[visionlist] [Conference] ACDL 2023, 6th Advanced Course on Data Science & Machine Learning - From Deep Learning to Foundation Models | June 10-14 | Riva del Sole Resort & SPA - Italy -> Early Registration: by April 23

Thu Apr 20 10:44:21 -04 2023

* Please forward to anybody who might be interested *

ACDL2023, An Interdisciplinary Course: From Deep Learning to Foundation
Models

If you want to learn
Transformers,
Large language models (e.g. GPT family, BERT, Megatron-Turing NLG, ...)
Vision - Large-scale vision models (e.g. MAE, SimCLR, ...)
Vision and language (e.g. DALL.E, ALIGN, CLIP, ...)
Beyond vision and language (e.g. video, Knowledge-Graph, structured data,
multilingual, ...)
and much more
then take part in ACDL 2023! ;-)

Riva del Sole Resort & SPA - Tuscany, Italy, June 10-14
https://acdl2023.icas.cc   acdl at icas.cc

EARLY REGISTRATION: by April 23
https://acdl2023.icas.cc/registration/
Oral Presentation Submission Deadline: by April 23 (AoE)

LECTURERS:
Each Lecturer will hold up to four lectures on one or more research topics.
https://acdl2023.icas.cc/lecturers/

Luca Beyer, Google Brain, Zürich, Switzerland
Lecture 1: "Large-Scale Pre-Training & Transfer in Computer Vision and
Vision-Text Models 1/2"
Lecture 2: "Large-Scale Pre-Training & Transfer in Computer Vision and
Vision-Text Models 2/2"
Lecture 3: "Transformers 1/2"
Lecture 4: "Transformers 2/2"

Aakanksha Chowdhery, Google Brain, USA
Lecture 1: TBA
Lecture 2: TBA

Thomas Kipf, Google Brain, USA
Lecture 1: "Graph Neural Networks 1/2"
Lecture 2: "Graph Neural Networks 2/2"
Lecture 3: "Structured Representation Learning for Perception 1/2"
Lecture 4: "Structured Representation Learning for Perception 2/2"

Pushmeet Kohli, DeepMind, London, UK
Lectures: TBA

Yi Ma, University of California, Berkeley, USA
Lecture 1: "An Overview of the Principles of Parsimony and
Self-Consistency: The Past, Present, and Future of Intelligence"
Lecture 2: "An Introduction to Low-Dimensional Models and Deep Networks"
Lecture 3: "Parsimony: White-box Deep Networks from Optimizing Rate
Reduction"
Lecture 4: "Self-Consistency: Closed-Loop Transcription of Low-Dimensional
Structures via Maximin Rate Reduction"

Gerhard Paass, Fraunhofer Institute -IAIS, Germany
Lecture 1: "Introduction to Foundation Models"
Lecture 2: "Foundation Models for Retrieval Applications"
Lecture 3: "Combining Foundation Models with External Text Resources"
Lecture 4: "Approaches to Increase Trustworthiness of Foundation Models2

Panos Pardalos, University of Florida, USA
Lecture 1: TBA
Lecture 2: TBA

Qing Qu, University of Michigan, USA
Lecture 1: "Low-Dimensional and Nonconvex Models for Shallow Representation
Learning"
Lecture 2: "Low-Dimensional Structures in Deep Representation Learning I"
Lecture 3: "Low-Dimensional Structures in Deep Representation Learning II"
Lecture 4: "Robust Learning of Overparameterized Networks via
Low-Dimensional Models"

Alex Smola, Amazon, USA (TBC)

Zoltan Szabo, LSE, London, UK
Lecture 1: "Shape-Constrained Kernel Machines and Their Applications"
Lecture 2: "Beyond Mean Embedding: The Power of Cumulants in RKHSs"

Michal Valko, DeepMind Paris & Inria France & ENS MVA
Lecture 1: "Reinforcement learning"
Lecture 2: "Deep Reinforcement Learning"
Lecture 3: "Learning by Bootstrapping: Representation Learning"
Lecture 4: "Learning by Bootstrapping: World Models"

TUTORIAL SPEAKERS:
Each Tutorial Speaker will hold more than four lessons on one or more
research topics.

