Differences
This shows you the differences between two versions of the page.
| Both sides previous revision Previous revision Next revision | Previous revision Next revision Both sides next revision | ||
|
education [2020/10/29 18:23] fablpd |
education [2020/11/24 12:02] fablpd |
||
|---|---|---|---|
| Line 42: | Line 42: | ||
| * **Robust Distributed Machine Learning**: With the proliferation of big datasets and models, Machine Learning is becoming distributed. Following the standard parameter server model, the learning phase is taken by two categories of machines: parameter servers and workers. Any of these machines could behave arbitrarily (i.e., said Byzantine) affecting the model convergence in the learning phase. Our goal in this project is to build a system that is robust against Byzantine behavior of both parameter server and workers. Our first prototype, AggregaThor(https://mlsys.org/Conferences/2019/doc/2019/54.pdf), describes the first scalable robust Machine Learning framework. It fixed a severe vulnerability in TensorFlow and it showed how to make TensorFlow even faster, while robust. Contact [[https://people.epfl.ch/arsany.guirguis|Arsany Guirguis]] for more information. | * **Robust Distributed Machine Learning**: With the proliferation of big datasets and models, Machine Learning is becoming distributed. Following the standard parameter server model, the learning phase is taken by two categories of machines: parameter servers and workers. Any of these machines could behave arbitrarily (i.e., said Byzantine) affecting the model convergence in the learning phase. Our goal in this project is to build a system that is robust against Byzantine behavior of both parameter server and workers. Our first prototype, AggregaThor(https://mlsys.org/Conferences/2019/doc/2019/54.pdf), describes the first scalable robust Machine Learning framework. It fixed a severe vulnerability in TensorFlow and it showed how to make TensorFlow even faster, while robust. Contact [[https://people.epfl.ch/arsany.guirguis|Arsany Guirguis]] for more information. | ||
| + | |||
| * **Consistency in global-scale storage systems**: We offer several projects in the context of storage systems, ranging from implementation of social applications (similar to [[http://retwis.redis.io/|Retwis]], or [[https://github.com/share/sharejs|ShareJS]]) to recommender systems, static content storage services (à la [[https://www.usenix.org/legacy/event/osdi10/tech/full_papers/Beaver.pdf|Facebook's Haystack]]), or experimenting with well-known cloud serving benchmarks (such as [[https://github.com/brianfrankcooper/YCSB|YCSB]]); please contact [[http://people.epfl.ch/dragos-adrian.seredinschi|Adi Seredinschi]] or [[https://people.epfl.ch/karolos.antoniadis|Karolos Antoniadis]] for further information. | * **Consistency in global-scale storage systems**: We offer several projects in the context of storage systems, ranging from implementation of social applications (similar to [[http://retwis.redis.io/|Retwis]], or [[https://github.com/share/sharejs|ShareJS]]) to recommender systems, static content storage services (à la [[https://www.usenix.org/legacy/event/osdi10/tech/full_papers/Beaver.pdf|Facebook's Haystack]]), or experimenting with well-known cloud serving benchmarks (such as [[https://github.com/brianfrankcooper/YCSB|YCSB]]); please contact [[http://people.epfl.ch/dragos-adrian.seredinschi|Adi Seredinschi]] or [[https://people.epfl.ch/karolos.antoniadis|Karolos Antoniadis]] for further information. | ||
| Line 47: | Line 48: | ||
| \\ | \\ | ||
| + | * **Consistency in global-scale storage systems**: Generative adversarial networks (GANs) have achieved some spectacular results in the six years since they have been introduced. However, their training process is still fraught with issues such as mode collapse, non-convegence or gradients collapse. Those issues have still not been completely resolved, making multiple restarts and selection of well-performing Generator-Discriminator pairs part of GAN training process. The process of adversarial generator-discriminator training is not dissimilar from co-evolution of two adversarial species - such as for instance hosts and pathogens, except that rounds of mutation/recombination/selection in search of fitness optimum are replaced by gradient descent. Our goal is to investigate - both experimentally and theoretically - if we can further stabilize and improve GAN training with evolutionary mechanisms, such as speciation, aneuploidization, neutral variability buffering or meta-evolutionary mechanisms. This work wold have implications on developing efficient solutions to detecting GAN products (aka deepfakes). You will need to have experience with scientific computing in Python, ideally with PyTorch experience, and ideally you should have some knowledge of population genetics. Contact [[https://people.epfl.ch/andrei.kucharavy|Andrei Kucharavy]] for more information. | ||