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Found 11 papers, 5 papers with code
Integrating Statistical Significance and Discriminative Power in Pattern Discovery
Pattern discovery plays a central role in both descriptive and predictive tasks across multiple domains.
TriSig: Assessing the statistical significance of triclusters
This work aims at proposing a statistical frame to assess the probability of patterns in tensor data to deviate from null expectations, extending well-established principles for assessing the statistical significance of patterns in matrix data.
User-Specific Bicluster-based Collaborative Filtering: Handling Preference Locality, Sparsity and Subjectivity
Collaborative Filtering (CF), the most common approach to build Recommender Systems, became pervasive in our daily lives as consumers of products and services.
EEG to fMRI Synthesis Benefits from Attentional Graphs of Electrode Relationships
In addition, we observe that haemodynamic activity at the scalp, in contrast with sub-cortical regions, is relevant to the learned shared space.
On the Role of Multi-Objective Optimization to the Transit Network Design Problem
Then, Genetic Algorithms are used, considering both single and multi objective approaches, to redesign the bus network that better fits the observed traffic demand.
Fitting Regularized Population Dynamics with Neural Differential Equations
Neural differential equations (neural DEs) are yet to see success in its application as interpretable autoencoders/descriptors, where they directly model a population of signals with the learned vector field.
Context-aware demand prediction in bike sharing systems: incorporating spatial, meteorological and calendrical context
Bike sharing demand is increasing in large cities worldwide.
DI2: prior-free and multi-item discretization ofbiomedical data and its applications
Motivation: A considerable number of data mining approaches for biomedical data analysis, including state-of-the-art associative models, require a form of data discretization.
EEG to fMRI Synthesis: Is Deep Learning a candidate?
EEG to fMRI synthesis offers a way to enhance and augment brain image data, and guarantee access to more affordable, portable and long-lasting protocols of brain activity monitoring.
fMRI Multiple Missing Values Imputation Regularized by a Recurrent Denoiser
Functional Magnetic Resonance Imaging (fMRI) is a neuroimaging technique with pivotal importance due to its scientific and clinical applications.
On the use of Pairwise Distance Learning for Brain Signal Classification with Limited Observations
The increasing access to brain signal data using electroencephalography creates new opportunities to study electrophysiological brain activity and perform ambulatory diagnoses of neuronal diseases.
