Here is my attempt to map out the major classes of algorithms in Machine Learning, organised around the associated induction principles and learning theory. The usual caveats apply around this being biased towards my own experience. At the highest level, we can distinguish between the Passive and Active learning settings. In the passive case, the … More A Map of Machine Learning Principles and Algorithms
In this post on the last day of the year, I thought I will share a map of mathematical structures that are useful for thinking about knowledge representation and reasoning (KRR) issues in Artificial Intelligence and Machine Learning. It is built on top of the diagram shown in Max Tegmark’s paper Is “the theory of … More A Map of Mathematical Structures for AI
Artificial Intelligence as a well-defined mathematical problem was solved a number of years ago through the formulation of the AIXI agent by Prof Marcus Hutter — see https://theconversation.com/to-create-a-super-intelligent-machine-start-with-an-equation-20756 for a quick introduction — but a key fundamental issue with the AIXI theory has always been the incomputability of the general solution. In a continuation of … More A Direct Approximation of AIXI using Logical State Abstractions
A few colleagues and I have just completed a new research paper titled Factored Conditional Filtering: Tracking States and Estimating Parameters in High-Dimensional Spaces. The research took over 3 years and I am really excited about the underlying theory and its possible applications. In particular, the paper shows how we can lift’ Bayesian filtering to … More Bayesian Filtering on Structured Environments
I read the book Divining a Digital Future: Mess and Mythology in Ubiquitous Computing by Genevieve Bell and Paul Dourish almost cover to cover last year and I have to say that finally gave me some understanding of the research agenda being pursued at the 3A Institute at the ANU. I hope I am not … More Book Review: Divining a Digital Future
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
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
Mental Models 4 Life 