Search Results for author:
Found 7 papers, 3 papers with code
On-Off Pattern Encoding and Path-Count Encoding as Deep Neural Network Representations
Specifically, we consider \textit{On-Off pattern} and \textit{PathCount} for investigating how information is stored in deep representations.
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).
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.
Probabilistic Imputation for Time-series Classification with Missing Data
In this paper, we propose a novel probabilistic framework for classification with multivariate time series data with missing values.
Traversing Between Modes in Function Space for Fast Ensembling
While this provides a way to efficiently train ensembles, for inference, multiple forward passes should still be executed using all the ensemble parameters, which often becomes a serious bottleneck for real-world deployment.
Martingale Posterior Neural Processes
Based on this result, instead of assuming any form of the latent variables, we equip a NP with a predictive distribution implicitly defined with neural networks and use the corresponding martingale posteriors as the source of uncertainty.
Scale Mixtures of Neural Network Gaussian Processes
We show that simply introducing a scale prior on the last-layer parameters can turn infinitely-wide neural networks of any architecture into a richer class of stochastic processes.
