AI Medical Compendium

Explore the latest research on artificial intelligence and machine learning in medicine.

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Conditional diffusion model for recommender systems.

Neural networks : the official journal of the International Neural Network Society
Recommender systems are used to filter personalized information for users, as it help avoid information overload. The diffusion model is an advanced deep generative model that has been used in recommender systems due to its effectiveness in reconstru...

Robust graph structure learning under heterophily.

Neural networks : the official journal of the International Neural Network Society
A graph is a fundamental mathematical structure in characterizing relations between different objects and has been widely used on various learning tasks. Most methods implicitly assume a given graph to be accurate and complete. However, real data is ...

U3UNet: An accurate and reliable segmentation model for forest fire monitoring based on UAV vision.

Neural networks : the official journal of the International Neural Network Society
Forest fires pose a serious threat to the global ecological environment, and the critical steps in reducing the impact of fires are fire warning and real-time monitoring. Traditional monitoring methods, like ground observation and satellite sensing, ...

Deterministic Autoencoder using Wasserstein loss for tabular data generation.

Neural networks : the official journal of the International Neural Network Society
Tabular data generation is a complex task due to its distinctive characteristics and inherent complexities. While Variational Autoencoders have been adapted from the computer vision domain for tabular data synthesis, their reliance on non-determinist...

TDAG: A multi-agent framework based on dynamic Task Decomposition and Agent Generation.

Neural networks : the official journal of the International Neural Network Society
The emergence of Large Language Models (LLMs) like ChatGPT has inspired the development of LLM-based agents capable of addressing complex, real-world tasks. However, these agents often struggle during task execution due to methodological constraints,...

Deep one-class probability learning for end-to-end image classification.

Neural networks : the official journal of the International Neural Network Society
One-class learning has many application potentials in novelty, anomaly, and outlier detection systems. It aims to distinguish both positive and negative samples with a model trained via only positive samples or one-class annotated samples. With the d...

CreINNs: Credal-Set Interval Neural Networks for Uncertainty Estimation in Classification Tasks.

Neural networks : the official journal of the International Neural Network Society
Effective uncertainty estimation is becoming increasingly attractive for enhancing the reliability of neural networks. This work presents a novel approach, termed Credal-Set Interval Neural Networks (CreINNs), for classification. CreINNs retain the f...

Federated learning with bilateral defense via blockchain.

Neural networks : the official journal of the International Neural Network Society
Federated Learning (FL) offers benefits in protecting client data privacy but also faces multiple security challenges, such as privacy breaches from unencrypted data transmission and poisoning attacks that compromise model performance, however, most ...

Dissecting the effectiveness of deep features as metric of perceptual image quality.

Neural networks : the official journal of the International Neural Network Society
There is an open debate on the role of artificial networks to understand the visual brain. Internal representations of images in artificial networks develop human-like properties. In particular, evaluating distortions using differences between intern...

Low-Rank Representation with Empirical Kernel Space Embedding of Manifolds.

Neural networks : the official journal of the International Neural Network Society
Low-Rank Representation (LRR) methods integrate low-rank constraints and projection operators to model the mapping from the sample space to low-dimensional manifolds. Nonetheless, existing approaches typically apply Euclidean algorithms directly to m...