# Neural Processes

@article{Garnelo2018NeuralP, title={Neural Processes}, author={Marta Garnelo and Jonathan Schwarz and Dan Rosenbaum and Fabio Viola and Danilo Jimenez Rezende and S. M. Ali Eslami and Yee Whye Teh}, journal={ArXiv}, year={2018}, volume={abs/1807.01622} }

A neural network (NN) is a parameterised function that can be tuned via gradient descent to approximate a labelled collection of data with high precision. A Gaussian process (GP), on the other hand, is a probabilistic model that defines a distribution over possible functions, and is updated in light of data via the rules of probabilistic inference. GPs are probabilistic, data-efficient and flexible, however they are also computationally intensive and thus limited in their applicability. We…

## 141 Citations

### Bootstrapping Neural Processes

- Computer ScienceNeurIPS
- 2020

The bootstrap is a classical data-driven technique for estimating uncertainty, which allows BNP to learn the stochasticity in NPs without assuming a particular form, and the efficacy of BNP on various types of data and its robustness in the presence of model-data mismatch are demonstrated.

### Residual Neural Processes

- Computer ScienceAAAI
- 2020

This paper proposes a simple yet effective remedy; the Residual Neural Process (RNP) that leverages traditional BLL for faster training and better prediction, and demonstrates that the RNP shows faster convergence and better performance, both qualitatively and quantitatively.

### Meta-Learning Priors for Efficient Online Bayesian Regression

- Computer ScienceWAFR
- 2018

The proposed ALPaCA is found to be a promising plug-in tool for many regression tasks in robotics where scalability and data-efficiency are important, and outperforms kernel-based GP regression, as well as state of the art meta-learning approaches.

### Learning to Estimate Point-Prediction Uncertainty and Correct Output in Neural Networks

- Computer Science
- 2019

A new framework called RIO is developed that makes it possible to estimate uncertainty in any pretrained standard NN without modifications to model architecture or training pipeline, and provides an important ingredient in building real-world applications of NNs.

### Global Convolutional Neural Processes

- Computer Science2021 IEEE International Conference on Data Mining (ICDM)
- 2021

A member GloBal Convolutional Neural Process (GBCoNP) is built that achieves the SOTA log-likelihood in latent NPFs and manipulation of the global uncertainty enables the probability evaluation on the functional priors.

### Transforming Gaussian Processes With Normalizing Flows

- Computer ScienceAISTATS
- 2021

A variational approximation to the resulting Bayesian inference problem is derived, which is as fast as stochastic variational GP regression and makes the model a computationally efficient alternative to other hierarchical extensions of GP priors.

### Quantifying Point-Prediction Uncertainty in Neural Networks via Residual Estimation with an I/O Kernel

- Computer ScienceICLR
- 2020

A new framework (RIO) is developed that makes it possible to estimate uncertainty in any pretrained standard NN without modifications to model architecture or training pipeline, and provides an important ingredient for building real-world NN applications.

### Wasserstein Neural Processes

- Computer ScienceArXiv
- 2019

It is shown that there are desirable classes of problems where NPs, with this loss of maximum likelihood, fail to learn any reasonable distribution, and this drawback is solved by using approximations of Wasserstein distance.

### Neural Clustering Processes

- Computer ScienceICML
- 2020

This work introduces deep network architectures trained with labeled samples from any generative model of clustered datasets, and develops two complementary approaches to this task, requiring either O(N) or O(K) network forward passes per dataset.

### VFunc: a Deep Generative Model for Functions

- Computer ScienceArXiv
- 2018

A deep generative model for functions that provides a joint distribution p(f, z) over functions f and latent variables z which lets us efficiently sample from the marginal p( f) and maximize a variational lower bound on the entropy H(f).

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Conditional Neural Processes are inspired by the flexibility of stochastic processes such as GPs, but are structured as neural networks and trained via gradient descent, yet scale to complex functions and large datasets.

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Manifold Gaussian Processes is a novel supervised method that jointly learns a transformation of the data into a feature space and a GP regression from the feature space to observed space, which allows to learn data representations, which are useful for the overall regression task.

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