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NP-PROV: Neural Processes with Position-Relevant-Only Variances
[article]
2020
arXiv
pre-print
Neural Processes (NPs) families encode distributions over functions to a latent representation, given context data, and decode posterior mean and variance at unknown locations. Since mean and variance are derived from the same latent space, they may fail on out-of-domain tasks where fluctuations in function values amplify the model uncertainty. We present a new member named Neural Processes with Position-Relevant-Only Variances (NP-PROV). NP-PROV hypothesizes that a target point close to a
arXiv:2007.00767v1
fatcat:lc3m3bxqivgejkmhyphb22afzq