2022
DOI: 10.48550/arxiv.2207.00753
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An End-to-End Set Transformer for User-Level Classification of Depression and Gambling Disorder

Abstract: This work proposes a transformer architecture for user-level classification of gambling addiction and depression that is trainable end-to-end. As opposed to other methods that operate at the post level, we process a set of social media posts from a particular individual, to make use of the interactions between posts and eliminate label noise at the post level. We exploit the fact that, by not injecting positional encodings, multi-head attention is permutation invariant and we process randomly sampled sets of t… Show more

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