Neural networks : the official journal of the International Neural Network Society
Apr 4, 2025
Semi-supervised federated learning (SSFL) has emerged as a promising paradigm to reduce the need for fully labeled data in training federated learning (FL) models. This paper focuses on the label-at-server scenario, where clients' data are entirely u...
Neural networks : the official journal of the International Neural Network Society
Apr 1, 2025
As an efficient model compression method, recent knowledge distillation methods primarily transfer the knowledge from a large teacher model to a small student model by minimizing the differences between the predictions from teacher and student. Howev...
Proceedings of the National Academy of Sciences of the United States of America
Mar 26, 2025
AI systems have attained superhuman performance across various domains. If the hidden knowledge encoded in these highly capable systems can be leveraged, human knowledge and performance can be advanced. Yet, this internal knowledge is difficult to ex...
Medicine, health care, and philosophy
Mar 15, 2025
It has been difficult historically for physicians, patients, and philosophers alike to quantify pain given that pain is commonly understood as an individual and subjective experience. The process of measuring and diagnosing pain is often a fraught an...
BMC psychology
Mar 6, 2025
BACKGROUND: The integration of Artificial Intelligence (AI) into daily life raises significant challenges and uncertainties, notably concerning job security and skill relevance. This has led to the emergence of 'AI anxiety'-a stress response to poten...
Theoretical medicine and bioethics
Feb 26, 2025
Predictions that artificial intelligence (AI) will become capable of replacing human beings in domains such as medicine rest implicitly on a theory of mind according to which knowledge can be captured propositionally without loss of meaning. Generati...
Neural networks : the official journal of the International Neural Network Society
Feb 14, 2025
To address the incompleteness of knowledge graphs, multi-hop reasoning aims to find the unknown information from existing data and enhance the comprehensive understanding. The presence of reasoning paths endows multi-hop reasoning with interpretabili...
Neural networks : the official journal of the International Neural Network Society
Jan 23, 2025
Contrastive learning has gained dominance in sequential recommendation due to its ability to derive self-supervised signals for addressing data sparsity problems. However, caused by random augmentations (e.g., crop, mask, and reorder), existing metho...
Neural networks : the official journal of the International Neural Network Society
Jan 21, 2025
Extrapolation reasoning in temporal knowledge graphs (TKGs) aims at predicting future facts based on historical data, and finds extensive application in diverse real-world scenarios. Existing TKG reasoning methods primarily focus on capturing the fac...
Neural networks : the official journal of the International Neural Network Society
Jan 18, 2025
Vision-language models are pre-trained by aligning image-text pairs in a common space to deal with open-set visual concepts. Recent works adopt fixed or learnable prompts, i.e., classification weights are synthesized from natural language description...