Learning of Tractable Probabilistic Models with Application to Multilabel Classifi...
A study on structural learning algorithms for probabilistic circuits
Inference and learning algorithms for probabilistic logic programming
Grant number: | 21/09990-0 |
Support Opportunities: | Scholarships in Brazil - Scientific Initiation |
Start date: | October 01, 2021 |
End date: | July 31, 2022 |
Field of knowledge: | Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques |
Principal Investigator: | Denis Deratani Mauá |
Grantee: | Jonas Rodrigues Lima Gonçalves |
Host Institution: | Instituto de Matemática e Estatística (IME). Universidade de São Paulo (USP). São Paulo , SP, Brazil |
Abstract This is a proposal for a Scientific Initiation project that aims to study Probabilistic Circuits, which are parametric probabilistic models based on neural networks that allow several inferences to be computed accurately and efficiently. Throughout the project, the student will become familiar with the main algorithms and techniques for parametric learning of these models from data, as well as with the main applications of probabilistic circuits. It is proposed that the student will implement some recently proposed techniques for regularization in parametric gradient learning in the open-source library being developed by the associated research group, thus contributing to current and cutting-edge scientific research. (AU) | |
News published in Agência FAPESP Newsletter about the scholarship: | |
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