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DeepProbLog: Neural Probabilistic Logic Programming

Neural Information Processing Systems

We show how existing inference and learning techniques can be adapted for the new language. Our experiments demonstrate that DeepProbLog supports (i) both symbolic and subsymbolic representations and inference, (ii) program induction, (iii) probabilistic (logic)programming, and(iv)(deep)learningfromexamples.







A Theory of Optimistically Universal Online Learnability for General Concept Classes

Neural Information Processing Systems

Haussler et al. [1994], Ryabko [2006] researched the online learning problem with a mix of both restrictions. There are also substantial amount of papers investigating online learnability with all measurable functions but restricted data processes.



HyperbolicNeuralNetworks

Neural Information Processing Systems

Hyperbolic spaces have recently gained momentum in the context of machine learning due to their high capacity and tree-likeliness properties. However, the representational power of hyperbolic geometry is not yet on par with Euclidean geometry, mostly because of the absence of corresponding hyperbolic neural networklayers.