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 1451–1475 of 1963 papers

TitleStatusHype
Functional Variational Bayesian Neural NetworksCode0
Gaussian Process Optimization with Adaptive Sketching: Scalable and No RegretCode1
Learning Gaussian Policies from Corrective Human Feedback—0
Financial Applications of Gaussian Processes and Bayesian Optimization—0
Scalable Grouped Gaussian Processes via Direct Cholesky Functional Representations—0
Stay Ahead of Poachers: Illegal Wildlife Poaching Prediction and Patrol Planning Under Uncertainty with Field Test EvaluationsCode0
Active learning for enumerating local minima based on Gaussian process derivatives—0
Deep Random Splines for Point Process Intensity Estimation of Neural Population DataCode0
Probabilistic Modeling for Novelty Detection with Applications to Fraud Identification—0
V2X System Architecture Utilizing Hybrid Gaussian Process-based Model Structures—0
Monotonic Gaussian Process for Spatio-Temporal Disease Progression Modeling in Brain Imaging Data—0
Local Function Complexity for Active Learning via Mixture of Gaussian Processes—0
Deeper Connections between Neural Networks and Gaussian Processes Speed-up Active LearningCode0
Banded Matrix Operators for Gaussian Markov Models in the Automatic Differentiation Era—0
Unsupervised Visual Domain Adaptation: A Deep Max-Margin Gaussian Process ApproachCode0
Bayesian Anomaly Detection and Classification—0
AReS and MaRS - Adversarial and MMD-Minimizing Regression for SDEsCode0
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
ProBO: Versatile Bayesian Optimization Using Any Probabilistic Programming LanguageCode0
Minimizing Negative Transfer of Knowledge in Multivariate Gaussian Processes: A Scalable and Regularized Approach—0
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Benchmark Results

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