Knowledge Infusion

67 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Neural-Symbolic Computing: An Effective Methodology for Principled Integration of Machine Learning and Reasoning (2019, background): “The integration of learning and reasoning through neural-symbolic computing has been an active branch of AI research for several years [14, 16, 17, 21, 25, 42, 53].”
Simultaneous Private Learning of Multiple Concepts (2015, background): “[28] L. G. Valiant.”
AI Reasoning Systems: PAC and Applied Methods (2018, background): “inciple of explicit rules (and also require the form of the rules to be given). Valiant even brings up this issue in his original Knowledge Infusion paper and brings up 3 reasons in defense of rules. [19] 1. Chaining rules allows the algorithm to make inference about situations too infrequent to hav”
Neurosymbolic AI: the 3rd wave (2020, unknown): “Neural-symbolic computing has been an active area of research seeking to establish such a bridge for several years [4, 26, 16, 20, 35, 43, 80, 93].”
Propositional Knowledge Representation and Reasoning in Restricted Boltzmann Machines. (2017, background): “Despite their difference, there is a strong argument that combination of the two should offer joint benefits (Smolensky 1995; Valiant 2006; Garcez, Lamb, and Gabbay 2008).”