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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 451–500 of 1963 papers

TitleStatusHype
Double-descent curves in neural networks: a new perspective using Gaussian processes—0
Deep Gaussian Processes for Few-Shot Segmentation—0
Deep Gaussian Processes for geophysical parameter retrieval—0
Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation—0
Deep Gaussian Processes for Regression using Approximate Expectation Propagation—0
Deep Gaussian Processes with Convolutional Kernels—0
Deep Gaussian Processes with Decoupled Inducing Inputs—0
Bayesian Active Learning for Scanning Probe Microscopy: from Gaussian Processes to Hypothesis Learning—0
Doubly infinite residual neural networks: a diffusion process approach—0
Compactly-supported nonstationary kernels for computing exact Gaussian processes on big data—0
A Receding Horizon Approach for Simultaneous Active Learning and Control using Gaussian Processes—0
Bayesian Additive Adaptive Basis Tensor Product Models for Modeling High Dimensional Surfaces: An application to high-throughput toxicity testing—0
A brief note on understanding neural networks as Gaussian processes—0
Physics Enhanced Data-Driven Models with Variational Gaussian Processes—0
Combining Parametric Land Surface Models with Machine Learning—0
Doubly Sparse Variational Gaussian Processes—0
Deep learning applied to computational mechanics: A comprehensive review, state of the art, and the classics—0
Deep learning generalizes because the parameter-function map is biased towards simple functions—0
Bayesian approach to model-based extrapolation of nuclear observables—0
Deep Manifold Prior—0
Meta-Learning Mean Functions for Gaussian Processes—0
Amortized variance reduction for doubly stochastic objectives—0
Bayesian Complementary Kernelized Learning for Multidimensional Spatiotemporal Data—0
Aggregation Models with Optimal Weights for Distributed Gaussian Processes—0
Deep Neural Networks as Point Estimates for Deep Gaussian Processes—0
Bayesian Control of Large MDPs with Unknown Dynamics in Data-Poor Environments—0
Effect Decomposition of Functional-Output Computer Experiments via Orthogonal Additive Gaussian Processes—0
Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification—0
Quantum neural networks form Gaussian processes—0
Combining human cell line transcriptome analysis and Bayesian inference to build trustworthy machine learning models for prediction of animal toxicity in drug development—0
Combining Gaussian processes and polynomial chaos expansions for stochastic nonlinear model predictive control—0
Deep Random Splines for Point Process Intensity Estimation—0
Architectures and random properties of symplectic quantum circuits—0
Combining additivity and active subspaces for high-dimensional Gaussian process modeling—0
Deep Reinforcement Multi-agent Learning framework for Information Gathering with Local Gaussian Processes for Water Monitoring—0
DeepRV: pre-trained spatial priors for accelerated disease mapping—0
Deep Sigma Point Processes—0
Collective Online Learning of Gaussian Processes in Massive Multi-Agent Systems—0
Bayesian Exploration of Pre-trained Models for Low-shot Image Classification—0
Deep Transformed Gaussian Processes—0
Arbitrarily-Conditioned Multi-Functional Diffusion for Multi-Physics Emulation—0
A Framework for Finding Local Saddle Points in Two-Player Zero-Sum Black-Box Games—0
Density Ratio Estimation-based Bayesian Optimization with Semi-Supervised Learning—0
Dependence between Bayesian neural network units—0
Designing Robust Biotechnological Processes Regarding Variabilities using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design—0
Using Gaussian Processes to Design Dynamic Experiments for Black-Box Model Discrimination under Uncertainty—0
Design of Experiments for Verifying Biomolecular Networks—0
Detecting British Columbia Coastal Rainfall Patterns by Clustering Gaussian Processes—0
Analysis of Nonstationary Time Series Using Locally Coupled Gaussian Processes—0
Collaborative Gaussian Processes for Preference Learning—0
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Benchmark Results

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