AI Medical Compendium

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Stabilizing sequence learning in stochastic spiking networks with GABA-Modulated STDP.

Neural networks : the official journal of the International Neural Network Society
Cortical networks are capable of unsupervised learning and spontaneous replay of complex temporal sequences. Endowing artificial spiking neural networks with similar learning abilities remains a challenge. In particular, it is unresolved how differen...

Self-supervised pre-trained neural network for quantum natural language processing.

Neural networks : the official journal of the International Neural Network Society
Quantum computing models have propelled advances in many application domains. However, in the field of natural language processing (NLP), quantum computing models are limited in representation capacity due to the high linearity of the underlying quan...

Advancements in exponential synchronization and encryption techniques: Quaternion-Valued Artificial Neural Networks with two-sided coefficients.

Neural networks : the official journal of the International Neural Network Society
This paper presents cutting-edge advancements in exponential synchronization and encryption techniques, focusing on Quaternion-Valued Artificial Neural Networks (QVANNs) that incorporate two-sided coefficients. The study introduces a novel approach t...

DropNaE: Alleviating irregularity for large-scale graph representation learning.

Neural networks : the official journal of the International Neural Network Society
Large-scale graphs are prevalent in various real-world scenarios and can be effectively processed using Graph Neural Networks (GNNs) on GPUs to derive meaningful representations. However, the inherent irregularity found in real-world graphs poses cha...

Temporal spiking generative adversarial networks for heading direction decoding.

Neural networks : the official journal of the International Neural Network Society
The spike-based neuronal responses within the ventral intraparietal area (VIP) exhibit intricate spatial and temporal dynamics in the posterior parietal cortex, presenting decoding challenges such as limited data availability at the biological popula...

Dual-tower model with semantic perception and timespan-coupled hypergraph for next-basket recommendation.

Neural networks : the official journal of the International Neural Network Society
Next basket recommendation (NBR) is an essential task within the realm of recommendation systems and is dedicated to the anticipation of user preferences in the next moment based on the analysis of users' historical sequences of engaged baskets. Curr...

Explainable exercise recommendation with knowledge graph.

Neural networks : the official journal of the International Neural Network Society
Recommending suitable exercises and providing the reasons for these recommendations is a highly valuable task, as it can significantly improve students' learning efficiency. Nevertheless, the extensive range of exercise resources and the diverse lear...

M4Net: Multi-level multi-patch multi-receptive multi-dimensional attention network for infrared small target detection.

Neural networks : the official journal of the International Neural Network Society
The detection of infrared small targets is getting more and more attention, and has a wider application in both military and civilian fields. The traditional infrared small target detection methods heavily rely on the setting of manual features, and ...

An extrapolation-driven network architecture for physics-informed deep learning.

Neural networks : the official journal of the International Neural Network Society
Current physics-informed neural network (PINN) implementations with sequential learning strategies often experience some weaknesses, such as the failure to reproduce the previous training results when using a single network, the difficulty to strictl...

Investigating self-supervised image denoising with denaturation.

Neural networks : the official journal of the International Neural Network Society
Self-supervised learning for image denoising problems in the presence of denaturation for noisy data is a crucial approach in machine learning. However, theoretical understanding of the performance of the approach that uses denatured data is lacking....