50 JAIIO – Programa preliminar de las 50 JAIIO

 

Estimados/as,

¿Querés ver el horario de tu exposición? ¿las conferencias que hay pensadas para vos? ¿las distintas charlas que se van a desarrollar?
 
¡Ya está listo el programa preliminar de las 50 JAIIO! 
 
En los próximos días recibirán información acerca de las plataformas y canales a través de los cuales se transmitirán las Jornadas.
 
Agendá las actividades que más te interesan, te esperamos del 18 al 29 de Octubre
 
¡No te las pierdas!
 
Saludos cordiales.

DeepLearn 2022 Winter: early registration October 9

5th INTERNATIONAL SCHOOL ON DEEP LEARNING
DeepLearn 2022 Winter
Bournemouth, UK
January 17-21, 2022
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Co-organized by:
Department of Computing and Informatics
Bournemouth University
Institute for Research Development, Training and Advice – IRDTA
Brussels/London
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Early registration: October 9, 2021
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SCOPE:
DeepLearn 2022 Winter 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 and Las Palmas de Gran Canaria.
Deep learning is a branch of artificial intelligence covering a spectrum of current exciting research and industrial innovation that provides more efficient algorithms to deal with large-scale data in a huge variety of different environments: computer vision, neurosciences, speech recognition, language processing, human-computer interaction, drug discovery, biomedical informatics, image analysis, recommender systems, advertising, fraud detection, robotics, games, 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 identified through 24 four-hour and a half courses and 3 keynote lectures, which will tackle the most active and promising topics. The organizers are convinced that outstanding speakers will attract the brightest and most motivated students. Face to face interaction and networking will be main components of the event.
An open session will give participants the opportunity to present their own work in progress in 5 minutes. Moreover, there will be two special sessions 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-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 2022 Winter 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 2022 Winter will take place in Bournemouth, a coastal resort town on the south coast of England. The venue will be:
TBA

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.
Full in vivo online participation will be possible. However, the organizers want to emphasize the importance of face to face interaction and networking in this kind of research training event.
KEYNOTE SPEAKERS:
Yi Ma (University of California, Berkeley), White-box Deep (Convolution) Networks from the Principle of Rate Reduction
Daphna Weinshall (Hebrew University of Jerusalem), Curriculum Learning in Deep Networks
Eric P. Xing (Carnegie Mellon University), It Is Time for Deep Learning to Understand Its Expense Bills
PROFESSORS AND COURSES:
  • Peter L. Bartlett (University of California, Berkeley), [intermediate/advanced] Deep Learning: A Statistical Viewpoint
  • Joachim M. Buhmann (Swiss Federal Institute of Technology, Zürich), [introductory/advanced] Model and Algorithm Validation for Data Science
  • Matias Carrasco Kind (University of Illinois, Urbana-Champaign), [intermediate] Anomaly Detection
  • Nitesh Chawla (University of Notre Dame), [introductory/intermediate] Graph Representation Learning
  • Seungjin Choi (BARO AI Academy), [introductory/intermediate] Bayesian Optimization over Continuous, Discrete, or Hybrid Spaces
  • Sumit Chopra (New York University), [intermediate] Deep Learning in Healthcare
  • Rüdiger Dillmann (Karlsruhe Institute of Technology), [introductory/intermediate] Building Brains for Robots
  • Marco Duarte (University of Massachusetts, Amherst), [introductory/intermediate] Explainable Machine Learning
  • Charles Elkan (University of California, San Diego), [intermediate] AI and ML Applications in Finance and Retail
  • Rob Fergus (New York University), [intermediate/advanced] Self-supervised Learning of Visual Representations for Recognition and Interaction
  • João Gama (University of Porto), [introductory] Learning from Data Streams: Challenges, Issues, and Opportunities
  • Claus Horn (Zurich University of Applied Sciences), [intermediate] Deep Learning for Biotechnology
  • Nathalie Japkowicz (American University), [intermediate/advanced] Learning from Class Imbalances
  • Gregor Kasieczka (University of Hamburg), [introductory/intermediate] Deep Learning Fundamental Physics: Rare Signals, Unsupervised Anomaly Detection, and Generative Models
  • Karen Livescu (Toyota Technological Institute at Chicago), [intermediate/advanced] Speech Processing: Automatic Speech Recognition and beyond
  • David McAllester (Toyota Technological Institute at Chicago), [intermediate/advanced] Information Theory for Deep Learning
  • Dhabaleswar K. Panda (Ohio State University), [intermediate] Exploiting High-performance Computing for Deep Learning: Why and How?
  • Fabio Roli (University of Cagliari), [introductory/intermediate] Adversarial Machine Learning
  • Jude W. Shavlik (University of Wisconsin, Madison), [introductory/intermediate] Advising, Explaining, Distilling, and Quantizing Deep Neural Networks
  • Kunal Talwar (Apple), [introductory/intermediate] Foundations of Differentially Private Learning
  • Tinne Tuytelaars (KU Leuven), [introductory/intermediate] Continual Learning in Deep Neural Networks
  • Lyle Ungar (University of Pennsylvania), [intermediate] Natural Language Processing using Deep Learning
  • Yu-Dong Zhang (University of Leicester), [introductory/intermediate] Convolutional Neural Networks and Their Applications to COVID-19 Diagnosis

