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Found 4 papers, 1 papers with code
Learning Signal Representations for EEG Cross-Subject Channel Selection and Trial Classification
Considering multi-channel trial recordings as statistical units and the EEG decoding task as the class of reference, the algorithm (i) exploits channel-specific 1D-Convolutional Neural Networks (1D-CNNs) as feature extractors in a supervised fashion to maximize class separability; (ii) it reduces a high dimensional multi-channel trial representation into a unique trial vector by concatenating the channels' embeddings and (iii) recovers the complex inter-channel relationships during channel selection, by exploiting an ensemble of AutoEncoders (AE) to identify from these vectors the most relevant channels to perform classification.
A Deep Variational Approach to Clustering Survival Data
In this work, we study the problem of clustering survival data $-$ a challenging and so far under-explored task.
Feature Selection for Imbalanced Data with Deep Sparse Autoencoders Ensemble
To achieve FS advantages in this setting, we propose a filtering FS algorithm ranking feature importance on the basis of the Reconstruction Error of a Deep Sparse AutoEncoders Ensemble (DSAEE).
Learning High-Order Interactions via Targeted Pattern Search
The algorithm relies on an interaction learning step based on a well-known frequent item set mining algorithm, and a novel dissimilarity-based interaction selection step that allows the user to specify the number of interactions to be included in the LR model.
