International Meeting on Foundation Models – IMFM 2023
“Foundation Models – From Theory to Industrial Applications”
The Wordsword Hotel & SPA – Grasmere – Lake District, England
Lecture Hall: Coleridge Suite
September 23-25, 2023
- Language – Large Language Models (e.g., GPT family, BERT, Megatron-Turing NLG, …)
- Vision – Large 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, …)
Keynote Speakers:
“Evaluating the Commonsense Reasoning abilities of Foundation Models“, Anthony G. Cohn, University of Leeds, UK The Alan Turing Institute, UK
“Foundation Models”, Sven Giesselbach, Fraunhofer Institute – IAIS, Germany
“GPT”, Sven Giesselbach, Fraunhofer Institute – IAIS, Germany
“OpenGPT-X and Application and Practical Training of Large Scale Language Models”, Sven Giesselbach, Fraunhofer Institute – IAIS, Germany
Important Dates
- Workshop paper submission deadline:
Saturday June 10, 2023 (AoE) - Workshop paper acceptance decision to authors: by
Monday July 10, 2023 - Camera Ready Submission Deadline: by
Thursday July 20, 2023 - All workshop papers must be submitted using EasyChair.
- Workshop dates: September 25-26, 2023
All accepted papers will be published in a volume of the series on Lecture Notes in Computer Science (LNCS) from Springer – Nature after the conference (Conference Post-Proceedings).
The call for papers (CfP) for the LOD 2023 workshop on “Foundation Models” is the same CfP used for the entire LOD 2023 conference.
Organizer and Chair: Giuseppe Nicosia (giuseppe.nicosia.1@gmail.com)
LOD 2023 Tracks
- Track on “AI for Fintech“
- Track on “AI for Genome-scale Models“
- Track on “AI for Medicine and Biology“
- Track on “AI for Sustainability“
- Track on “AI to help to fight Climate Change“
- Track on “Artificial General Intelligence“
- Track on “Biologically Plausible Learning“
- Track on “Data Science for Sustainable Cities“
- Track on “Deep Learning for Bioengineering and Synthetic Biology“
- Track on “Deep Learning for Economic Applications“
- Track on “Deep Learning for Genomics“
- Track on “Deep Learning for Graphs“
- Track on “Deep Neuroevolution“
- Track on “Generative Adversarial Networks“
- Track on “Generative Artificial Intelligence“
- Track on “Geometric Deep Learning“
- Track on “Integrative Machine Learning“
- Track on “Large Language Models” part of the Workshop on “Foundation Models“
- Track on “Large Vision Models” part of the Workshop on “Foundation Models“
- Track on “Vision and Language” part of the Workshop on “Foundation Models“
- Track on “Beyond Vision and Language” part of the Workshop on “Foundation Models“
- Track on “Multi-Objective Optimization“
- Track on “Multi-Task Learning“
- Track on “Reinforcement Learning“
- Track on “AI for Network/Cloud Management” (TBC)
