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Deep Unsupervised Learning for Climate Informatics with Claire Monteleoni - #497
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
The Supervised Training of the v a E
In both cases, there's going to be that threshold hyper parameter for whether you're saying your reconstruction error is above a certain value. It turns out that the one with entirely unsupervised training of the v a e performed better. And so we view that as really a form of regularization, which helps your ultimate model to be more generalized. i think it can apply really in any setting where you have a major class and balance and you don't want to use a lot of labele data.
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