Neural networks : the official journal of the International Neural Network Society
39423500
Graph Neural Networks (GNNs) are playing an increasingly vital role in the field of recommender systems. To improve knowledge perception within GNNs, contrastive learning has been applied and has proven to be highly effective. GNNs have the ability t...
Neural networks : the official journal of the International Neural Network Society
39626533
Multi-hop path completion is a key part of temporal knowledge graph completion, which aims to infer complex relationships and obtain interpretable completion results. However, the traditional multi-hop path completion models mainly focus on the stati...
The advent of the Transformer has significantly altered the course of research in Natural Language Processing (NLP) within the domain of deep learning, making Transformer-based studies the mainstream in subsequent NLP research. There has also been co...
We address an open problem in the philosophy of artificial intelligence (AI): how to justify the epistemic attitudes we have towards the trustworthiness of AI systems. The problem is important, as providing reasons to believe that AI systems are wort...
Neural networks : the official journal of the International Neural Network Society
39667214
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...
Neural networks : the official journal of the International Neural Network Society
39855005
Entity alignment (EA) is a typical strategy for knowledge graph integration, aiming to identify and align different entity pairs representing the same real object from different knowledge graphs. Temporal Knowledge Graph (TKG) extends the static know...
Neural networks : the official journal of the International Neural Network Society
39855003
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...
Humans and animals have a striking ability to learn relationships between items in experience (such as stimuli, objects and events), enabling structured generalization and rapid assimilation of new information. A fundamental type of such relational l...