Titlebook: Mathematical Aspects of Quantum Field Theories; Damien Calaque,Thomas Strobl Book 2015 Springer International Publishing Switzerland 2015

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ral-Symbolic Learning Systems. .P is an example of such a system. In order to enable effective learning from examples and background knowledge, the main insight was to keep the network structure as simple as possible, and try to find the best symbolic representation for it. We have done so by presen

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David Li-Bland,Pavol Ševera these hybrid models, these methods use a knowledge compiler to turn the symbolic model into a differentiable arithmetic circuit, after which gradient descent can be performed. However, these methods require compiling a reasonably sized circuit, which is not always possible, as for many symbolic pro

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Ivan Contrerasmodels clearly capable of convincingly faking true reasoning behavior, the question of whether they are also capable of real reasoning—and how the difference should be defined—becomes increasingly vexed. Here we introduce a new tool, Logic Tensor Probes (LTP), that may help to shed light on the prob

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antees. The challenge is how to effectively integrate neural and symbolic computation, to enable learning and reasoning from raw data. Existing pipelines that train the neural and symbolic components sequentially require extensive labelling, whereas end-to-end approaches are limited in terms of scal