Derives explicit bounds decomposing KL(GP || LNP) into three costs with decay rates O(e^{-c d^{2/d_x}}) for squared-exponential kernels and O(d^{-2ν/d_x}) for Matérn kernels, plus recommendations to predict variance from locations alone and use second-order pooling.
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Transformer components arise as the natural solution to precision-weighted directional state estimation on the hypersphere.
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Three Costs of Amortizing Gaussian Process Inference with Neural Processes
Derives explicit bounds decomposing KL(GP || LNP) into three costs with decay rates O(e^{-c d^{2/d_x}}) for squared-exponential kernels and O(d^{-2ν/d_x}) for Matérn kernels, plus recommendations to predict variance from locations alone and use second-order pooling.
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RT-Transformer: The Transformer Block as a Spherical State Estimator
Transformer components arise as the natural solution to precision-weighted directional state estimation on the hypersphere.
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