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 81–90 of 1963 papers

TitleStatusHype
Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence ModelingCode1
Supernova Light Curves Approximation based on Neural Network ModelsCode1
LIMO: Latent Inceptionism for Targeted Molecule GenerationCode1
Neural Diffusion ProcessesCode1
Active Bayesian Causal InferenceCode1
Posterior and Computational Uncertainty in Gaussian ProcessesCode1
Bayesian Active Learning with Fully Bayesian Gaussian ProcessesCode1
High-dimensional additive Gaussian processes under monotonicity constraintsCode1
Probabilistic Estimation of Instantaneous Frequencies of Chirp SignalsCode1
Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property PredictionCode1
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Benchmark Results

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