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Meet OmniPred: A Machine Learning Framework to Transform Experimental Design with Universal Regression Models
MarkTechPost
The ability to predict outcomes from a myriad of parameters has traditionally been anchored in specific, narrowly focused regression methods...
3 months ago
Mechanical behavior predictions of additively manufactured microstructures using functional Gaussian process ...
Nature
Relational linkages connecting process, structure, and properties are some of the most sought after goals in additive manufacturing (AM).
36 months ago
Automatic Kernel Selection for Gaussian Processes Regression with Approximate Bayesian Computation and ...
Frontiers
The current work introduces a novel combination of two Bayesian tools, Gaussian Processes (GPs), and the use of the Approximate Bayesian...
82 months ago
Metric Learning and Manifolds: Preserving the Intrinsic Geometry
Microsoft
In recent years, manifold learning has become increasingly popular as a tool for performing non-linear dimensionality reduction.
148 months ago
A Conceptual Explanation of Bayesian Hyperparameter Optimization for Machine Learning
Towards Data Science
Following are four common methods of hyperparameter optimization for machine learning in order of increasing efficiency: I was pretty proud...
71 months ago
Bayesian nonparametric models characterize instantaneous strategies in a competitive dynamic game
Nature
Previous studies of strategic social interaction in game theory have predominantly used games with clearly-defined turns and limited choices...
62 months ago
Figure 2: Mappings between the manifold of positive definite matrices...
ResearchGate
Metric learning has been shown to be highly effective to improve the performance of nearest neighbor classification. In this paper, we address the problem...
74 months ago
Bridging Reinforcement Learning and Iterative Learning Control: Autonomous Motion Learning for Unknown, Nonlinear ...
Frontiers
This work addresses the problem of reference tracking in autonomously learning robots with unknown, nonlinear dynamics.
25 months ago
Stochastic learning and extremal-field map based autonomous guidance of low-thrust spacecraft | Scientific Reports
Nature
A supervised stochastic learning method called the Gaussian Process Regression (GPR) is used to design an autonomous guidance law for...
20 months ago
AI Researchers Propose Neural Diffusion Processes (NDPs), A Novel Approach Based Upon Diffusion Models, That ...
MarkTechPost
Neural Diffusion Processes (NDPs), a proposed denoising diffusion model approach for learning probabilities on function spaces and...
24 months ago