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Australian Information Industry Association (AIIA) Awards Finalists Visit AIML
- Date: Thu, 29 Aug 2024, 10:45 am - 11:45 am
- Location: AIML
AIML welcomed the finalists of the (AIIA)听Awards with a tour of our institute. These finalists represent a diverse range of innovators from across Australia, including individuals, groups, government, and private organisations. Our Business and Development Manager,听Matthew Cliff, led the tour showcasing AIML鈥檚 research innovations and addressed many enthusiastic questions on specific use cases of AI adoption across various industries.
[Read more about Australian Information Industry Association (AIIA) Awards Finalists Visit AIML]
AIML Special Presentation: Translating Running Gait Assessments to In-field Application Using Wearable Sensors
- Date: Fri, 23 Aug 2024, 10:30 am - 11:15 am
- Location: AIML
Dr Joel Fuller is a Sports Scientist and Physiotherapy researcher from Macquarie University. He has expertise in research areas related to biomechanics, wearable technology, performance enhancement and injury prevention. Joel presented findings from his most recent wearable technology research which used IMU systems to (1) identify fatigue onset in athletes during an intensive running training intervention and (2) identify and retrain runners at high risk of impact-related injuries. He described how his work is seeking to engage with machine learning and deep learning to enhance performance and injury prevention outcomes for athletes and explore potential avenues for research translation.
AIML Research Seminar: Embracing Changes in Deep Learning: Continual Learning with Augmented and Modularised Memory (with Pre-trained Models)
- Date: Tue, 20 Aug 2024
- Location: AIML
Conventional DL approaches focus on the end results on fixed datasets/scenarios and fail to handle the dynamically raised novel requirements in the real world. Continual learning (CL) aims to train deep neural networks (DNNs) to efficiently accumulate knowledge on dynamically arriving data and task streams like humans. The main challenges include how to enable DNNs to learn on data and task streams with non-stationary distributions without catastrophic forgetting. To ensure DNNs effectively retain past knowledge while accommodating future tasks with a balance between stability and plasticity, we explore CL techniques from the viewpoint of augmenting听补苍诲 modularising听迟丑别 memorisation听of DNNs. Given pre-trained models, do we need continual learning or how should we use continual learning? This talk delved into investigating how to continually perform adaptive learning with the pre-trained models and how to continually enhance pre-trained models.听
Department for Education visit
- Date: Thu, 8 Aug 2024
- Location: AIML
AIML hosted over 100 secondary school students during the Education and School Tours at Lot Fourteen event hosted by the South Australian Department of Education.听
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