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Found 6 papers, 2 papers with code
Generative Oversampling for Imbalanced Data via Majority-Guided VAE
Learning with imbalanced data is a challenging problem in deep learning.
ByPE-VAE: Bayesian Pseudocoresets Exemplar VAE
To address this issue, we propose Bayesian Pseudocoresets Exemplar VAE (ByPE-VAE), a new variant of VAE with a prior based on Bayesian pseudocoreset.
Stein Variational Gradient Descent with Multiple Kernel
In practice, we notice that the kernel used in SVGD-based methods has a decisive effect on the empirical performance.
Mutual Information Gradient Estimation for Representation Learning
To this end, we propose the Mutual Information Gradient Estimator (MIGE) for representation learning based on the score estimation of implicit distributions.
Deep Minimax Probability Machine
In a worst-case scenario, MPM tries to minimize an upper bound of misclassification probabilities, considering the global information (i. e., mean and covariance information of each class).
Stochastic Sequential Neural Networks with Structured Inference
Inheriting these advantages of stochastic neural sequential models, we propose a structured and stochastic sequential neural network, which models both the long-term dependencies via recurrent neural networks and the uncertainty in the segmentation and labels via discrete random variables.
