1 citations · 1 across the 6 of their papers we have counts for
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cs.AI2020
Symbolic Learning and Reasoning with Noisy Data for Probabilistic Anchoring
Pedro Zuidberg Dos Martires, Nitesh Kumar, Andreas Persson +2
Robotic agents should be able to learn from sub-symbolic sensor data, and at the same time, be able to reason about objects and communicate with humans on a symbolic level. This ra…
cs.AI2020★ 1 cited
Learning Distributional Programs for Relational Autocompletion
Kumar Nitesh, Kuzelka Ondrej, De Raedt Luc
Relational autocompletion is the problem of automatically filling out some missing values in multi-relational data. We tackle this problem within the probabilistic logic programmin…