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Found 22 papers, 7 papers with code
Coupled generator decomposition for fusion of electro- and magnetoencephalography data
Leveraging data from a multisubject, multimodal (electro- and magnetoencephalography (EEG and MEG)) neuroimaging experiment, we demonstrate the efficacy of the framework in identifying common features in response to face perception stimuli, while accommodating modality- and subject-specific variability.
Time to Cite: Modeling Citation Networks using the Dynamic Impact Single-Event Embedding Model
Using this likelihood, we propose the Dynamic Impact Single-Event Embedding model (DISEE).
Continuous-time Graph Representation with Sequential Survival Process
We apply the developed framework to a recent continuous time dynamic latent distance model characterizing network dynamics in terms of a sequence of piecewise linear movements of nodes in latent space.
CSLP-AE: A Contrastive Split-Latent Permutation Autoencoder Framework for Zero-Shot Electroencephalography Signal Conversion
While the present work only considers conversion of EEG, the proposed CSLP-AE provides a general framework for signal conversion and extraction of content (task activation) and style (subject variability) components of general interest for the modeling and analysis of biological signals.
Probabilistic Block Term Decomposition for the Modelling of Higher-Order Arrays
Tensors are ubiquitous in science and engineering and tensor factorization approaches have become important tools for the characterization of higher order structure.
A Hybrid Membership Latent Distance Model for Unsigned and Signed Integer Weighted Networks
Graph representation learning (GRL) has become a prominent tool for furthering the understanding of complex networks providing tools for network embedding, link prediction, and node classification.
Characterizing Polarization in Social Networks using the Signed Relational Latent Distance Model
On four real social signed networks of polarization, we demonstrate that the model extracts low-dimensional characterizations that well predict friendships and animosity while providing interpretable visualizations defined by extreme positions when endowing the model with an embedding space restricted to polytopes.
Piecewise-Velocity Model for Learning Continuous-time Dynamic Node Representations
We further impose a scalable Kronecker structured Gaussian Process prior to the dynamics accounting for community structure, temporal smoothness, and disentangled (uncorrelated) latent embedding dimensions optimally learned to characterize the network dynamics.
A Hierarchical Block Distance Model for Ultra Low-Dimensional Graph Representations
This paper proposes a novel scalable graph representation learning method named the Hierarchical Block Distance Model (HBDM).
Scalable Hierarchical Embeddings of Complex Networks
Graph representation learning has become important in order to understand and predict intrinsic structures in complex networks.
Short Term Blood Glucose Prediction based on Continuous Glucose Monitoring Data
In this context, we evaluate both population-based and patient-specific RNNs and contrast them to patient-specific ARIMA models and a simple baseline predicting future observations as the last observed.
Probabilistic PARAFAC2
The PARAFAC2 is a multimodal factor analysis model suitable for analyzing multi-way data when one of the modes has incomparable observation units, for example because of differences in signal sampling or batch sizes.
Scalable Group Level Probabilistic Sparse Factor Analysis
Many data-driven approaches exist to extract neural representations of functional magnetic resonance imaging (fMRI) data, but most of them lack a proper probabilistic formulation.
Completely random measures for modelling block-structured sparse networks
Statistical methods for network data often parameterize the edge-probability by attributing latent traits such as block structure to the vertices and assume exchangeability in the sense of the Aldous-Hoover representation theorem.
Nonparametric Modeling of Dynamic Functional Connectivity in fMRI Data
We further investigate what drives dynamic states using the model on the entire data collated across subjects and task/rest.
Bayesian Dropout
Dropout has recently emerged as a powerful and simple method for training neural networks preventing co-adaptation by stochastically omitting neurons.
Completely random measures for modelling block-structured networks
Recently Caron and Fox (2014) proposed the use of a different notion of exchangeability due to Kallenberg (2009) and obtained a network model which admits power-law behaviour while retaining desirable statistical properties, however this model does not capture latent vertex traits such as block-structure.
Adaptive Reconfiguration Moves for Dirichlet Mixtures
Bayesian mixture models are widely applied for unsupervised learning and exploratory data analysis.
Non-parametric Bayesian modeling of complex networks
Modeling structure in complex networks using Bayesian non-parametrics makes it possible to specify flexible model structures and infer the adequate model complexity from the observed data.
The Infinite Degree Corrected Stochastic Block Model
On synthetic data we demonstrate that including the degree correction yields better performance both on recovering the true group structure and predicting missing links when degree heterogeneity is present, whereas performance is on par for data with no degree heterogeneity within clusters.
Nonparametric Bayesian models of hierarchical structure in complex networks
Analyzing and understanding the structure of complex relational data is important in many applications including analysis of the connectivity in the human brain.
Infinite Relational Modeling of Functional Connectivity in Resting State fMRI
Functional magnetic resonance imaging (fMRI) can be applied to study the functional connectivity of the neural elements which form complex network at a whole brain level.
