AI Medical Compendium

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Neural network emulator for atmospheric chemical ODE.

Neural networks : the official journal of the International Neural Network Society
Modelling atmospheric chemistry is complex and computationally intense. Given the recent success of Deep neural networks in digital signal processing, we propose a Neural Network Emulator for fast chemical concentration modelling. We consider atmosph...

Finite-time H output synchronization for DCRDNNs with multiple delayed and adaptive output couplings.

Neural networks : the official journal of the International Neural Network Society
This work concentrates on solving the finite-time H output synchronization (FTHOS) issue of directed coupled reaction-diffusion neural networks (DCRDNNs) with multiple delayed and adaptive output couplings in the presence of external disturbances. Ba...

VCSAP: Online reinforcement learning exploration method based on visitation count of state-action pairs.

Neural networks : the official journal of the International Neural Network Society
In the domain of online reinforcement learning, strategies that leverage inherent rewards for exploration tend to achieve commendable outcomes within contexts characterized by deceptive or sparse rewards. Counting through the visitation of states is ...

Spectral adversarial attack on graph via node injection.

Neural networks : the official journal of the International Neural Network Society
Graph Neural Networks (GNNs) have shown remarkable achievements and have been extensively applied in various downstream tasks, such as node classification and community detection. However, recent studies have demonstrated that GNNs are vulnerable to ...

CS-QCFS: Bridging the performance gap in ultra-low latency spiking neural networks.

Neural networks : the official journal of the International Neural Network Society
Spiking Neural Networks (SNNs) are at the forefront of computational neuroscience, emulating the nuanced dynamics of biological systems. In the realm of SNN training methods, the conversion from ANNs to SNNs has generated significant interest due to ...

Enhancing Recommender Systems through Imputation and Social-Aware Graph Convolutional Neural Network.

Neural networks : the official journal of the International Neural Network Society
Recommendation systems are vital tools for helping users discover content that suits their interests. Collaborative filtering methods are one of the techniques employed for analyzing interactions between users and items, which are typically stored in...

Simignore: Exploring and enhancing multimodal large model complex reasoning via similarity computation.

Neural networks : the official journal of the International Neural Network Society
Recently, the field of multimodal large language models (MLLMs) has grown rapidly, with many Large Vision-Language Models (LVLMs) relying on sequential visual representations. In these models, images are broken down into numerous tokens before being ...

Identity Model Transformation for boosting performance and efficiency in object detection network.

Neural networks : the official journal of the International Neural Network Society
Modifying the structure of an existing network is a common method to further improve the performance of the network. However, modifying some layers in network often results in pre-trained weight mismatch, and fine-tune process is time-consuming and r...

Improving the performance of echo state networks through state feedback.

Neural networks : the official journal of the International Neural Network Society
Reservoir computing, using nonlinear dynamical systems, offers a cost-effective alternative to neural networks for complex tasks involving processing of sequential data, time series modeling, and system identification. Echo state networks (ESNs), a t...

Information-controlled graph convolutional network for multi-view semi-supervised classification.

Neural networks : the official journal of the International Neural Network Society
Graph convolutional networks have achieved remarkable success in the field of multi-view learning. Unfortunately, most graph convolutional network-based multi-view learning methods fail to capture long-range dependencies due to the over-smoothing pro...