Self-Attention Between Datapoints (Paper review) скачать в хорошем качестве

Self-Attention Between Datapoints (Paper review)

Paper link: https://openreview.net/forum?id=wRXzO... Abstract: We challenge a common assumption underlying most supervised deep learning: that a model makes a prediction depending only on its parameters and the features of a single input. To this end, we introduce a general-purpose deep learning architecture that takes as input the entire dataset instead of processing one datapoint at a time. Our approach uses self-attention to reason about relationships between datapoints explicitly, which can be seen as realizing non-parametric models using parametric attention mechanisms. However, unlike conventional non-parametric models, we let the model learn end-to-end from the data how to make use of other datapoints for prediction. Empirically, our models solve cross-datapoint lookup and complex reasoning tasks unsolvable by traditional deep learning models. We show highly competitive results on tabular data, early results on CIFAR-10, and give insight into how the model makes use of the interactions between points. Authors: Jannik Kossen, Neil Band, Clare Lyle, Aidan Gomez, Tom Rainforth, Yarin Gal Connect with me: Twitter:   / andreimargeloiu   LinkedIn:   / andreimargeloiu