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Found 9 papers, 5 papers with code
BloomGML: Graph Machine Learning through the Lens of Bilevel Optimization
These optimal features typically depend on tunable parameters of the lower-level energy in such a way that the entire bilevel pipeline can be trained end-to-end.
Efficient Multimodal Sampling via Tempered Distribution Flow
Sampling from high-dimensional distributions is a fundamental problem in statistical research and practice.
Learning Manifold Dimensions with Conditional Variational Autoencoders
Although the variational autoencoder (VAE) and its conditional extension (CVAE) are capable of state-of-the-art results across multiple domains, their precise behavior is still not fully understood, particularly in the context of data (like images) that lie on or near a low-dimensional manifold.
Learning Multitask Gaussian Bayesian Networks
We consider the problem of identifying alterations of brain functional connectivity for a single MDD patient.
Training Deep Generative Models via Auxiliary Supervised Learning
Deep generative modeling has long been viewed as a challenging unsupervised learning problem, partly due to the lack of labels and the high dimension of the data.
Unbiased Contrastive Divergence Algorithm for Training Energy-Based Latent Variable Models
The contrastive divergence algorithm is a popular approach to training energy-based latent variable models, which has been widely used in many machine learning models such as the restricted Boltzmann machines and deep belief nets.
Randomized spectral co-clustering for large-scale directed networks
Directed networks are broadly used to represent asymmetric relationships among units.
Stochastic Approximate Gradient Descent via the Langevin Algorithm
We introduce a novel and efficient algorithm called the stochastic approximate gradient descent (SAGD), as an alternative to the stochastic gradient descent for cases where unbiased stochastic gradients cannot be trivially obtained.
Gradient-based Sparse Principal Component Analysis with Extensions to Online Learning
In this work we study sparse PCA based on the convex FPS formulation, and propose a new algorithm that is computationally efficient and applicable to large and high-dimensional data sets.
