AI Medical Compendium

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Distributed aggregative optimization with affine coupling constraints.

Neural networks : the official journal of the International Neural Network Society
This paper investigates a distributed aggregative optimization problem subject to coupling affine inequality constraints, in which local objective functions depend not only on their own decision variables but also on an aggregation of all the agents'...

Decoding neuronal networks: A Reservoir Computing approach for predicting connectivity and functionality.

Neural networks : the official journal of the International Neural Network Society
In this study, we address the challenge of analyzing electrophysiological measurements in neuronal networks. Our computational model, based on the Reservoir Computing Network (RCN) architecture, deciphers spatio-temporal data obtained from electrophy...

Multi-modal cross-domain self-supervised pre-training for fMRI and EEG fusion.

Neural networks : the official journal of the International Neural Network Society
Neuroimaging techniques including functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) have shown promise in detecting functional abnormalities in various brain disorders. However, existing studies often focus on a single domai...

On the probability of necessity and sufficiency of explaining Graph Neural Networks: A lower bound optimization approach.

Neural networks : the official journal of the International Neural Network Society
The explainability of Graph Neural Networks (GNNs) is critical to various GNN applications, yet it remains a significant challenge. A convincing explanation should be both necessary and sufficient simultaneously. However, existing GNN explaining appr...

Heterophilous distribution propagation for Graph Neural Networks.

Neural networks : the official journal of the International Neural Network Society
Graph Neural Networks (GNNs) have achieved remarkable success in various graph mining tasks by aggregating information from neighborhoods for representation learning. The success relies on the homophily assumption that nearby nodes exhibit similar be...

Unified Knowledge-Guided Molecular Graph Encoder with multimodal fusion and multi-task learning.

Neural networks : the official journal of the International Neural Network Society
The remarkable success of Graph Neural Networks underscores their formidable capacity to assimilate multimodal inputs, markedly enhancing performance across a broad spectrum of domains. In the context of molecular modeling, considerable efforts have ...

Promises and perils of using Transformer-based models for SE research.

Neural networks : the official journal of the International Neural Network Society
Many Transformer-based pre-trained models for code have been developed and applied to code-related tasks. In this paper, we analyze 519 papers published on this topic during 2017-2023, examine the suitability of model architectures for different task...

Probabilistic-sampling-based asynchronous control for semi-Markov jumping neural networks with reaction-diffusion terms.

Neural networks : the official journal of the International Neural Network Society
This paper investigates the probabilistic-sampling-based asynchronous control problem for semi-Markov reaction-diffusion neural networks (SMRDNNs). Aiming at mitigating the drawback of the well-known fixed-sampling control law, a more general probabi...

FPANet: Frequency-based video demoiréing using frame-level post alignment.

Neural networks : the official journal of the International Neural Network Society
Moiré patterns, created by the interference between overlapping grid patterns in the pixel space, degrade the visual quality of images and videos. Therefore, removing such patterns (demoiréing) is crucial, yet remains a challenge due to their complex...

Enhancing consistency and mitigating bias: A data replay approach for incremental learning.

Neural networks : the official journal of the International Neural Network Society
Deep learning systems are prone to catastrophic forgetting when learning from a sequence of tasks, as old data from previous tasks is unavailable when learning a new task. To address this, some methods propose replaying data from previous tasks durin...