As the EMCR Informal Talk series are designed to share peoples’ own lives, keeping the Informal Talk as an in-person only event best aligns with its personal nature and can maximize interactions with the audience.
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.
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.
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: 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: 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: 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: 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.
Speaker: Dr Zhen Fang (University of Technology Sydney)
In this talk, we will present the latest advancements in OOD detection theory, and OOD detection algorithms.
Speaker: Sharon Yixuan Li (University of Wisconsin-Madison)
I will introduce a new algorithmic framework, which jointly optimizes for both accurate classification of ID samples, and reliable detection of OOD data.
Speaker: Prof Phoebe Chen (La Trobe University)
There is a large number of possible applications that can benefit from the analysis of multimedia data.
Speaker: Akari Asai (University of Washington)
We study the problem of retrieval with instructions, where users of a retrieval system explicitly describe their intent along with their queries.
Speaker: Xinyi Gao
This comprehensive experimental study demonstrates that SNS consistently outperforms existing methods in different benchmark datasets.
Speaker: Hechuan Wen
Causal inference is increasingly important in guiding decision-making in high-stake domains, such as healthcare, education, e-commerce, etc.
Speaker: Dr Shoujin Wang (University of Technology Sydney)
In recent years, sequential/session-based recommendations have emerged as a new recommendation paradigm to well model users’ dynamic and short-term preferences for more accurate and timely recommendations.
Speaker: Andrea Parker, Growth ML Engineer (Weights & Biases)
Join us to learn all about experiment tracking at scale with Pachyderm and Weights & Biases.
Speaker: Dr Grace Hui Yang (Georgetown University)
We propose a novel SEgment-based Neural Indexing method, SEINE, which provides a general indexing framework that can flexibly support a variety of interaction-based neural retrieval methods.
Speaker: Prof Stefan Böttcher - Universität Paderborn (Germany)
This talk gives an overview of the key ideas behind grammar-based compression techniques for strings, trees, and graphs.
Speaker: Dr Ziran Wang (Purdue University)
In this talk, a Mobility Digital Twin (MDT) framework is introduced, which is defined as an Artificial Intelligence (AI)-based data-driven cloud-edge-device framework for mobility services.
Speaker: Dr Shixun Huang (RMIT University)
In this talk, recent advances under multiple fields (i.e., data mining, viral marketing and urban computing) of graph analytics will be introduced.
