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Found 11 papers, 0 papers with code
Two-Stage Stance Labeling: User-Hashtag Heuristics with Graph Neural Networks
In this work, we develop a two stage stance labeling method that utilizes the user-hashtag bipartite graph and the user-user interaction graph.
Machine Fault Classification using Hamiltonian Neural Networks
A new approach is introduced to classify faults in rotating machinery based on the total energy signature estimated from sensor measurements.
Evaluation of Induced Expert Knowledge in Causal Structure Learning by NOTEARS
The goal of the paper is to study the impact of expert knowledge on causal relations in the form of additional constraints used in the formulation of the nonparametric NOTEARS.
From Causal Pairs to Causal Graphs
Causal structure learning from observational data remains a non-trivial task due to various factors such as finite sampling, unobserved confounding factors, and measurement errors.
Explainable Deep Modeling of Tabular Data using TableGraphNet
The vast majority of research on explainability focuses on post-explainability rather than explainable modeling.
Approximate Sampling using an Accelerated Metropolis-Hastings based on Bayesian Optimization and Gaussian Processes
The main innovations include the use of Bayesian optimization to reach the high probability region quickly, emulating the target distribution using Gaussian processes (GP), and using Laplace approximation of the GP to build a proposal distribution that captures the underlying correlation better.
A Multiple Filter Based Neural Network Approach to the Extrapolation of Adsorption Energies on Metal Surfaces for Catalysis Applications
Our investigations also included the case when the species data used to train the predictive model is of different size relative to the species the model tries to predict - an extrapolation in the data space which is typically difficult with regular machine learning models.
An Approximate Bayesian Long Short-Term Memory Algorithm for Outlier Detection
In this study, we propose an approximate estimation of the weights uncertainty using Ensemble Kalman Filter, which is easily scalable to a large number of weights.
EnLLVM: Ensemble Based Nonlinear Bayesian Filtering Using Linear Latent Variable Models
Real-time nonlinear Bayesian filtering algorithms are overwhelmed by data volume, velocity and increasing complexity of computational models.
Approximate Computational Approaches for Bayesian Sensor Placement in High Dimensions
Then, sensors are placed where highest mutual information (lower bound) is achieved and QoI is inferred via Bayes rule given sensor measurements.
Environmental Modeling Framework using Stacked Gaussian Processes
A network of independently trained Gaussian processes (StackedGP) is introduced to obtain predictions of quantities of interest with quantified uncertainties.
