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My research is focused on learning algorithms for neural networks to tackle problems with continuous and sparse data. Under these assumptions, the trade-off between model bias and sample efficiency lies at the heart of my interests in biological and artificial neural networks, statistical learning theory and reinforcement learning. In particular, during my PhD I am developing tools around a class of neural network architectures termed HyperNetworks that can incorporate model design and its optimization into a single framework.
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Simon Schug,Seijin Kobayashi, Yassir Akram, Maciej Wołczyk, Alexandra Proca,Johannes von Oswald,Razvan Pascanu,João Sacramento,Angelika Steger
CoRR (2023)
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