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The existing deep learning based time series classification (TSC) algorithms have some success in multivariate time series, their accuracy is not high when we apply them on brain EEG time series (65
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biological networks as a form of relational and structural learning. Given a network dataset, we wish to infer a model of the distribution of the elements of this data-set, possibly as a mixture of several
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strong out-of-distribution generalization capability [2]. If user-specific information is identified and removable from the input data, the devised techniques can also be applied for privacy-sensitive
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Anomaly detection is an important task in data mining. Traditionally most of the anomaly detection algorithms have been designed for ‘static’ datasets, in which all the observations are available
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of robust QoS metrics for medical applications known to perform poorly with established QoS metrics. We would also like to explore the geographical distribution of available services in Australia, and where
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Federated learning (FL) is an emerging machine learning paradium to enable distributed clients (e.g., mobile devices) to jointly train a machine learning model without pooling their raw data into a
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-black hole collisions have been observed. This new field of discovery is in its infancy, with many more astrophysical sources waiting to be uncovered such as signals from supernovae explosions and
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to pursue an undergraduate degree at Monash in engineering, you may be eligible for this scholarship. No application is required. You will be automatically assessed when you list Monash as a VTAC preference