AI Medical Compendium

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FedPD: Defending federated prototype learning against backdoor attacks.

Neural networks : the official journal of the International Neural Network Society
Federated Learning (FL) is an efficient, distributed machine learning paradigm that enables multiple clients to jointly train high-performance deep learning models while maintaining training data locally. However, due to its distributed computing nat...

Robust long-tailed recognition with distribution-aware adversarial example generation.

Neural networks : the official journal of the International Neural Network Society
Confronting adversarial attacks and data imbalances, attaining adversarial robustness under long-tailed distribution presents a challenging problem. Adversarial training (AT) is a conventional solution for enhancing adversarial robustness, which gene...

Output sampling synchronization and state estimation in flux-charge domain memristive neural networks with leakage and time-varying delays.

Neural networks : the official journal of the International Neural Network Society
This paper theoretically explores the coexistence of synchronization and state estimation analysis through output sampling measures for a class of memristive neural networks operating within the flux-charge domain. These networks are subject to const...

Adaptive expert fusion model for online wind power prediction.

Neural networks : the official journal of the International Neural Network Society
Wind power prediction is a challenging task due to the high variability and uncertainty of wind generation and weather conditions. Accurate and timely wind power prediction is essential for optimal power system operation and planning. In this paper, ...

A simple remedy for failure modes in physics informed neural networks.

Neural networks : the official journal of the International Neural Network Society
Physics-informed neural networks (PINNs) have shown promising results in solving a wide range of problems involving partial differential equations (PDEs). Nevertheless, there are several instances of the failure of PINNs when PDEs become more complex...

Counterfactual learning for higher-order relation prediction in heterogeneous information networks.

Neural networks : the official journal of the International Neural Network Society
Heterogeneous Information Networks (HINs) play a crucial role in modeling complex social systems, where predicting missing links/relations is a significant task. Existing methods primarily focus on pairwise relations, but real-world scenarios often i...

A multi-memory-augmented network with a curvy metric method for video anomaly detection.

Neural networks : the official journal of the International Neural Network Society
Anomaly detection task in video mainly refers to identifying anomalous events that do not conform to the learned normal patterns in the inferring phase. However, the Euclidean metric used in the learning and inferring phase by the most of the existin...

FedMEKT: Distillation-based embedding knowledge transfer for multimodal federated learning.

Neural networks : the official journal of the International Neural Network Society
Federated learning (FL) enables a decentralized machine learning paradigm for multiple clients to collaboratively train a generalized global model without sharing their private data. Most existing works have focused on designing FL systems for unimod...

OperaGAN: A simultaneous transfer network for opera makeup and complex headwear.

Neural networks : the official journal of the International Neural Network Society
Standard makeup transfer techniques mainly focus on facial makeup. The texture details of headwear in style examples tend to be ignored. When dealing with complex portrait style transfer, simultaneous correct headwear and facial makeup transfer often...

Self-distillation improves self-supervised learning for DNA sequence inference.

Neural networks : the official journal of the International Neural Network Society
Self-supervised Learning (SSL) has been recognized as a method to enhance prediction accuracy in various downstream tasks. However, its efficacy for DNA sequences remains somewhat constrained. This limitation stems primarily from the fact that most e...