5 papers
Decomposing A Neural Learner
Zelong Bi
Neural learners for classification and regression are often criticized as black boxes. We propose a probabilistic decomposition of neural learners that accounts for heterogeneous p…
Missing Data Imputation under Manifold Hypothesis
Zelong Bi, Amuchechukwu Ibenegbu, Sarat Moka
The manifold hypothesis posits that high-dimensional data are concentrated near a low-dimensional embedded manifold. Recent advances in mixture variational autoencoders (VAEs) prov…
Manifold Dimension Estimation via Local Graph Structure
Zelong Bi, Pierre Lafaye de Micheaux
Most existing manifold dimension estimators rely on the assumption that the underlying manifold is locally flat within the neighborhoods under consideration. More recently, curvatu…
A Survey and Comparative Evaluation of Intrinsic Dimension Estimators under the Manifold Hypothesis
Zelong Bi, Pierre Lafaye de Micheaux
The manifold hypothesis suggests that high-dimensional data often lie on or near a low-dimensional manifold. Estimating the dimension of this manifold is essential for leveraging i…
Benfordness of the Generalized Gamma Distribution
Zelong Bi, Irfan Durmić, Steven J. Miller
The generalized gamma distribution shows up in many problems related to engineering, hydrology as well as survival analysis. Earlier work has been done that estimated the deviation…