Gaussian Processes
527 papers with code • 0 benchmarks • 4 datasets
Gaussian Processes is a powerful framework for several machine learning tasks such as regression, classification and inference. Given a finite set of input output training data that is generated out of a fixed (but possibly unknown) function, the framework models the unknown function as a stochastic process such that the training outputs are a finite number of jointly Gaussian random variables, whose properties can then be used to infer the statistics (the mean and variance) of the function at test values of input.
Benchmarks
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Libraries
Use these libraries to find Gaussian Processes models and implementations
MPI FAUST Dataset
WHOI-Plankton
Poser
Most implemented papers
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
In comparison, Bayesian models offer a mathematically grounded framework to reason about model uncertainty, but usually come with a prohibitive computational cost.
Conditional Neural Processes
deepmind/neural-processes
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ICML 2018
Deep neural networks excel at function approximation, yet they are typically trained from scratch for each new function.
Gaussian Processes for Big Data
We introduce stochastic variational inference for Gaussian process models.
Doubly Stochastic Variational Inference for Deep Gaussian Processes
ICL-SML/Doubly-Stochastic-DGP
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NeurIPS 2017
Existing approaches to inference in DGP models assume approximate posteriors that force independence between the layers, and do not work well in practice.
Deep Neural Networks as Gaussian Processes
brain-research/nngp
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ICLR 2018
As such, previous work has not identified that these kernels can be used as covariance functions for GPs and allow fully Bayesian prediction with a deep neural network.
Neural Tangent Kernel: Convergence and Generalization in Neural Networks
thegregyang/GP4A
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NeurIPS 2018
While the NTK is random at initialization and varies during training, in the infinite-width limit it converges to an explicit limiting kernel and it stays constant during training.
Adversarial Robustness Toolbox v1.0.0
IBM/adversarial-robustness-toolbox
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Defending Machine Learning models involves certifying and verifying model robustness and model hardening with approaches such as pre-processing inputs, augmenting training data with adversarial samples, and leveraging runtime detection methods to flag any inputs that might have been modified by an adversary.
Efficiently Sampling Functions from Gaussian Process Posteriors
j-wilson/GPflowSampling
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ICML 2020
Gaussian processes are the gold standard for many real-world modeling problems, especially in cases where a model's success hinges upon its ability to faithfully represent predictive uncertainty.
Scalable Bayesian Optimization Using Deep Neural Networks
automl/pybnn
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Bayesian optimization is an effective methodology for the global optimization of functions with expensive evaluations.
Deep Kernel Learning
We introduce scalable deep kernels, which combine the structural properties of deep learning architectures with the non-parametric flexibility of kernel methods.
