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[1805.10872] DeepProbLog: Neural Probabilistic Logic Programming
We introduce DeepProbLog, a probabilistic logic programming language that incorporates deep learning by means of neural predicates. We show how existing inference and learning techniques can be adapted for the new language. Our experiments demonstrate that DeepProbLog supports both symbolic and subsymbolic representations and inference, 1) program induction, 2) probabilistic (logic) programming, and 3) (deep) learning from examples. To the best of our knowledge, this work is the first to propose a framework where general-purpose neural networks and expressive probabilistic-logical modeling and reasoning are integrated in a way that exploits the full expressiveness and strengths of both worlds and can be trained end-to-end based on examples.
neural-net  logic-programming  probabilistic-programming 
17 days ago by arsyed
[1408.6474] Developments in Formal Proofs
This report describes three particular technological advances in formal proofs. The HOL Light proof assistant will be used to illustrate the design of a highly reliable system. Today, proof assistants can verify large bodies of advanced mathematics; and as an example, we turn to the formal proof in Coq of the Feit-Thompson Odd Order theorem in group theory. Finally, we discuss advances in the automation of formal proofs, as implemented in proof assistants such as Mizar, Coq, Isabelle, and HOL Light.
proof  mathematics  logic-programming  programming-language  representation  rather-interesting  nudge-targets  consider:representation 
march 2017 by Vaguery

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