DeepPSL: End-to-end perception and reasoning with applications to zero shot learning

• URL: http://arxiv.org/abs/2109.13662v2
• Date: Wed, 29 Sep 2021 08:19:22 GMT
• Title: DeepPSL: End-to-end perception and reasoning with applications to zero shot learning
• Authors: Nigel Duffy, Sai Akhil Puranam, Sridhar Dasaratha, Karmvir Singh Phogat, Sunil Reddy Tiyyagura
• Abstract summary: We produce an end-to-end trainable system that integrates reasoning and perception. DeepPSL is a variant of Probabilistic Soft Logic (PSL) We evaluate DeepPSL on a zero shot learning problem in image classification.
• Score: 1.1124588036301817
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We introduce DeepPSL a variant of Probabilistic Soft Logic (PSL) to produce an end-to-end trainable system that integrates reasoning and perception. PSL represents first-order logic in terms of a convex graphical model -- Hinge Loss Markov random fields (HL-MRFs). PSL stands out among probabilistic logic frameworks due to its tractability having been applied to systems of more than 1 billion ground rules. The key to our approach is to represent predicates in first-order logic using deep neural networks and then to approximately back-propagate through the HL-MRF and thus train every aspect of the first-order system being represented. We believe that this approach represents an interesting direction for the integration of deep learning and reasoning techniques with applications to knowledge base learning, multi-task learning, and explainability. We evaluate DeepPSL on a zero shot learning problem in image classification. State of the art results demonstrate the utility and flexibility of our approach.

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