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 January 9, 2022.

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 January 9, 2022.
EMPLOYER SESSION:
Firms searching for personnel well skilled in deep learning will have 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 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 January 9, 2022.
ORGANIZING COMMITTEE:
Rashid Bakirov (Bournemouth, co-chair)
Nan Jiang (Bournemouth, co-chair)
Carlos Martín-Vide (Tarragona, program chair)
Sara Morales (Brussels)
David Silva (London, co-chair)
REGISTRATION:
It has to be done at
The selection of up to 8 courses requested in the registration template is only tentative 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 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 get exhausted. It is highly recommended to register prior to the event.
FEES:
Fees comprise access to all courses and lunches. There are several early registration deadlines. Fees depend on the registration deadline.
ACCOMMODATION:
Accommodation suggestions will be available in due time at
CERTIFICATE:
A certificate of successful participation in the event will be delivered indicating the number of hours of lectures.
QUESTIONS AND FURTHER INFORMATION:
ACKNOWLEDGMENTS:
Bournemouth University
Institute for Research Development, Training and Advice – IRDTA, Brussels/London

Submit Your Research Articles – 2nd International Conference on Software Engineering, Security and Blockchain (SESBC 2021)

nd  International Conference on Software Engineering, Security andBlockchain (SESBC 2021)
December 24 ~ 25, 2021, Sydney, Australiahttps://cse2021.org/sesbc/index Scope2
nd
 International Conference on Software Engineering, Security and Blockchain (SESBC2021)
 Will provide an excellent international forum for sharing knowledge and results in theory,methodology and applications of Software Engineering, Security and Blockchain. Authors aresolicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in theareas of Software Engineering, Security and Blockchain.

2021 OkIP Intl Conf on Automated & Intelligent Systems|| OkCity, USA|| Nov 15-18

2021 OkIP International Conference on Automated and Intelligent Systems (CAIS)

MNTC Conference Center, Oklahoma City, OK, USA & Online

November 15-18, 2021

 

>> Co-located Conferences and Events

 

>> Keynotes/Invited Talks

“Machine Learning for Critical Systems Security”

– Nancy R. Mead, PhD, Carnegie Mellon University, USA

 

“Sustainable Energy Harvesting and Wireless Power Transfer Systems”

– Manos M. Tentzeris, PhD, Georgia Institute of Technology, USA

 

“Blockchain Technology and its implications in Business Applications and Healthcare IT”

– Akhil Kumar, PhD, Penn State University, USA

 

>> Technical Research & Industry Tracks

– Agent-based, Automated, and Distributed Supports

– Intelligent Systems and Applications

– Knowledge-based and Control Supports

– Robotics and Vehicles

 

>> Contribution Types

– Full Paper: Accomplished research results (6 pages)

– Short Paper: Work in progress/fresh developments (3 pages)

– Poster/Journal First: Displayed/Oral presented (1 page)

 

>> Important Dates (Extended):

– Submission: Oct 16, 2021

– Conference: Nov 15-18, 2021

 

>> Technical Program Committee

 

>> Venue

 

>> For more information, submission details, and important dates, visit:

 

Please feel free to contact us for any inquiry at:

1st Workshop on Computer Vision for Winter Sports

1st Workshop on Computer Vision for Winter Sports (CV4WS)

in conjunction with the

IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2022  

 

 

Workshop Website: https://machinelearning.uniud.it/events/CV4WS-2022/

 

 

Call for Papers

Winter sports such as skiing, bobsleigh, ice-skating and ice-hockey are popular winter activities. To improve human well-being and sociality, different winter sport federations want to promote such sports by increasing the people engagement in them. This is mostly obtained by the broadcasting of professional competitions. Indeed, the TV transmission of only alpine and nordic skiing competitions attracts around 6 billions of viewers (source: International Ski Federation – FIS – 2019/2020 season reports) including many new potential practitioners.

