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08/09/2023
Methods for Acquiring and Incorporating Knowledge into Stock Price Prediction: A Survey
Predicting stock prices presents a challenging research problem due to t...
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05/26/2023
Unleashing the Potential of Unsupervised Deep Outlier Detection through Automated Training Stopping
Outlier detection (OD) has received continuous research interests due to...
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03/15/2023
Application of probabilistic modeling and automated machine learning framework for high-dimensional stress field
Modern computational methods, involving highly sophisticated mathematica...
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10/24/2022
Are we really making much progress in unsupervised graph outlier detection? Revisiting the problem with new insight and superior method
A large number of studies on Graph Outlier Detection (GOD) have emerged ...
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05/17/2022
A Framework for CSI-Based Indoor Localization with 1D Convolutional Neural Networks
Modern indoor localization techniques are essential to overcome the weak...
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08/10/2021
Fully Hyperbolic Graph Convolution Network for Recommendation
Recently, Graph Convolution Network (GCN) based methods have achieved ou...
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08/10/2021
Label-informed Graph Structure Learning for Node Classification
Graph Neural Networks (GNNs) have achieved great success among various d...
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07/02/2021
CHISEL: Compression-Aware High-Accuracy Embedded Indoor Localization with Deep Learning
GPS technology has revolutionized the way we localize and navigate outdo...
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03/29/2021
Graph Classification by Mixture of Diverse Experts
Graph classification is a challenging research problem in many applicati...
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03/12/2021
Discovery of Physics and Characterization of Microstructure from Data with Bayesian Hidden Physics Models
There has been a surge in the interest of using machine learning techniq...
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12/01/2020
Seasonal association between viral etiologies of hospitalized acute lower respiratory infections and meteorological factors in China
Background: Acute lower respiratory infections present pronounced season...
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08/14/2020
Data-Informed Decomposition for Localized Uncertainty Quantification of Dynamical Systems
Industrial dynamical systems often exhibit multi-scale response due to m...
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08/05/2020
Bayesian learning of orthogonal embeddings for multi-fidelity Gaussian Processes
We present a Bayesian approach to identify optimal transformations that ...
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03/26/2020
Advances in Bayesian Probabilistic Modeling for Industrial Applications
Industrial applications frequently pose a notorious challenge for state-...
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01/02/2020
Bayesian task embedding for few-shot Bayesian optimization
We describe a method for Bayesian optimization by which one may incorpor...
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09/27/2019
Data-driven discovery of free-form governing differential equations
We present a method of discovering governing differential equations from...
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07/26/2019
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 exte...
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07/25/2019
Towards Scalable Gaussian Process Modeling
Numerous engineering problems of interest to the industry are often char...
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03/16/2013