Search Results for author:
Found 3 papers, 0 papers with code
Deep Inductive Logic Programming meets Reinforcement Learning
One approach to explaining the hierarchical levels of understanding within a machine learning model is the symbolic method of inductive logic programming (ILP), which is data efficient and capable of learning first-order logic rules that can entail data behaviour.
Explainability in Machine Learning: a Pedagogical Perspective
We provide a pedagogical perspective on how to structure the learning process to better impart knowledge to students and researchers in machine learning, when and how to implement various explainability techniques as well as how to interpret the results.
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.
