Speeding-up ProbLog's Parameter Learning
Francisco H. O. V. de Faria, Arthur C. Gusm\~ao, Fabio G. Cozman,, Denis D. Mau\'a
TL;DR
This paper introduces methods to significantly accelerate ProbLog's parameter learning process, especially with complete data, by providing insights that lead to substantial speed improvements.
Contribution
The paper presents novel insights and techniques that drastically enhance the speed of ProbLog's parameter learning algorithm for complete data.
Findings
Speed improvements of up to orders of magnitude
Effective techniques for faster parameter learning
Applicable to complete data scenarios
Abstract
ProbLog is a state-of-art combination of logic programming and probabilities; in particular ProbLog offers parameter learning through a variant of the EM algorithm. However, the resulting learning algorithm is rather slow, even when the data are complete. In this short paper we offer some insights that lead to orders of magnitude improvements in ProbLog's parameter learning speed with complete data.
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Taxonomy
TopicsTime Series Analysis and Forecasting · Anomaly Detection Techniques and Applications · Data Mining Algorithms and Applications
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings