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Truyen Tran

Applied Artificial Intelligence Institute & School of IT, Deakin University

Title: Deep analytics via learning to reason

Abstract: Deep learning, enabled by powerful compute, and fuelled by massive data, has delivered unprecedented data analytics capabilities. However, major limitations remain. Chiefly among those is that deep neural networks tend to exploit the surface statistics in the data, creating short-cuts from the input to the output, without really deeply understanding of the data. As a result, these networks fail miserably to generalize to novel combinations. This is because the networks perform shallow pattern matching but not deliberate reasoning – the capacity to deliberately deduce new knowledge out of the contextualized data. Second, machine learning is often trained to do just one task at a time, making it impossible to re-define tasks on the fly as needed in a complex operating environment. This talk presents our recent developments to extend the capacity of neural networks to remove these limitations. Our main focus is on learning to reason from data, that is, learning to determine if the data entails a conclusion. This capacity opens up new ways to generate insights from data through arbitrary querying using natural languages without the need of predefining a narrow set of tasks.

Bio: Dr Truyen Tran is Associate Professor, Head of AI, Health and Science at Deakin University where he leads a research team on the next generation of deep learning and applications to computer vision, computational science, biomedicine and software analytics. He publishes regularly at top AI/ML/KDD venues such as NeurIPS, ICML, ICLR, CVPR, UAI, AAAI, IJCAI and KDD. Tran has received multiple recognitions, awards and prizes including Best Paper Runner Up at UAI (2009), Geelong Tech Award (2013), CRESP Best Paper of the Year (2014), Third Prize on Kaggle Galaxy-Zoo Challenge (2014), Title of Kaggle Master (2014), Best Student Papers Runner Up at PAKDD (2015) and ADMA (2016), and Distinguished Paper at ACM SIGSOFT (2015). He obtained a Bachelor of Science from University of Melbourne and a PhD in Computer Science from Curtin University in 2001 and 2008, respectively.

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Manuel Clavel

Eastern International University

Title: Model-Driven Security for a Software Developer. The Case of Fine-Grained Access Control Policies