![]() ![]() Since 2018, he is a Post-doctoral Researcher at IBM Research – Zurich. In 2018, he received the Fritz Kutter Award of ETH Zurich and IBM Research Division Award for contributions in neuromorphic computing using phase-change memory devices. in 2017 from École Polytechnique Fédérale de Lausanne. Stanisław Woźniak pursues his research in the field of neuromorphic computing, taking inspiration from the neural networks in the brain to develop novel computing architectures. This includes unsupervised word representation learning as implemented in FastText, unsupervised feature learning for images using clustering losses, and initial experiments on control tasks such as navigation in visually challenging environments. In this talk, I will present some of the work we did in that direction at FAIR. Recent work has shown that for many problems, so-called self-supervised learning allows obtaining great features while only using the raw data, alleviating the need for costly labeling. A typical success story was the use of CNNs for computer vision tasks, which was fueled by the construction of ImageNet. However, good performance is conditioned on the obtention of high-quality manual labels for the task at hand. A graduate of École Polytechnique, where he got a Masters Degree in Mathematics, Machine Learning and Computer Vision (MVA)ĭeep-learning systems trained on huge collections of annotated data have proven extremely useful in solving complex tasks and helping build great applications. ![]() in Computer Science while working in the Willow team where he was involved in Human Action Detection in Videos with Aligned Text. Before joining Facebook, in 2016, he received his Ph.D. Co-creator of the fastText library designed to help build scalable text representation and classification solutions. His main research interests are focused on large-scale unsupervised learning. He is interested in applications of machine learning to Computer Vision and Natural Language Processing. Piotr Bojanowski is Research Scientist at Facebook AI Research (FAIR). ![]()
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