Machine Learning seminar
When: Wed, 07 August 2019, 3:00pm
Where: AG22, College Building
Who: Alessandro Daniele; Fondazione Bruno Kessler (Trento, Italy)
Title: Knowledge Enhanced Neural Networks
Abstract: We propose Knowledge Enhanced Neural Networks (KENN), an architecture for injecting prior knowledge, codified by a set of logical clauses, into a neural network. In KENN clauses are directly incorporated in the structure of the neural network as a new layer that includes a set of additional learnable parameters, called clause weights. As a consequence, KENN can learn the level of satisfiability to impose in the final classification. When training data contradicts a constraint, KENN learns to ignore it, making the system robust to the presence of wrong knowledge. Moreover, the method returns learned clause weights, which gives us informations about the influence of each constraint in the final predictions, increasing the interpretability of the model. We evaluated KENN on two standard datasets for multilabel classification, showing that the injection of clauses automatically extracted from the training data sensibly improves the performances. Furthermore, we apply KENN to solve the problem of finding relationship between detected objects in images by adopting manually curated clauses. The evaluation shows that KENN outperforms the state of the art methods on this task.
Bio: Alessandro Daniele received his master degree in Computer Science from Università degli Studi di Padova in 2014. At the end of 2014 he started working for a private company focusing on the development of a Business Intelligence software. In 2015 he worked as a research fellow at CRIBI Biotechnology Center at Università degli Studi di Padova, continuing his master thesis work on Multiple Sequence Alignment, a well known problem in Bioinformatics.
In 2016 he started his PhD at Università degli Studi di Firenze and at Data Knowledge and Management (DKM) group at Fondazione Bruno Kessler (Trento, Italy). His main research interest is in Machine Learning and its application, with a particular focus on Neural Symbolic Integration.