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Learning Probabilistic Logic Programs in Continuous Domains
The field of statistical relational learning aims at unifying logic and probability to reason and learn from data.
Tractable Querying and Learning in Hybrid Domains via Sum-Product Networks
By leveraging local structure, representations such as sum-product networks (SPNs) can capture high tree-width models with many hidden layers, essentially a deep architecture, while still admitting a range of probabilistic queries to be computable in time polynomial in the network size.
What data do we need for training an AV motion planner?
If cheaper sensors could be used for collection instead, data availability would go up, which is crucial in a field where data volume requirements are large and availability is small.
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