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2022-4: Homogenising data density distribution for better pattern recognition

Date: Thursday 21 April Time: 2.00pm – 3.00pm Speaker: Dr Ye Zhu Abstract: Distance-based and density-based algorithms have been widely applied in various industries for clustering and anomaly detection. However, these algorithms usually suffer from the long-standing issue of inhomogeneous cluster densities, since they implicitly assume that all clusters have approximately the same density. Many remedies […]

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2022-3: Time series in healthcare: challenges and open problems

Date: Wednesday 23 March Time: 1.00pm – 2.00pm Speaker: Professor Maia Angelova  Title: Time series in healthcare: challenges and open problems Abstract: Time series datasets, such as electronic health records (EHR), electrocardiograms (ECG), electroencephalograms (EEG), sleep records, monitoring vital signs, COVID-19 spread, are sources of information that can capture the onset and spread of disease, lifestyle risks, the results and […]

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2021-12: Diversity Enhanced Active Learning with Strictly Proper Scoring Rules

Date: Time: 12 Dec, 2021, Friday 10-11 am AEST Title: Diversity Enhanced Active Learning with Strictly Proper Scoring Rules Abstract: We study acquisition functions for active learning (AL) for text classification. The Expected Loss Reduction (ELR) method focuses on a Bayesian estimate of the reduction in classification error, recently updated with Mean Objective Cost of […]

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2021-10: Local Reliable Community Search in Weighted Graphs

Date: Time: 8 Oct, 2021, Friday 1pm-2pm AEST Title: Local Reliable Community Search in Weighted Graphs Abstract: Community search is a fundamental task in the analysis of complex networks, which is widely applied in various scenarios like the social network, collaboration network, and topology networks. In the real world, the network data is dynamic and the interaction between entities […]

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2021-09: Internet of Medical Things (IoMT): A Future Connected Healthcare System

Date: 15/09/2021, 12-1 pm  Title: Internet of Medical Things (IoMT): A Future Connected Healthcare System Abstract: IoMT stands for the Internet of Medical Things and it’s a combination of wearable, healthcare and medical devices along with applications that can connect all the healthcare information systems through networking technologies. It’s a very big market that was […]

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2021-09: AI and Data-Driven Modelling for Precision Medicine and Healthcare

Date: 23/09/2021, 10-11 am  Title: AI and Data-Driven Modelling for Precision Medicine and Healthcare Abstract: Sleep and nutrition are essential and repeating processes which are vital for our quality of life and wellbeing. These processes involve complex dynamics and regulation at multi-scale that reflect developmental changes in mental and physical health, along with the day-to-day […]

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2021-09: Representation Learning for Short Text Clustering

Date: 03/09/2021, 2-3 pm  Title: Quantum Computing and Quantum Machine Learning: Concepts, Applications and Major Players Abstract: Effective representation learning is critical for short text clustering due to the sparse, high-dimensional and noise attributes of short text corpus. Existing pretrained  models (e.g., Word2vec and BERT) have greatly improved the  expressiveness for short text representations with more […]

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2021-09: Quantum Computing and Quantum Machine Learning: Concepts, Applications and Major Players

Date: 01/09/2021, 12-1 pm  Title: Quantum Computing and Quantum Machine Learning: Concepts, Applications and Major Players Abstract: Quantum computing is a new and rapidly evolving area of research and development. It concerns building and using information processing systems, which are capable of harnessing phenomena at atomic and sub-atomic scale. Such systems work on entirely new […]

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2021-08: Reflections on Interdisciplinary Research Practice

Date: 27/08/2021, 11-12 am Title: Heart rate variability (HRV) Analysis using Entropy Methods Abstract: Discussing opportunities and challenges when conducting interdisciplinary research. Speaker: Dr Birgit Muskat, Australian National University. Dr Muskat is the Deputy Director, Research School of Management, Higher Degree Research at the College of Business and Economics at ANU. Her research interests include […]

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2021-08: Deep Graph Contrastive Learning

Date: 06/08/2021, 2-3 pm  Title: Deep Graph Contrastive Learning  Abstract: Self-supervised learning (SSL) has been extensively studied to alleviate the label sparsity problem in deep models. Recent self-supervised learning techniques are converging around the central theme of Contrastive Learning (CL), which aims to maximize the consistency of representations under multiple views of the input data. […]

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