  In machine learning, no data point stands alone. We believe that context is
an underappreciated concept in many machine learning methods. We propose
Attention-Based Clustering (ABC), a neural architecture based on the attention
mechanism, which is designed to learn latent representations that adapt to
context within an input set, and which is inherently agnostic to input sizes
and number of clusters. By learning a similarity kernel, our method directly
combines with any out-of-the-box kernel-based clustering approach. We present
competitive results for clustering Omniglot characters and include analytical
evidence of the effectiveness of an attention-based approach for clustering.
