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Found 9 papers, 4 papers with code
Balanced Marginal and Joint Distributional Learning via Mixture Cramer-Wold Distance
This measure enables us to capture both marginal and joint distributional information simultaneously, as it incorporates a mixture measure with point masses on standard basis vectors.
Joint Distributional Learning via Cramer-Wold Distance
The assumption of conditional independence among observed variables, primarily used in the Variational Autoencoder (VAE) decoder modeling, has limitations when dealing with high-dimensional datasets or complex correlation structures among observed variables.
Uniform Pessimistic Risk and Optimal Portfolio
The optimality of allocating assets has been widely discussed with the theoretical analysis of risk measures.
Interpretable Water Level Forecaster with Spatiotemporal Causal Attention Mechanisms
In real data analysis, we use the Han River dataset from 2016 to 2021, compare the proposed model with deep learning models, and confirm that our model provides an interpretable and consistent model with prior knowledge, such as a seasonality arising from the tidal force.
Causally Disentangled Generative Variational AutoEncoder
We present a new supervised learning technique for the Variational AutoEncoder (VAE) that allows it to learn a causally disentangled representation and generate causally disentangled outcomes simultaneously.
Distributional Learning of Variational AutoEncoder: Application to Synthetic Data Generation
The Gaussianity assumption has been consistently criticized as a main limitation of the Variational Autoencoder (VAE) despite its efficiency in computational modeling.
Geodesic Multi-Modal Mixup for Robust Fine-Tuning
Such a lack of alignment and uniformity might restrict the transferability and robustness of embeddings.
EXoN: EXplainable encoder Network
To improve the performance of our VAE in a classification task without the loss of performance as a generative model, we employ a new semi-supervised classification method called SCI (Soft-label Consistency Interpolation).
Primal path algorithm for compositional data analysis
To reflect these characteristics, a specific regularized regression model with linear constraints is commonly used.
