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Found 6 papers, 2 papers with code
Beyond Multilayer Perceptrons: Investigating Complex Topologies in Neural Networks
Our study sheds light on the potential of complex topologies for enhancing the performance of ANNs and provides a foundation for future research exploring the interplay between multiple topological attributes and their impact on model performance.
Multimodal and multicontrast image fusion via deep generative models
As proof of concept, we test our architecture on the well characterized Human Connectome Project database demonstrating that our latent embeddings can be clustered into easily separable subject strata which, in turn, map to different phenotypical information which was not included in the embedding creation process.
VAESim: A probabilistic approach for self-supervised prototype discovery
Then, we reconstruct the sample based on a similarity measure between the sample embedding and the prototypical vectors of the clusters.
Contrastive learning for unsupervised medical image clustering and reconstruction
The lack of large labeled medical imaging datasets, along with significant inter-individual variability compared to clinically established disease classes, poses significant challenges in exploiting medical imaging information in a precision medicine paradigm, where in principle dense patient-specific data can be employed to formulate individual predictions and/or stratify patients into finer-grained groups which may follow more homogeneous trajectories and therefore empower clinical trials.
4Ward: a Relayering Strategy for Efficient Training of Arbitrarily Complex Directed Acyclic Graphs
Thanks to their ease of implementation, multilayer perceptrons (MLPs) have become ubiquitous in deep learning applications.
An intertwined neural network model for EEG classification in brain-computer interfaces
In this paper, we present a deep neural network architecture specifically engineered to a) provide state-of-the-art performance in multiclass motor imagery classification and b) remain robust to preprocessing to enable real-time processing of raw data as it streams from EEG and BCI equipment.
