One of my PhD students has just released a paper titled Spatially Invariant Unsupervised 3D Object Segmentation Using Graph Neural Networks. Here’s the abstract: In this paper, we tackle the problem of unsupervised 3D object segmentation from a point cloud without RGB information. In particular, we propose a framework, SPAIR3D, to model a point cloud … More Unsupervised 3D Object Segmentation
I am sometimes asked what is the difference between Machine Learning (ML) and X, where X is one of a number of things like Statistics, Evolutionary Computing, Control Theory, etc. A variation of the question is what are problem classes that can be tackled by both ML and non-ML techniques, and what are the pros … More Machine Learning: A Broad Church
Since starting my part-time appointment as an associate professor at the Australian National University, I have been thinking about spending more time on fundamental research. As Don Knuth counsels, “if you find that you’re spending almost all your time on theory, start turning some attention to practical things; it will improve your theories. If you … More Large-Scale Distributed Analytics: A Research Program
Like many AI researchers, I watched wide-eyed when IBM’s Watson beat human world champions at the difficult game of Jeopardy in 2011. It was a watershed event in AI history and it certainly provided a much-needed boost to knowledge-representation-based approaches to AI, against a backdrop of 20 years of superior advances in statistical approaches to AI. … More IBM Watson: Fake It till You Make It?
Unifying logic and probability is an active and ongoing research topic of great interest to many. There are many proposals of probabilistic logics in the literature, each with a different motivation, either computational or philosophical, and a different system of syntax and semantics. This state of affairs is confusing and not satisfactory, especially in view … More Unifying Logic and Probability for Learning