SOTAVerified

Gaussian Processes

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.

Source: Sequential Randomized Matrix Factorization for Gaussian Processes: Efficient Predictions and Hyper-parameter Optimization

Papers

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Showing 1476–1500 of 1963 papers

TitleStatusHype
Deeper Connections between Neural Networks and Gaussian Processes Speed-up Active LearningCode0
Local Function Complexity for Active Learning via Mixture of Gaussian Processes—0
Banded Matrix Operators for Gaussian Markov Models in the Automatic Differentiation Era—0
Unsupervised Visual Domain Adaptation: A Deep Max-Margin Gaussian Process ApproachCode0
AReS and MaRS - Adversarial and MMD-Minimizing Regression for SDEsCode0
Bayesian Anomaly Detection and Classification—0
Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent—0
ODIN: ODE-Informed Regression for Parameter and State Inference in Time-Continuous Dynamical SystemsCode0
Bayesian Image Classification with Deep Convolutional Gaussian Processes—0
Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation—0
Low-pass filtering as Bayesian inference—0
The role of a layer in deep neural networks: a Gaussian Process perspective—0
Functional Regularisation for Continual Learning with Gaussian ProcessesCode0
ProBO: Versatile Bayesian Optimization Using Any Probabilistic Programming LanguageCode0
Minimizing Negative Transfer of Knowledge in Multivariate Gaussian Processes: A Scalable and Regularized Approach—0
Ensembling methods for countrywide short term forecasting of gas demand—0
Towards Practical Lipschitz Bandits—0
On the Limitations of Representing Functions on Sets—0
Meta-Learning Mean Functions for Gaussian Processes—0
Active Learning with Gaussian Processes for High Throughput PhenotypingCode0
Approximating Gaussian Process Emulators with Linear Inequality Constraints and Noisy Observations via MC and MCMC—0
Gaussian processes with linear operator inequality constraintsCode0
Adaptive Activity Monitoring with Uncertainty Quantification in Switching Gaussian Process Models—0
Performance prediction of data streams on high-performance architecture—0
Learning Nonlinear State Space Models with Hamiltonian Sequential Monte Carlo Sampler—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ICKy, periodicRoot mean square error (RMSE)0.03—Unverified