Raphaël Berthier, EPFL, Switzerland
Lecture 1: "Implicit Regularization in Neural Networks"
Lecture 2: "Incremental Learning in Diagonal Linear Networks"
Lecture 3: "Approaches to Study Dynamics of Neural Networks"
Lecture 4: "The Fast-Slow Regime of Neural Networks"

Bruno Loureiro, École Normale Supérieure, France
Lectures 1-10: "Wonders of high-dimensions: the maths and physics of
Machine Learning"

Varun Ojha, Newcastle University, UK
Lecture 1: "Characterization of Deep Neural Networks"
Lecture 2: "Backpropagation Neural Tree"
Lecture 3: "Sensitivity Analysis of Deep Learning and Optimization
Algorithms"

https://acdl2023.icas.cc/lecturers/

PAST LECTURERS: https://acdl2023.icas.cc/past-lecturers/

ACDL 2023 VENUE:
Riva del Sole Resort & SPA
Località Riva del Sole – Castiglione della Pescaia (Grosseto)
CAP 58043 – Tuscany – Italy
p: +39-0564-928111
f: +39-0564-935607
e: events at rivadelsole.it
w: www.rivadelsole.it
https://acdl2023.icas.cc/venue/

PAST EDITIONS: https://acdl2023.icas.cc/past-editions/

REGISTRATION: https://acdl2023.icas.cc/registration/

CERTIFICATE & 8 ECTS:
PhD students, PostDocs, Industry Practitioners, Junior and Senior
Academics, and  will be typical profiles of the ACDL attendants.The Course
will involve a total of 36–40 hours of lectures, according to the academic
system the final achievement will be equivalent to 8 ECTS points for the
PhD Students (and some strongly motivated master student) attending the
Course.
At the end of the course, a formal certificate will be delivered indicating
the 8 ECTS points.

Anyone interested in participating in ACDL 2023 should register as soon as
possible. Similarly for accommodation at the Riva del Sole Resort & SPA
(the Course Venue), book your full board accommodation at the Riva del Sole
Resort & SPA as soon as possible. All course participants must stay at the
Riva del Sole Resort & SPA.

See you in Riva del Sole in June!
                                Giuseppe Nicosia & Panos Pardalos - ACDL
2023 Directors.

*6th Advanced Course on Data Science & Machine Learning - ACDL2023*
10-14 June
Riva del Sole Resort & SPA, Castiglione della Pescaia (Grosseto) – Tuscany,
Italy
An Interdisciplinary Course: Big Data, Deep Learning & AI without Borders
*Early Registration: by April 23 (AoE)*
W:  https://acdl2023.icas.cc/
E:   acdl at icas.cc
FB: https://www.facebook.com/groups/204310640474650/
T:    https://twitter.com/TaoSciences
The Course is equivalent to 8 ECTS points for the PhD Students and the
Master Students attending the Course.

*9th International Conference on machine Learning, Optimization & Data
science – LOD 2023 *September 22 – 26, 2023 – Grasmere, Lake District,
England – UK
*Paper Submission Deadline: May 10*
lod at icas.cc
https://lod2023.icas.cc/

*ACAIN 2023, the* *3rd International Advanced Course & Symposium on
Artificial Intelligence & Neuroscience*, September 22 – 26, 2023 –
Grasmere, Lake District, England – UK
*Paper Submission (Symposium): by April 26 (AoE)*
*Early Registration (Course): by April 26*
W:  https://acain2023.icas.cc/
E:   acain at icas.cc
FB:
https://www.facebook.com/ACAIN-Int-Advanced-Course-Symposium-on-AI-Neuroscience-100503321621692/
The Course is equivalent to 8 ECTS points for the PhD Students and the
Master Students attending the Course.

Il giorno gio 20 apr 2023 alle ore 15:08 Ioanna Koroni <
ioannakoroni at csd.auth.gr> ha scritto:

> The Artificial Intelligence and Information Analysis Laboratory (AIIA Lab, AIIA.CVML
> R&D group <https://aiia.csd.auth.gr/computer-vision-machine-learning/>)
> of the School of Informatics, Aristotle University of Thessaloniki, Greece
> (AUTH) has two open postdoctoral research positions. The interested
> applicant must have strong theoretical and/or applied background in machine
> learning and computer vision, with an emphasis on deep learning. Potential
> (not exclusive) application domains include robotics/autonomous systems and
> digital media.
>
> *Qualifications*
>
>    - PhD degree* in machine learning and/or computer vision.
>    - Strong publication record in well-known international journals and
>    conferences.
>    - Previous professional experience with international collaborative
>    research projects (e.g., Horizon 2020/Horizon Europe) is desirable.
>    - Good English writing skills.
>
> *Application*
>
> Interested candidates are kindly asked to send an e-mail to Prof. Ioannis
> Pitas *pitas at csd.auth.gr <pitas at csd.auth.gr>* with their Curriculum vitae
> and transcript of records.
>
>
> _______________________________________________
> visionlist mailing list
> visionlist at visionscience.com
> http://visionscience.com/mailman/listinfo/visionlist_visionscience.com
>
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