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Research Seminar – Kyle Martin – The Wonderful World of Deep Metric Learning

Research Seminar – Kyle Martin – The Wonderful World of Deep Metric Learning

October 2 @ 12:00 pm - 1:00 pm

Deep Metric Learners (DMLs) are a group of neural network architectures which learn to optimise case representations for similarity-based return by training upon multiple cases simultaneously to incorporate relationship knowledge. In this talk, I offer a brief introduction to the wonderful world of deep metric learning, and explore some of the work we have been […]

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Research Discussion

Research Discussion

October 9 @ 1:00 pm - 2:00 pm

Internal school discussion about upcoming research opportunities .

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Research Seminar – Adamu Ali-Gombe – Learning from Small and Imbalanced Dataset of Images using Generative Adversarial Neural Networks

Research Seminar – Adamu Ali-Gombe – Learning from Small and Imbalanced Dataset of Images using Generative Adversarial Neural Networks

October 16 @ 1:00 pm - 2:00 pm

The performance of deep learning models is unmatched by any other approach in supervised computer vision tasks such as image classification. However, training these models require a lot of labelled data which are not always available. Labelling a massive dataset is largely a manual and very demanding process. Thus, this problem has led to the […]

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Research Seminar – David Noble (Aventus Ai) – AI Driven Asset Integrity For Intelligent Digital Twin Development

Research Seminar – David Noble (Aventus Ai) – AI Driven Asset Integrity For Intelligent Digital Twin Development

October 23 @ 1:00 pm - 2:00 pm

TBC

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Research Seminar – Thanh Nguyen – Multi-label Classification on Data Stream

Research Seminar – Thanh Nguyen – Multi-label Classification on Data Stream

October 30 @ 1:00 pm - 2:00 pm

Many batch learning algorithms have been introduced for offline multi-label classification (MLC) over the years. However, the increasing data volume in many applications such as social networks, sensor networks, and traffic monitoring has posed many challenges to batch MLC learning. For example, it is often expensive to re-train the model with the newly arrived samples, […]

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