Probabilistic Reasoning via Deep Learning: Neural Association Models

@article{Liu2016ProbabilisticRV,
  title={Probabilistic Reasoning via Deep Learning: Neural Association Models},
  author={Quan Liu and Hui Jiang and Zhen-Hua Ling and Si Wei and Yu Hu},
  journal={CoRR},
  year={2016},
  volume={abs/1603.07704}
}
In this paper, we propose a new deep learning approach, called neural association model (NAM), for probabilistic reasoning in artificial intelligence. We propose to use neural networks to model association between any two events in a domain. Neural networks take one event as input and compute a conditional probability of the other event to model how likely these two events are associated. The actual meaning of the conditional probabilities varies between applications and depends on how the… CONTINUE READING
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