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Found 6 papers, 2 papers with code
Stochastic Optimal Control for Diffusion Bridges in Function Spaces
In this paper, we present a theory of stochastic optimal control (SOC) tailored to infinite-dimensional spaces, aiming to extend diffusion-based algorithms to function spaces.
Joint-Embedding Masked Autoencoder for Self-supervised Learning of Dynamic Functional Connectivity from the Human Brain
Although generative self-supervised learning techniques, especially masked autoencoders, have shown promising results in representation learning in various domains, their application to dynamic graphs for dynamic functional connectivity remains underexplored, facing challenges in capturing high-level semantic representations.
Large-scale Graph Representation Learning of Dynamic Brain Connectome with Transformers
Here, we propose a method for learning the representation of dynamic functional connectivity with Graph Transformers.
A Generative Self-Supervised Framework using Functional Connectivity in fMRI Data
Deep neural networks trained on Functional Connectivity (FC) networks extracted from functional Magnetic Resonance Imaging (fMRI) data have gained popularity due to the increasing availability of data and advances in model architectures, including Graph Neural Network (GNN).
On Divergence Measures for Bayesian Pseudocoresets
Finally, we propose a novel Bayesian pseudocoreset algorithm based on minimizing forward KL divergence.
Convergence Analysis of Optimization Algorithms
The regret bound of an optimization algorithms is one of the basic criteria for evaluating the performance of the given algorithm.
