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Found 13 papers, 9 papers with code
Aardvark Weather: end-to-end data-driven weather forecasting
Machine learning is revolutionising medium-range weather prediction.
Autoregressive Conditional Neural Processes
Our work provides an example of how ideas from neural distribution estimation can benefit neural processes, and motivates research into the AR deployment of other neural process models.
Environmental Sensor Placement with Convolutional Gaussian Neural Processes
This paper proposes using a convolutional Gaussian neural process (ConvGNP) to address these issues.
Sparse Gaussian Process Hyperparameters: Optimize or Integrate?
In this work we propose an algorithm for sparse Gaussian process regression which leverages MCMC to sample from the hyperparameter posterior within the variational inducing point framework of Titsias (2009).
A Note on the Chernoff Bound for Random Variables in the Unit Interval
The Chernoff bound is a well-known tool for obtaining a high probability bound on the expectation of a Bernoulli random variable in terms of its sample average.
Practical Conditional Neural Processes Via Tractable Dependent Predictions
Existing approaches which model output dependencies, such as Neural Processes (NPs; Garnelo et al., 2018b) or the FullConvGNP (Bruinsma et al., 2021), are either complicated to train or prohibitively expensive.
Modelling Non-Smooth Signals with Complex Spectral Structure
The Gaussian Process Convolution Model (GPCM; Tobar et al., 2015a) is a model for signals with complex spectral structure.
Wide Mean-Field Bayesian Neural Networks Ignore the Data
Finally, we show that the optimal approximate posterior need not tend to the prior if the activation function is not odd, showing that our statements cannot be generalized arbitrarily.
How Tight Can PAC-Bayes be in the Small Data Regime?
Interestingly, this lower bound recovers the Chernoff test set bound if the posterior is equal to the prior.
The Gaussian Neural Process
Neural Processes (NPs; Garnelo et al., 2018a, b) are a rich class of models for meta-learning that map data sets directly to predictive stochastic processes.
Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes
Stationary stochastic processes (SPs) are a key component of many probabilistic models, such as those for off-the-grid spatio-temporal data.
Scalable Exact Inference in Multi-Output Gaussian Processes
Multi-output Gaussian processes (MOGPs) leverage the flexibility and interpretability of GPs while capturing structure across outputs, which is desirable, for example, in spatio-temporal modelling.
Convolutional Conditional Neural Processes
We introduce the Convolutional Conditional Neural Process (ConvCNP), a new member of the Neural Process family that models translation equivariance in the data.
