Dear Colleague(s),
Thus, below you will find the extended deadline call for papers for EGML-EC 2024 – The Third workshop on Enhancing Generative Machine Learning with Evolutionary Computation.
Extended deadline: 12 April.
https://sites.google.com/view/egml-ec2024
Feel free to distribute, and thank you for your time.
Best regards,
The Workshop Chairs
Jamal Toutouh
Una-May O’Reilly
João Correia
Penousal Machado
Erik Hemberg
———————————————————————-
CALL FOR PAPERS
3rd Workshop on Enhancing Generative Machine Learning with Evolutionary Computation
https://sites.google.com/view/egml-ec2024
Genetic and Evolutionary Computation Conference (GECCO'24)
Melbourne, Australia, July 14 to 18, 2024
.Overview and Scope
Generative Machine Learning has become a key field in machine learning and deep learning. In recent years, this field of research has proposed many deep generative models (DGMs) that range from a broad family of methods such as large language models (LLMs), generative adversarial networks (GANs), variational autoencoders (VAEs), Transformers, autoregressive (AR) models and stable diffusion models (SD). Although these methods have achieved state-of-the-art results in the generation of synthetic data of different types, such as images, speech, text, molecules, video, etc., Deep generative models are still difficult to train, optimize, and fine tune.
There are still open problems, such as the vanishing gradient and mode collapse in DGMs, which limit their performance. Although there are strategies to minimize the effect of those problems, they remain fundamentally unsolved. In recent years, evolutionary computation (EC) and related bio-inspired techniques (e.g. particle swarm optimization) and in the form of Evolutionary Machine Learning approaches have been successfully applied to mitigate the problems that arise when training DGMs, leveraging the quality of the results to impressive levels. Among other approaches, these new solutions include LLM, GAN, VAE, AR, and SD training methods or fine tuning optimization based on evolutionary and coevolutionary algorithms, the combination of deep neuroevolution with training approaches, and the evolutionary exploration of latent space.
The workshop on Enhancing Generative Machine Learning with Evolutionary Computation (EGML-EC) aims to act as a medium for debate, exchange of knowledge and experience, and encourage collaboration for researchers focused on DGMs and the EC community. Bringing these two communities together will be essential for making significant advances in this research area. Thus, this workshop provides a critical forum for disseminating the experience on the topic of enhancing generative modeling with EC, presenting new and ongoing research in the field, and to attract new interest from our community.
.Topics of Interest
-Particular topics of interest are (not exclusively):
-Evolutionary prompt optimization for large language models
-Evolutionary operators based on large language models
-Evolutionary and co-evolutionary algorithms to train deep generative models
-EC-based optimization of hyper-parameters for deep generative models
-Neuroevolution applied to train deep generative architectures
-Dynamic EC-based evolution of deep generative models training parameters
-Evolutionary latent space exploration (e.g. LVEs)
-Real-world applications of EC-based deep generative models solutions
-Multi-criteria adversarial training of deep generative models
-Evolutionary generative adversarial learning models
-Software libraries and frameworks for deep generative models applying EC
All accepted papers of this workshop will be included in the Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'24) Companion Volume.
.Important Dates
Submission opening: February 12, 2024
Submission deadline: April 12, 2024
Acceptance notification: May 3, 2024
Camera-ready and registration: May 10, 2024
Workshop date: TBC depending on GECCO program schedule (July 14 or 18, 2024)
.Instructions for Authors
We invite submissions of two types of paper:
· Regular papers (limit 8 pages)
· Short papers (limit 4 pages)
Papers should present original work that meets the high-quality standards of GECCO. Each paper will be rigorously evaluated in a review process. Accepted papers appear in the ACM digital library as part of the Companion Proceedings of GECCO. Each paper accepted needs to have at least one author registered by the author registration deadline. Papers must be submitted via the online submission system https://ssl.linklings.net/conferences/gecco/. Please refer to https://gecco-2024.sigevo.org/Paper-Submission-Instructions for more detailed instructions.
As a published ACM author, you and your co-authors are subject to all ACM Publications Policies (https://www.acm.org/publications/policies/toc), including ACM's new Publications Policy on Research Involving Human Participants and Subjects (https://www.acm.org/publications/policies/research-involving-human-participants-and-subjects).
Workshop Chairs
· Jamal Toutouh, Univ. of Málaga (ES) – MIT (USA), jamal@lcc.uma.es
· Una-May O’Reilly, MIT (USA), unamay@csail.mit.edu
· João Correia, University of Coimbra (PT), jncor@dei.uc.pt
· Penousal Machado, University of Coimbra (PT), machado@dei.uc.pt
· Erik Hemberg, MIT (USA), hembergerik@csail.mit.edu
More information at:
Dear Colleague(s),
Thus, below you will find the extended deadline call for papers for EGML-EC 2024 – The Third workshop on Enhancing Generative Machine Learning with Evolutionary Computation.
