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Found 13 papers, 8 papers with code
Variational Stochastic Gradient Descent for Deep Neural Networks
We model gradient updates as a probabilistic model and utilize stochastic variational inference (SVI) to derive an efficient and effective update rule.
Exploring Continual Learning of Diffusion Models
Diffusion models have achieved remarkable success in generating high-quality images thanks to their novel training procedures applied to unprecedented amounts of data.
Discouraging posterior collapse in hierarchical Variational Autoencoders using context
Hierarchical Variational Autoencoders (VAEs) are among the most popular likelihood-based generative models.
Equivariant Priors for Compressed Sensing with Unknown Orientation
In compressed sensing, the goal is to reconstruct the signal from an underdetermined system of linear measurements.
On Analyzing Generative and Denoising Capabilities of Diffusion-based Deep Generative Models
Their main strength comes from their unique setup in which a model (the backward diffusion process) is trained to reverse the forward diffusion process, which gradually adds noise to the input signal.
Alleviating Adversarial Attacks on Variational Autoencoders with MCMC
Variational autoencoders (VAEs) are latent variable models that can generate complex objects and provide meaningful latent representations.
Diagnosing Vulnerability of Variational Auto-Encoders to Adversarial Attacks
In this work, we explore adversarial attacks on the Variational Autoencoders (VAE).
CKConv: Continuous Kernel Convolution For Sequential Data
Convolutional networks are unable to handle sequences of unknown size and their memory horizon must be defined a priori.
Ranked #5 on
Sequential Image Classification
on Sequential MNIST
Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems
Automatic segmentation methods based on deep learning have recently demonstrated state-of-the-art performance, outperforming the ordinary methods.
BooVAE: A Scalable Framework for Continual VAE Learning under Boosting Approach
Variational Auto Encoders (VAE) are capable of generating realistic images, sounds and video sequences.
BooVAE: Boosting Approach for Continual Learning of VAE
We learn the approximation of the aggregated posterior as a prior for each task.
Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems
Automatic segmentation methods based on deep learning have recently demonstrated state-of-the-art performance, outperforming the ordinary methods.
Ensemble of 3D CNN regressors with data fusion for fluid intelligence prediction
In this work, we aim at predicting children's fluid intelligence scores based on structural T1-weighted MR images from the largest long-term study of brain development and child health.
