Lipizzaner framework is the backbone of several research studies about GAN training that have been published in different fora.
Journals and book chapters:
- E. Hemberg, J. Toutouh, U. O’Reilly. Spatial Coevolution for Diverse Training of Generative Adversarial Networks. ACM Transactions on Evolutionary Learning and Optimization, pages 25, Springer (Under review).
- J. Toutouh, E. Hemberg, U. O’Reilly. Data Dieting in GAN Training. In: Iba H., Noman N. (eds) Deep Neural Evolution. Natural Computing Series. Springer, Singapore. DOI: 10.1007/978-981-15-3685-4_14
- M. Esteban, J. Toutouh, S. Nesmachnow. Parallel/distributed generative adversarial neural networks for data augmentation of COVID-19 training images. In the Latin America High Performance Computing Conference (CARLA 2020). (Under review).
- J. Toutouh, E. Hemberg, U. O’Reilly. Analyzing the Components of Distributed Coevolutionary GAN Training. In The Sixteenth International Conference on Parallel Problem Solving from Nature (PPSN XVI). pages. 10, 2020. arxiv.org/abs/2008.01124
- J. Toutouh, E. Hemberg, U. O’Reilly. Re-purposing Heterogeneous Generative Ensembles with Evolutionary Computation. In Genetic and Evolutionary Computation Conference (GECCO ’20), pages. 10, 2020. DOI: 10.1145/3377930.3390229
- E. Perez, S. Nesmachnow, J. Toutouh, E. Hemberg, U. O’Reilly. Parallel/distributed implementation of cellular training for generative adversarial neural networks. In 10th IEEE Workshop Parallel/Distributed Combinatorics and Optimization (PDCO 2020), pages 7, 2020.DOI: 10.1109/IPDPSW50202.2020.00092
- Jamal Toutouh, Erik Hemberg, and Una-May O’Reilly. Spatial Evolutionary Generative Adversarial Networks. In Genetic and Evolutionary Computation Conference (GECCO ’19), July 13–17, 2019, Prague, Czech Republic. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/3321707.3321860
- A. Al-Dujaili, T. Schmiedlechner, E. Hemberg, U. O’Reilly. Towards distributed coevolutionary GANs. In AAAI 2018 Fall Symposium, 2018.
- T. Schmiedlechner, I. Ng Zhi Yong, A. Al-Dujaili, E. Hemberg, U. O’Reilly. Lipizzaner: A System That Scales Robust Generative Adversarial Network Training. In NeurIPS 2018 Workshop on System for Machine Learning, 2018.
- A System that Scales Robust Generative Adversarial Network Training presented in the MIT College of Computing poster session 2019.
- Mustangs: Robust Training of Generative Adversarial Networks by Fostering Diversity presented in the MIT-IBM Watson AI Lab networking and poster reception 2019.
- Coevolutionary GANs Training to Foster Diversity presented in the GANocracy: Democratizing GANs 2019.
Tutorials and invited talks:
- Lipizzaner: Distributed Coevolution for Resilient Generative Adversarial Networks (GAN) Training, Jamal Toutouh, Universidad de la Republica, Montevideo Uruguay, April 2020.
- Deep Neuroevolution applied to Generative Adversarial Networks, Jamal Toutouh, Spain AI, April 2020.
- Spatial Coevolutionary Deep Neural Networks Training. Jamal Toutouh. Universidad de la Republica, Montevideo Uruguay, May 2019.