Parameter Learning in Probabilistic Databases: A Least Squares Approach
By Bernd Gutmann, Angelika Kimmig, Kristian Kersting, Luc De Raedt. 2008
74 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Management Science and Operations Research, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Angelika Kimmig
- Luc De Raedt
- Sebastijan Dumancic
- Robin Manhaeve
- Thomas Demeester
- Guy Van den Broeck
- Vaishak Belle
- Kristian Kersting
In Management Science and Operations Research
In Signal Processing
Citations in the corpus
- DeepProbLog: Neural Probabilistic Logic Programming (2018, methodology): “[21], we use gradient descent rather than EM.”
- Under Consideration for Publication in Theory and Practice of Logic Programming on the Implementation of the Probabilistic Logic Programming Language Problog (unknown): “(Gutmann et al. 2008) therefore introduces the k -probability P k (q|T ), which approximates the success probability by using the k -best (that is, the k most likely) explanations instead of all proofs when building the DNF formula used in Equation (7): P k (q|T ) = P e∈E k (q) bi ∈var (e) b i…”
- An Algebraic Prolog for Reasoning about Possible Worlds (2011, methodology): “As a final example, the gradient of the success probability, as used for parameter learning in ProbLog (Gutmann et al. 2008), can directly be calculated in aProbLog as well, using the gradient semiring (Eisner 2002).”