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 1001–1025 of 1963 papers

TitleStatusHype
Towards a population-informed approach to the definition of data-driven models for structural dynamics—0
Towards Fully Automated Segmentation of Rat Cardiac MRI by Leveraging Deep Learning Frameworks—0
Towards Improved Learning in Gaussian Processes: The Best of Two Worlds—0
Towards Improved Variational Inference for Deep Bayesian Models—0
Investigating Low Data, Confidence Aware Image Prediction on Smooth Repetitive Videos using Gaussian Processes—0
Towards Recurrent Autoregressive Flow Models—0
Towards Scalable Bayesian Optimization via Gradient-Informed Bayesian Neural Networks—0
Turbine location-aware multi-decadal wind power predictions for Germany using CMIP6—0
Trained quantum neural networks are Gaussian processes—0
Training Deep Gaussian Processes using Stochastic Expectation Propagation and Probabilistic Backpropagation—0
Transductive Kernels for Gaussian Processes on Graphs—0
Transductive Learning for Multi-Task Copula Processes—0
Transformers Beyond Order: A Chaos-Markov-Gaussian Framework for Short-Term Sentiment Forecasting of Any Financial OHLC timeseries Data—0
Bayesian Image Classification with Deep Convolutional Gaussian Processes—0
Transport Gaussian Processes for Regression—0
Treatment-Response Models for Counterfactual Reasoning with Continuous-time, Continuous-valued Interventions—0
Truncated Variance Reduction: A Unified Approach to Bayesian Optimization and Level-Set Estimation—0
Trust Your Robots! Predictive Uncertainty Estimation of Neural Networks with Sparse Gaussian Processes—0
tvGP-VAE: Tensor-variate Gaussian Process Prior Variational Autoencoder—0
Twin gaussian processes for structured prediction—0
Two Gaussian Approaches to Black-Box Optomization—0
Fast Deep Mixtures of Gaussian Process Experts—0
Uncertainty-Aware Out-of-Distribution Detection with Gaussian Processes—0
Uncertainty-aware Remaining Useful Life predictor—0
Uncertainty-Aware Semi-Supervised Method Using Large Unlabeled and Limited Labeled COVID-19 Data—0
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Benchmark Results

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