talks

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talks [2016/10/26 11:13] luciro |
talks [2017/03/10 23:16] (current) luciro |
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====== Talks ====== | ====== Talks ====== | ||

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+ | ===== Neural-Symbolic Systems for Verification, Run-Time Monitoring and Learning ===== | ||

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+ | Artur Garcez | ||

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+ | Neural-symbolic systems combine symbolic reasoning and neural computation. They have been shown capable of outperforming purely symbolic and purely connectionist machine learning by using symbolic background knowledge with data-driven learning in neural networks. A neural-symbolic system translates symbolic knowledge into the initial structure of a neural network, which can be trained from examples in the usual way. Learning expands or revises that knowledge. Knowledge extraction closes the cycle by producing a revised symbolic description of the network. Neural-symbolic systems exist for logic programming, answer-set programming, modal and intuitionistic logic, nonmonotonic logic, temporal logic, first-order logic, etc. In this talk, I will review briefly the developments in the area and how neural-symbolic systems can be used with model checking for adapting system descriptions. I will also exemplify how system properties described in temporal logic can be encoded in a neural network, which can be used for run-time monitoring of a system in the presence of deviations from the system specification. I will conclude with a brief presentation of some recent achievements and discuss the challenges in knowledge extraction from neural networks, relational learning in neural networks, and symbolic reasoning in deep networks. | ||

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+ | Slides: {{:garcez2017.pdf|}} | ||

===== SMT-based Verification Applied to Non-convex Optimization Problems ===== | ===== SMT-based Verification Applied to Non-convex Optimization Problems ===== |

talks.txt ยท Last modified: 2017/03/10 23:16 by luciro