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ACDL 2023 : 6th Advanced Course on Data Science & Machine Learning


When Jun 10, 2023 - Jun 14, 2023
Where Riva del Sole Resort & SPA - Tuscany
Submission Deadline May 23, 2023
Categories    artificial intelligence   deep learning   machine learning   data science

Call For Papers

The 6th Advanced Course on Data Science & Machine Learning – ACDL 2023 (June 10-14) is a full-immersion five-day Course at the Riva del Sole Resort & SPA (Castiglione della Pescaia – Grosseto – Tuscany, Italy) on cutting-edge advances in Data Science and Deep Learning Learning with lectures delivered by world-renowned experts. The Course provides a stimulating environment for junior and senior academics, early career researches, Post-Docs, PhD students and industry leaders. Participants will also have the chance to present their results with talks, and to interact with their colleagues, in a convivial and productive environment.

MSc students, 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 the Master Students attending the Course.

Each Lecturer will hold up to four lectures on one or more research topics.

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: "PaLM-E: An Embodied Language Model"
Lecture 2: "Efficiently Scaling Large Model Inference"

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 : "Diffusion capacity of single and interconnected networks"

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"

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"

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

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"


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