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| * **Text GANs**: For a long time the Generative adversarial networks (GANs) were limited to differentiable data generation - notably images and videos. Recent advances allow text-generating GANs to be build. Lighter and easier to train than other solution capable of text generation, their capabilities remain unexpored as of now. The goal of this project would be implementing possible GAN architectures and evaluating their ability to generate texts with pre-set features - such as style or topics. You will need to have experience with scientific computing in Python, ideally with PyTorch experience, and ideally some experience with NLP. Contact [[https://people.epfl.ch/andrei.kucharavy|Andrei Kucharavy]] for more information. | * **Text GANs**: For a long time the Generative adversarial networks (GANs) were limited to differentiable data generation - notably images and videos. Recent advances allow text-generating GANs to be build. Lighter and easier to train than other solution capable of text generation, their capabilities remain unexpored as of now. The goal of this project would be implementing possible GAN architectures and evaluating their ability to generate texts with pre-set features - such as style or topics. You will need to have experience with scientific computing in Python, ideally with PyTorch experience, and ideally some experience with NLP. Contact [[https://people.epfl.ch/andrei.kucharavy|Andrei Kucharavy]] for more information. | ||
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| + | * **Byzantine-resilient heterogeneous GANs**: Byzantine-resilient federated learning has emerged as a major theme over the last couple of years, in grand part due to the need to distribute machine learning across many nodes, due to performance and privacy concerns. Until now it has focused on training a single model across many workers and many parameter serves. While this approach has brought on formidable results - including in GAN training, the topic of efficient, distributed and byzantine-resilient training of heterogeneous architectures remain relatively unexplored. In the context of Generative adversarial networks (GANs), such learning is critical to training light discriminators that can specialize in detecting specific featuers of generator-generated images. The goal of this project will be to investigate the potential for GAN training process poisonning by malicious discriminators and generators and investigate efficient protocols to ensure the training process robustness. You will need to have experience with scientific computing in Python, ideally with PyTorch experience, and notions of distributed computing. Contact [[https://people.epfl.ch/andrei.kucharavy|Andrei Kucharavy]] for more information. | ||
| ===== Semester Projects ===== | ===== Semester Projects ===== | ||