'Parameter learning' diaporamas de présentation

Knowledge Representation Meets Machine Learning: Part 2/3

666 views • 66 slides

Nevin L. Zhang Room 3504, phone: 2358-7015, Email: lzhang@cst.hk Home page

713 views • 58 slides

Can KR Represent Real-World Knowledge?

485 views • 48 slides

Efficient Learning of Statistical Relational Models

627 views • 45 slides

Statistical Learning

576 views • 37 slides

Parameter learning in Markov nets

436 views • 29 slides

Bayes network i nference

Bayes network i nference. A general scenario: Query variables: X Evidence ( observed ) variables and their values: E = e Unobserved variables: Y Inference problem : answer questions about the query variables given the evidence variables

278 views • 27 slides

Probabilistic graphical models and regulatory networks

Probabilistic graphical models and regulatory networks. BMI/CS 576 www.biostat.wisc.edu/bmi576.html Sushmita Roy sroy@biostat.wisc.edu Nov 27 th , 2012. Two main questions in regulatory networks. Sko1. Hot1. HSP12. X 2. X 1. Hot1. Sko1. BOOLEAN. LINEAR. DIFF. EQNS.

389 views • 26 slides

Selected Topics in Graphical Models

320 views • 23 slides

Sum-Product Networks

Sum-Product Networks. CS886 Topics in Natural Language Processing Guest Lecture by Pascal Poupart University of Waterloo July 22, 2015. Outline. What is a Sum-Product Network? Inference Language modeling. What is a Sum-Product Network?. Poon and Domingos , UAI 2011

257 views • 18 slides

Modeling Speech Acts and Joint Intentions in Markov Logic

Modeling Speech Acts and Joint Intentions in Markov Logic. Henry Kautz University of Washington. Goal. Infer and track the goals, intentions, and beliefs of the participants in a meeting Sources of information: Communicative actions Gestures Documents (agenda, minutes, emails)

214 views • 6 slides