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Learning Invariant Representations of Social Media Users

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Figure

Figure 1: The map fθ takes an episode as input and outputs a vector. Here A denotes a multi-head self-attentionlayer, C a stack of 1D convolutions, E an embedding lookup, M an MLP, and P a pooling layer.
Figure 2: Projections of embeddings of user episodes. Each point is the result of mapping an episode to a singlepoint in R512 and projected to R2 using t-SNE
Table 1: Reddit author ranking results with 111,396 possible targets. The best results for each feature group are inprinted innoted in parenthesis
Figure 3: We report Recall@8 for different episodelengths using all features.
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