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The relative performance gain for Fig.1 c) is In Tab. 6, we show FPS(F) FPS(E) of various feature fusion models with the varied set sizeN. Notethatmethodswithout intra-set relationships, PFE [11] and CFAN [3], are computationally very fast and require little memory. Incontrast, the maximum set sizeN for RSA [7] is384 because the intra-set attention with the feature map is a memory-intensivemodule. In other words, it is the mean of the row-wise entropy of the normalized assignment map. Lower entropy value tells you that the cluster features are deviating from a simple average of all samples.

Weusebrainimagingrecordings ofsubjectsreading complex natural text to interpret word and sequence embeddings from4 recent NLP models - ELMo, USE, BERT and Transformer-XL. We study how their representations differ across layer depth, contextlength, and attention type.

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Ifthext, krf(xt)k , perturbxt by , with sampled zero.