Fredrik Hellström
Fredrik Hellström is a PhD student in the Communication systems group. His research is focused on the theory of deep neural networks, a type of machine learning. Deep neural networks are used for purposes like computer vision and natural language processing, with possible applications ranging from board games to autonomous cars.
Showing 8 publications
New Family of Generalization Bounds Using Samplewise Evaluated CMI
Information-Theoretic Generalization Bounds: Tightness and Expressiveness
Evaluated CMI Bounds for Meta Learning: Tightness and Expressiveness
Fast-Rate Loss Bounds via Conditional Information Measures with Applications to Neural Networks
Generalization Bounds via Information Density and Conditional Information Density
Generalization Error Bounds via mth Central Moments of the Information Density
New constraints on inelastic dark matter from IceCube
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