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Recently, it has been shown that many functions on sets can be represented bysum decompositions. These decompositons easily lend themselves to neuralapproximations, extending the applicability of neural nets to set-valuedinputs---Deep Set learning. This work investigates a core component of Deep Setarchitecture: aggregation functions. We suggest and examine alternatives tocommonly used aggregation functions, including learnable recurrent aggregationfunctions. Empirically, we show that the Deep Set networks are highly sensitiveto the choice of aggregation functions: beyond improved performance, we findthat learnable aggregations lower hyper-parameter sensitivity and generalizebetter to out-of-distribution input size.

On Deep Set Learning and the Choice of Aggregations