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Found 4 papers, 1 papers with code
A Bayesian Topic Model for Human-Evaluated Interpretability
Given a nonparametric topic model, we can include weakly-supervised input using novel modifications to the nonparametric generative model.
Parallel development of social preferences in fish and machines
We found that when artificial fish had two core learning mechanisms (reinforcement learning and curiosity-driven learning), artificial fish developed fish-like social preferences.
OpBerg: Discovering causal sentences using optimal alignments
Current methods for extracting causal sentences are based on either machine learning or a predefined database of causal terms.
Source-LDA: Enhancing probabilistic topic models using prior knowledge sources
The approach taken is to complement the existing topic distributions over words with a known distribution based on a predefined set of topics.
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