Events

Join us at the 2023 Summer of AI (SAI) from 11-15 December, where you'll hear from UQ's two new Professors of AI, as well as some of our leading academics in the field.

The talk will give some background on diffusion as a Generative Model for images, and text-based imaging.

This thesis addresses this gap by proposing a triadic relationship among learning theories, educational technologies, and learning analytics.

One open research problem in this field is how do we fully automate task creation?

State-of-the-art methods for relational databases date back until the late 1970s, and compute a lossless, dependency-preserving decomposition into Third Normal Form (3NF), which will be in Boyce-Codd Normal Form (BCNF) whenever possible. The talk will showcase two very recent new developments.

In this session, I will provide an overview of database integration, including the motivation, problem definition, important tasks and real-world examples.

In this talk, we will introduce the fundamental notions about scales of measurement and meaningfulness, and we will show how they apply to IR evaluation measures.

This talk will give a high level overview of Large Language Models and present my thoughts on the potential benefits and likely risks of AI and what we should be doing about them.

In this talk I will first briefly introduce my previous work in Immersive Analytics, which includes the visualisation of abstract and 3D data in multiple XR scenarios.

Speaker: Dr Paul Thomas, Microsoft
We report on experiments using GPT-4, a recent large language model, to label documents for relevance.

This work aims to minimise the costs associated with the process of creating systematic reviews.

Our study delves into a novel decompositional approach to enhance and evaluate text-to-image alignment.

Speaker: Toni Peggrem
Are you interested in the future of artificial intelligence (AI) in Queensland? Join us for an exclusive one-hour talk into the Queensland AI Hub.

Speaker: Dr Maxime Cordeil (University of Queensland)
In this talk I will first briefly introduce my previous work in Immersive Analytics, which includes the visualisation of abstract and 3D data in multiple XR scenarios.

Speaker: Yi Zhang
We introduce a client-server framework designed to fortify MARL in real-world scenarios, achieving scalability, parallelization, and privacy preservation.

Speaker: Haodong Hong
Our research introduces a novel task, Vision-and-Language Navigation with Multi-modal Prompts (VLN-MP), in which instructions consist of both natural language and images as prompts.

Speaker: Yawen Zhao
By overcoming three key challenges, we aim to work towards PU boosting methods which have superior performance alongside computational efficiency and minimal dependence on hyperparameter tuning.

Speaker: Ekaterina Khramtsova
In this research, we explore various methods for assessing the generalisability of Deep Neural Networks on OOD samples within different scenarios, namely for model selection and for performance estimation.

Speaker: Yiyun Zhang
In this study, we propose novel unsupervised approaches for BTRS analysis, specifically focusing on change and damage detection in buildings, without the need to use any annotations.

Speaker: Yanping Zheng (DATA61-CSIRO)
My research focuses on improving the scalability and expressiveness of dynamic graph neural networks.

Speaker: Sara Hajari
In this research, a detailed exploration of problem instance generation is carried out, and possible ways this approach can be used in benchmarking practice are discussed.

Speaker: Chenhao Zhang
My research aims to improve recommendation systems in low-data scenarios, benefiting small and medium-sized companies and responding to public emergencies.

Speaker: Cheng Soon Ong, Data61, CSIRO
The AI hype claims that everything can be solved by AI. The reality is that while there are many exciting advances in recent years, many open problems remain.

Speaker: Shaochen Yu
As the role of data amplifies in decision-making, our methodologies promise efficient and streamlined solutions in format identification.

Speaker: Jiechen Xu
The process of data annotation, also referred to as data labeling, is a crucial step in various research fields like machine learning and behavioral studies.