Recent advances in deep learning have been driven by large-scale parametric models, which can be computationally expensive and lack interpretability. Semi-parametric methods query the training set at inference time and can be more compact, although they typically have quadratic computational complexity. Here, we introduce SPIN, a general-purpose semi-parametric neural architecture whose computational cost is linear in the size and dimensionality of the data. Our architecture is inspired by inducing point methods and relies on a novel application of cross-attention between datapoints. At inference time, its computational cost is constant in the training set size as the data gets distilled into a fixed number of inducing points. We find that our method reduces the computational requirements of existing semi-parametric models by up to an order of magnitude across a range of datasets and improves state-of-the-art performance on an important practical problem, genotype imputation.

介绍了一种半参数感知网络(SPIN)的模型，该模型可以在推理时计算，使用了感应点方法中数据点的交互机制，可以支持元学习中的大区域数据，并取得了 state-of-the-art 的性能表现。

半参数感知点网络和神经过程