61 research outputs found
AutoDiff: combining Auto-encoder and Diffusion model for tabular data synthesizing
Diffusion model has become a main paradigm for synthetic data generation in
many subfields of modern machine learning, including computer vision, language
model, or speech synthesis. In this paper, we leverage the power of diffusion
model for generating synthetic tabular data. The heterogeneous features in
tabular data have been main obstacles in tabular data synthesis, and we tackle
this problem by employing the auto-encoder architecture. When compared with the
state-of-the-art tabular synthesizers, the resulting synthetic tables from our
model show nice statistical fidelities to the real data, and perform well in
downstream tasks for machine learning utilities. We conducted the experiments
over publicly available datasets. Notably, our model adeptly captures the
correlations among features, which has been a long-standing challenge in
tabular data synthesis. Our code is available at
https://github.com/UCLA-Trustworthy-AI-Lab/AutoDiffusion
Inverse sequential simulation: Performance and implementation details
For good groundwater flow and solute transport numerical modeling, it is important to characterize the formation
properties. In this paper, we analyze the performance and important implementation details of a
new approach for stochastic inverse modeling called inverse sequential simulation (iSS). This approach is
capable of characterizing conductivity fields with heterogeneity patterns difficult to capture by standard
multiGaussian-based inverse approaches. The method is based on the multivariate sequential simulation
principle, but the covariances and cross-covariances used to compute the local conditional probability distributions
are computed by simple co-kriging which are derived from an ensemble of conductivity and piezometric
head fields, in a similar manner as the experimental covariances are computed in an ensemble Kalman
filtering. A sensitivity analysis is performed on a synthetic aquifer regarding the number of members of the
ensemble of realizations, the number of conditioning data, the number of piezometers at which piezometric
heads are observed, and the number of nodes retained within the search neighborhood at the moment of
computing the local conditional probabilities. The results show the importance of having a sufficiently large
number of all of the mentioned parameters for the algorithm to characterize properly hydraulic conductivity
fields with clear non-multiGaussian features.
© 2015 Elsevier Ltd. All rights reserved.The first author acknowledgs the financial support from the China Scholarship Council (CSC [2010]3010). Financial support to carry out this work was also received from the Spanish Ministry of Economy and Competitiveness through Project CGL2014-59841-P. We thank the three reviewers for their thorough review and their insightful comments, which have helped to improve the final manuscript.Xu, T.; Gómez-Hernández, JJ. (2015). Inverse sequential simulation: Performance and implementation details. Advances in Water Resources. 86B:311-326. https://doi.org/10.1016/j.advwatres.2015.04.015S31132686
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