Extended deadline: 12 April.
https://sites.google.com/view/egml-ec2024
Feel free to distribute, and thank you for your time.
Best regards,
The Workshop Chairs
Jamal Toutouh
Una-May O’Reilly
João Correia
Penousal Machado
Erik Hemberg
———————————————————————-
CALL FOR PAPERS
3rd Workshop on Enhancing Generative Machine Learning with Evolutionary Computation
https://sites.google.com/view/egml-ec2024
Genetic and Evolutionary Computation Conference (GECCO'24)
Melbourne, Australia, July 14 to 18, 2024
.Overview and Scope
Generative Machine Learning has become a key field in machine learning and deep learning. In recent years, this field of research has proposed many deep generative models (DGMs) that range from a broad family of methods such as large language models (LLMs), generative adversarial networks (GANs), variational autoencoders (VAEs), Transformers, autoregressive (AR) models and stable diffusion models (SD). Although these methods have achieved state-of-the-art results in the generation of synthetic data of different types, such as images, speech, text, molecules, video, etc., Deep generative models are still difficult to train, optimize, and fine tune.
There are still open problems, such as the vanishing gradient and mode collapse in DGMs, which limit their performance. Although there are strategies to minimize the effect of those problems, they remain fundamentally unsolved. In recent years, evolutionary computation (EC) and related bio-inspired techniques (e.g. particle swarm optimization) and in the form of Evolutionary Machine Learning approaches have been successfully applied to mitigate the problems that arise when training DGMs, leveraging the quality of the results to impressive levels. Among other approaches, these new solutions include LLM, GAN, VAE, AR, and SD training methods or fine tuning optimization based on evolutionary and coevolutionary algorithms, the combination of deep neuroevolution with training approaches, and the evolutionary exploration of latent space.
The workshop on Enhancing Generative Machine Learning with Evolutionary Computation (EGML-EC) aims to act as a medium for debate, exchange of knowledge and experience, and encourage collaboration for researchers focused on DGMs and the EC community. Bringing these two communities together will be essential for making significant advances in this research area. Thus, this workshop provides a critical forum for disseminating the experience on the topic of enhancing generative modeling with EC, presenting new and ongoing research in the field, and to attract new interest from our community.
.Topics of Interest
-Particular topics of interest are (not exclusively):
-Evolutionary prompt optimization for large language models
-Evolutionary operators based on large language models
-Evolutionary and co-evolutionary algorithms to train deep generative models
-EC-based optimization of hyper-parameters for deep generative models
-Neuroevolution applied to train deep generative architectures
-Dynamic EC-based evolution of deep generative models training parameters
-Evolutionary latent space exploration (e.g. LVEs)
-Real-world applications of EC-based deep generative models solutions
-Multi-criteria adversarial training of deep generative models
-Evolutionary generative adversarial learning models
-Software libraries and frameworks for deep generative models applying EC
All accepted papers of this workshop will be included in the Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'24) Companion Volume.
.Important Dates
Submission opening: February 12, 2024
Submission deadline: April 12, 2024
Acceptance notification: May 3, 2024
Camera-ready and registration: May 10, 2024
Workshop date: TBC depending on GECCO program schedule (July 14 or 18, 2024)
.Instructions for Authors
We invite submissions of two types of paper:
· Regular papers (limit 8 pages)
· Short papers (limit 4 pages)
Papers should present original work that meets the high-quality standards of GECCO. Each paper will be rigorously evaluated in a review process. Accepted papers appear in the ACM digital library as part of the Companion Proceedings of GECCO. Each paper accepted needs to have at least one author registered by the author registration deadline. Papers must be submitted via the online submission system https://ssl.linklings.net/conferences/gecco/. Please refer to https://gecco-2024.sigevo.org/Paper-Submission-Instructions for more detailed instructions.
As a published ACM author, you and your co-authors are subject to all ACM Publications Policies (https://www.acm.org/publications/policies/toc), including ACM's new Publications Policy on Research Involving Human Participants and Subjects (https://www.acm.org/publications/policies/research-involving-human-participants-and-subjects).
Workshop Chairs
· Jamal Toutouh, Univ. of Málaga (ES) – MIT (USA), jamal@lcc.uma.es
· Una-May O’Reilly, MIT (USA), unamay@csail.mit.edu
· João Correia, University of Coimbra (PT), jncor@dei.uc.pt
· Penousal Machado, University of Coimbra (PT), machado@dei.uc.pt
· Erik Hemberg, MIT (USA), hembergerik@csail.mit.edu
More information at:
