  Neural Processes (NPs) are popular methods in meta-learning that can estimate
predictive uncertainty on target datapoints by conditioning on a context
dataset. Previous state-of-the-art method Transformer Neural Processes (TNPs)
achieve strong performance but require quadratic computation with respect to
the number of context datapoints, significantly limiting its scalability.
Conversely, existing sub-quadratic NP variants perform significantly worse than
that of TNPs. Tackling this issue, we propose Latent Bottlenecked Attentive
Neural Processes (LBANPs), a new computationally efficient sub-quadratic NP
variant, that has a querying computational complexity independent of the number
of context datapoints. The model encodes the context dataset into a constant
number of latent vectors on which self-attention is performed. When making
predictions, the model retrieves higher-order information from the context
dataset via multiple cross-attention mechanisms on the latent vectors. We
empirically show that LBANPs achieve results competitive with the
state-of-the-art on meta-regression, image completion, and contextual
multi-armed bandits. We demonstrate that LBANPs can trade-off the computational
cost and performance according to the number of latent vectors. Finally, we
show LBANPs can scale beyond existing attention-based NP variants to larger
dataset settings.