Increased interest towards the winter disciplines can be achieved by enhancing the viewing experience of the spectators through richer broadcasting contents and higher athlete performance, and these can be driven by yet-to-come video technologies. The most common solutions available today to analyze the athlete performance are based on sensors like IMUs or GNSS. Image and video analytics is not a predominant technology in the winter sports domain yet, even though a large amount of visual data is available through broadcast videos or can be generated at a cheap cost with standard cameras. We believe this gap is in part due to the challenging settings that the snowy and icy environments set when they are imaged with continuously moving cameras. These issues must be then considered in relation to the real-time processing requirements of broadcasting applications or the decision-making processes performed during the trainings. We believe that the problems arising in these domains offer particular and stimulating challenges that would lead to relevant contributions to the computer vision field.

For these motivations, the goal of our workshop is twofold: on one hand to promote the employment of computer vision and AI solutions in the winter sports industry, by presenting the latest research solutions for winter sports-related problems; and at the same time, we would like to stimulate the interest of the computer vision and AI audience with new and interesting problems that could lead to the engagement of researchers and the development of new solutions. Hence, we invite researchers and engineers interested in these topics to join our workshop, submitting ongoing and recently published ideas, demos, and applications in support of increasing the effectiveness and efficiency of computer vision technologies in the winter sports domain.

Research papers are solicited in, but not limited to, the following topic areas:

·       Machine learning solutions for video understanding or activity recognition regarding winter sports

·       Pose estimation of athletes

·       Evaluation and measurement of athlete performance

·       Performance forecasting

·       Detection/evaluation/prevention of injuries in winter sports with computer vision

·       Crowd and spectators monitoring

·       Augmented/virtual reality for winter sports and fan engagement

·       Applications of computer vision/AI to winter sports (skiing, ice-hockey, ice-skating,

·       biathlon, bobsleigh, luge, curling, etc.).

·       Image/video understanding in winter/harsh weather conditions

·       Camera pose estimation in broadcast videos

·       Video-based trajectory reconstruction and analysis

·       Winter scene reconstruction from images/videos

·       Snow/ice measurements and analysis with computer vision

·       Real-time processing algorithms

·       Fusion of image/video data and other sensor data

·       Datasets, benchmarks and annotations of winter sport data

There will be two submission tracks: full papers and extended abstracts.

Full paper submissions should propose comprehensive and well-validated solutions, and adhere to the guidelines of standard WACV 2022 submissions (max 8 pages + references). Accepted full papers will be published under the WACV 2022 Workshops Proceedings and included in IEEE Xplore.

Extended abstracts should be max 4 pages in length (including tables, figures and references) and can describe novel but not extensively validated ideas, on-going works, or be recaps of recently published papers (either journal or conference). The accepted abstracts will be published under an arXiv compendium.

All submissions should be compiled for double-blind review, adopt the standard WACV 2022 template (https://www.overleaf.com/latex/templates/wacv-2022-author-kit-template/cpnsmqvrczmz), and be submitted via the workshop's CMT platform: https://cmt3.research.microsoft.com/CV4WS2022.

Important dates for full paper submissions:

·       November 1, 2021 23:59 PST: full paper submission due

·       November 14, 2021: notification to authors of full paper submissions

·       November 17, 2021 23:59 PST: camera-ready full papers due

Important dates for extended abstract submissions:

·       November 30, 2021 23:59 PST: extended abstract submission due

·       December 12, 2021: notification to authors of extended abstract submissions

·       December 15, 2021 23:59 PST: camera-ready extended abstract due

Workshop date:

·       January 8, 2022 (Morning)

 

Organizers

Matteo Dunnhofer, University of Udine, Italy

Prof. Nicola Conci, University of Trento, Italy

Prof. Christian Micheloni, University of Udine, Italy

 

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