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Found 25 papers, 5 papers with code
Interpretable Multi-Source Data Fusion Through Latent Variable Gaussian Process
Towards resolving this issue, a multi-source data fusion framework based on Latent Variable Gaussian Process (LVGP) is proposed.
Methods for Acquiring and Incorporating Knowledge into Stock Price Prediction: A Survey
Predicting stock prices presents a challenging research problem due to the inherent volatility and non-linear nature of the stock market.
Enhancing Cell Proliferation and Migration by MIR-Carbonyl Vibrational Coupling: Insights from Transcriptome Profiling
Cell proliferation and migration highly relate to normal tissue self-healing, therefore it is highly significant for artificial controlling.
Regulating the Hydrophobic Domain in Peptide-Catecholamine Coassembled Nanostructures for Fluorescence Enhancement
Hydrophobic domains provide specific microenvironment for essential functional activities in life.
Unleashing the Potential of Unsupervised Deep Outlier Detection through Automated Training Stopping
To our knowledge, our approach is the first to enable reliable identification of the optimal training iteration during training without requiring any labels.
Application of probabilistic modeling and automated machine learning framework for high-dimensional stress field
Modern computational methods, involving highly sophisticated mathematical formulations, enable several tasks like modeling complex physical phenomenon, predicting key properties and design optimization.
Unsupervised Graph Outlier Detection: Problem Revisit, New Insight, and Superior Method
In addition, we observe that existing algorithms have a performance drop with the mitigated data leakage issue.
A Framework for CSI-Based Indoor Localization with 1D Convolutional Neural Networks
Modern indoor localization techniques are essential to overcome the weak GPS coverage in indoor environments.
Inverse Aerodynamic Design of Gas Turbine Blades using Probabilistic Machine Learning
One of the critical components in Industrial Gas Turbines (IGT) is the turbine blade.
Label-informed Graph Structure Learning for Node Classification
Graph Neural Networks (GNNs) have achieved great success among various domains.
Fully Hyperbolic Graph Convolution Network for Recommendation
These methods embed users and items in Euclidean space, and perform graph convolution on user-item interaction graphs.
CHISEL: Compression-Aware High-Accuracy Embedded Indoor Localization with Deep Learning
GPS technology has revolutionized the way we localize and navigate outdoors.
Graph Classification by Mixture of Diverse Experts
Graph classification is a challenging research problem in many applications across a broad range of domains.
GraphDIVE: Graph Classification by Mixture of Diverse Experts
Graph classification is a challenging research problem in many applications across a broad range of domains.
Discovery of Physics and Characterization of Microstructure from Data with Bayesian Hidden Physics Models
Remarkably, we find that the physics learned from the first specimen allows us to understand the backscattering observed in the latter sample, a qualitative feature that is wholly absent from the specimen from which the physics were inferred.
Concentration solutions to singularly prescribed Gaussian and geodesic curvatures problem
We consider the following Liouville-type equation with exponential Neumann boundary condition: $$ -\Delta\tilde u = \varepsilon^2 K(x) e^{2\tilde u}, \quad x\in D, \qquad \frac{\partial \tilde u}{\partial n} + 1 = \varepsilon \kappa(x) e^{\tilde u}, \quad x\in\partial D, $$ where $D\subset \mathbb R^2$ is the unit disc, $\varepsilon^2 K(x)$ and $\varepsilon \kappa(x)$ stand for the prescribed Gaussian curvature and the prescribed geodesic curvature of the boundary, respectively.
Analysis of PDEs
Data-based Discovery of Governing Equations
Without a prior definition of the model structure, first a free-form of the equation is discovered, and then calibrated and validated against the available data.
Data-Informed Decomposition for Localized Uncertainty Quantification of Dynamical Systems
The region of interest can be specified based on the localization features of the solution, user interest, and correlation length of the random material properties.
Bayesian learning of orthogonal embeddings for multi-fidelity Gaussian Processes
We present a Bayesian approach to identify optimal transformations that map model input points to low dimensional latent variables.
Advances in Bayesian Probabilistic Modeling for Industrial Applications
The methodology, called GE's Bayesian Hybrid Modeling (GEBHM), is a probabilistic modeling method, based on the Kennedy and O'Hagan framework, that has been continuously scaled-up and industrialized over several years.
Bayesian task embedding for few-shot Bayesian optimization
We describe a method for Bayesian optimization by which one may incorporate data from multiple systems whose quantitative interrelationships are unknown a priori.
Data-driven discovery of free-form governing differential equations
We present a method of discovering governing differential equations from data without the need to specify a priori the terms to appear in the equation.
A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty
Multi-fidelity Gaussian process is a common approach to address the extensive computationally demanding algorithms such as optimization, calibration and uncertainty quantification.
Towards Scalable Gaussian Process Modeling
Accurately estimating these hyperparameters is a key ingredient in developing a reliable and generalizable surrogate model.
Joint Representation Classification for Collective Face Recognition
In this paper, a joint representation classification (JRC) for collective face recognition is proposed.
