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 576–600 of 1963 papers

TitleStatusHype
Few-Shot Speech Deepfake Detection Adaptation with Gaussian ProcessesCode0
A piece-wise constant approximation for non-conjugate Gaussian Process modelsCode0
Heterogeneous Multi-Task Gaussian Cox ProcessesCode0
Fixed-Mean Gaussian Processes for Post-hoc Bayesian Deep LearningCode0
Benchmarking optimality of time series classification methods in distinguishing diffusionsCode0
FRIDAY: Real-time Learning DNN-based Stable LQR controller for Nonlinear Systems under Uncertain DisturbancesCode0
Hierarchical Inducing Point Gaussian Process for Inter-domain ObservationsCode0
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian OptimizationCode0
Federated Learning for Non-factorizable Models using Deep Generative Prior ApproximationsCode0
Embarrassingly Parallel Inference for Gaussian ProcessesCode0
Beyond Intuition, a Framework for Applying GPs to Real-World DataCode0
Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive UncertaintiesCode0
Adjusting Model Size in Continual Gaussian Processes: How Big is Big Enough?Code0
Hyperbolic Secant representation of the logistic function: Application to probabilistic Multiple Instance Learning for CT intracranial hemorrhage detectionCode0
Fast Evaluation of Additive Kernels: Feature Arrangement, Fourier Methods, and Kernel DerivativesCode0
How Good are Low-Rank Approximations in Gaussian Process Regression?Code0
End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control TasksCode0
Implementation and Analysis of GPU Algorithms for Vecchia ApproximationCode0
Fast Kernel Approximations for Latent Force Models and Convolved Multiple-Output Gaussian processesCode0
Bias-Free Scalable Gaussian Processes via Randomized TruncationsCode0
Federated Causal Inference from Observational DataCode0
From Deep Additive Kernel Learning to Last-Layer Bayesian Neural Networks via Induced Prior ApproximationCode0
Incorporating Prior Knowledge into Neural Networks through an Implicit Composite KernelCode0
Entropic Trace Estimates for Log DeterminantsCode0
Gaussian Process Behaviour in Wide Deep Neural NetworksCode0
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Benchmark Results

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