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Found 4 papers, 1 papers with code
Deep End-to-end Causal Inference
Causal inference is essential for data-driven decision making across domains such as business engagement, medical treatment and policy making.
FCause: Flow-based Causal Discovery
Current causal discovery methods either fail to scale, model only limited forms of functional relationships, or cannot handle missing values.
Sparse Uncertainty Representation in Deep Learning with Inducing Weights
Bayesian neural networks and deep ensembles represent two modern paradigms of uncertainty quantification in deep learning.
Alexandria: Unsupervised High-Precision Knowledge Base Construction using a Probabilistic Program
The use of a probabilistic program allows uncertainty in the text to be propagated through to the retrieved facts, which increases accuracy and helps merge facts from multiple sources.
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