Self-Attention through Kernel-Eigen Pair Sparse Variational Gaussian Processes

While the great capability of Transformers significantly boosts prediction accuracy, it could also yield overconfident predictions and require calibrated uncertainty estimation, which can be commonly tackled by Gaussian processes (GPs). Existing works apply GPs with symmetric kernels under variational inference to the attention kernel; however, omitting the fact that attention kernels are in essence asymmetric. Moreover, the complexity of deriving the GP posteriors remains high for large-scale data. In this work, we propose Kernel-Eigen Pair Sparse Variational Gaussian Processes (KEP-SVGP) …

accuracy apply attention capability cs.ai cs.cv cs.lg gaussian processes gps inference kernel prediction predictions processes self-attention stat.ml through transformers uncertainty