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LATEST PROJECTS
Cost-Efficient Neurological Disorder detection ​
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We proposed a neurological disorder detection scheme by considering the feature extraction cost. We encourage the classifiers to use cheap features instead of expensive ones. By doing that, we seek to reduce power consumption on a chip.
Model Compression on Oblique Trees
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We proposed a model compression technique to generate sparse connected oblique trees. The model is compatible with cost-efficient inference.
Migraine Classification using Somatosensory Evoked Potentials
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We proposed a system based on somatosensory evoked potentials (SSEP) for migraine state classification. Using gradient boosted decision trees, we achieved an accuracy of 89.7% by using a single-channel noninvasive SSEP.
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