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compressed into lightweight student models using knowledge distillation, enabling efficient real-time inference on mobile devices. The distilled models will be deployed and optimized on mobile platforms, with
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-time, on-device applications. This project focuses on exploring model optimization strategies, including compression, quantization, and efficient architecture design, to reduce the resource footprint
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” of novel stretchable structures by advancing numerical modelling, data-driven, and optimisation approaches in a nonlinear regime. Project Background Stretchable mechanical structures are a subset of
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of our iconic innovations were once considered impossible until someone, just like you, joined us and took on the challenge. Visit CSIRO.au and www.cdscc.nasa.gov for more information. The opportunity
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that both parameter estimation and model selection can be interpreted as problems of data compression. The principle is simple: if we can compress data, we have learned something about its underlying
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to cloud-based machine learning services, on-device ML is privacy-friendly, of low latency, and can work offline. User data will remain at the mobile device for ML inference. Problems: In order to enable
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knowledge of building services Basic computer skills. Be available to take part in an after-hours on call roster. Strong understanding of WHS and environmental legislation, codes and standards. Problem