
David Silva (London, organization chair)
REGISTRATION:
It has to be done at
https://deeplearn.irdta.eu/2024/registration/
The selection of 8 courses requested in the registration template is only tentative and non-binding. For logistical reasons, it will be helpful to have an estimation of the respective demand for each course.
Since the capacity of the venue is limited, registration requests will be processed on a first come first served basis. The registration period will be closed and the on-line registration tool disabled when the capacity of the venue will have got exhausted. It is highly recommended to register prior to the event.
FEES:
Fees comprise access to all program activities and lunches.
There are several early registration deadlines. Fees depend on the registration deadline.
The fees for on site and for online participation are the same.
ACCOMMODATION:
Accommodation suggestions will be available at
https://deeplearn.irdta.eu/2024/accommodation/
CERTIFICATE:
A certificate of successful participation in the event will be delivered indicating the number of hours of lectures. This should be sufficient for those participants who plan to request ECTS recognition from their home university.
QUESTIONS AND FURTHER INFORMATION:
ACKNOWLEDGMENTS:
Universidade da Maia
Universidade do Porto
Universitat Rovira i Virgili