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Gaussian Processes
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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.
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이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
LLINBO: Trustworthy LLM-in-the-Loop Bayesian Optimization
Sample-Constrained Black Box Optimization for Audio Personalization
Fast Gaussian Processes under Monotonicity Constraints
MathOptAI.jl: Embed trained machine learning predictors into JuMP models
Fast ground penetrating radar dual-parameter full waveform inversion method accelerated by hybrid compilation of CUDA kernel function and PyTorch
Trustworthy Prediction with Gaussian Process Knowledge Scores
Predicting Stock Market Crash with Bayesian Generalised Pareto Regression
Scalable Machine Learning Algorithms using Path Signatures
Gaussian Processes and Reproducing Kernels: Connections and Equivalences
Overcoming Overfitting in Reinforcement Learning via Gaussian Process Diffusion Policy
Effect Decomposition of Functional-Output Computer Experiments via Orthogonal Additive Gaussian Processes
Accurate and Uncertainty-Aware Multi-Task Prediction of HEA Properties Using Prior-Guided Deep Gaussian Processes
Tailored Architectures for Time Series Forecasting: Evaluating Deep Learning Models on Gaussian Process-Generated Data
The Currents of Conflict: Decomposing Conflict Trends with Gaussian Processes
Machine learning for in-situ composition mapping in a self-driving magnetron sputtering system
Direct Integration of Recursive Gaussian Process Regression Into Extended Kalman Filters With Application to Vapor Compression Cycle Control
Transformers Beyond Order: A Chaos-Markov-Gaussian Framework for Short-Term Sentiment Forecasting of Any Financial OHLC timeseries Data
Discovery of Probabilistic Dirichlet-to-Neumann Maps on Graphs
Constrained Bayesian Optimization under Bivariate Gaussian Process with Application to Cure Process Optimization
Fast Derivative Valuation from Volatility Surfaces using Machine Learning
Few-Shot Speech Deepfake Detection Adaptation with Gaussian Processes
Adaptive finite element type decomposition of Gaussian processes
A Provable Approach for End-to-End Safe Reinforcement Learning
A Multi-output Gaussian Process Regression with Negative Transfer Mitigation for Generating Boundary Test Scenarios of Multi-UAV Systems
Learning-Based Robust Fixed-Time Terminal Sliding Mode Control
STACI: Spatio-Temporal Aleatoric Conformal Inference
Empirical 3D Channel Modeling for Cellular-Connected UAVs: A Triple-Layer Machine Learning Approach
A fast sound power prediction tool for genset noise using machine learning
A Novel Shape-Aware Topological Representation for GPR Data with DNN Integration
Scalable Gaussian Processes with Low-Rank Deep Kernel Decomposition
Unsupervised cell segmentation by fast Gaussian Processes
Predictive posterior sampling from non-stationnary Gaussian process priors via Diffusion models with application to climate data
Multi-Output Gaussian Processes for Graph-Structured Data
Modular Jump Gaussian Processes
Gaussian Processes in Power Systems: Techniques, Applications, and Future Works
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