AI News, NIPS Proceedingsβ
- On Wednesday, June 6, 2018
- By Read More
Part of: Advances in Neural Information Processing Systems 27 (NIPS 2014) Gaussian processes (GPs) are a powerful tool for probabilistic inference over functions.
They have been applied to both regression and non-linear dimensionality reduction, and offer desirable properties such as uncertainty estimates, robustness to over-fitting, and principled ways for tuning hyper-parameters.
We show that GP performance improves with increasing amounts of data in regression (on flight data with 2 million records) and latent variable modelling (on MNIST).
- On Saturday, October 19, 2019
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