Events

Speaker: Prof Chen Gao (Tsinghua University, China)

This one-day face-to-face course will bring industry professionals together for dialogue and sharing of knowledge to better understand oils for transformer and tap changer applications and testing thereof. This course will deliver the theoretical background information necessary along with practical experience from industry experts to understand the issues of oils and their application in a modern power system. This course is aimed at procurement, asset strategies, operations and maintenance managers and engineers in generation, transmission and distribution, renewables manufacturing mining industrial and infrastructure organisations.

Speaker: Achmad Ginanjar

Continual graph learning (CGL) is purposed to continuously update a graph model with graph data being fed in a streaming manner.

The research findings provide novel insights and understanding of the sensemaking processes in various settings and contribute to modelling practice and the design of supporting tools.

This research introduces a corresponding attack method to uncover the actual vulnerabilities within recommender systems.

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.

This talk will provide an inside view of what AI/ML/DL infrastructure UQ RCC has available right now and an early view of what is coming next.

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

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: 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: 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.