Lots of learning tasks require dealing with graph data which contains rich relation information among elements. Modeling physics systems, learning molecular fingerprints, predicting protein interface, and classifying diseases demand a model to learn from graph inputs. In other domains such as learning from non-structural data like texts and images, reasoning on extracted structures (like the…
AI Open Template
Write in a clean editor, then format for AI Open in one click — DocuGuru applies the official Elsevier template with numbered references and exports a submission-ready PDF plus the editable LaTeX source. Free to start.
About the AI Open format
AI Open is a peer-reviewed journal published by Elsevier, covering Topic Modeling, Natural Language Processing Techniques, Advanced Graph Neural Networks.
| Publisher | Elsevier |
|---|---|
| Reference style | Numbered (Elsevier) Numbered — [1], [2] in the text [1] A. Smith, B. Jones, C. Lee, A representative article title, AI Open 12 (2023) 45–58.
Formats any DOI in AI Open style. No sign-up. |
| Publishes research in | Topic Modeling Natural Language Processing Techniques Advanced Graph Neural Networks Multimodal Machine Learning Applications Domain Adaptation and Few-Shot Learning |
| ISSN | 2666-6510 |
| Citation impact (2-yr) | 6.43 |
| h-index | 29 |
| i10-index | 48 |
| Total citations | 9,876 |
| Article processing charge | $800 |
| Open access | Yes |
| Top institutions publishing here | Tsinghua University |
| Journal website | www.sciencedirect.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Papers published in AI Open per year
Citation impact of AI Open by publication year
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Most-cited papers in AI Open
Large-scale pre-trained models (PTMs) such as BERT and GPT have recently achieved great success and become a milestone in the field of artificial intelligence (AI). Owing to sophisticated pre-training objectives and huge model parameters, large-scale PTMs can effectively capture knowledge from massive labeled and unlabeled data. By storing knowledge into huge parameters and fine-tuning on…
Recently, prompt tuning has been widely applied to stimulate the rich knowledge in pre-trained language models (PLMs) to serve NLP tasks. Although prompt tuning has achieved promising results on some few-class classification tasks, such as sentiment classification and natural language inference, manually designing prompts is cumbersome. Meanwhile, generating prompts automatically is also difficult and time-consuming.…
As an effective strategy, data augmentation (DA) alleviates data scarcity scenarios where deep learning techniques may fail. It is widely applied in computer vision then introduced to natural language processing and achieves improvements in many tasks. One of the main focuses of the DA methods is to improve the diversity of training data, thereby helping…
Legal artificial intelligence (LegalAI) aims to benefit legal systems with the technology of artificial intelligence, especially natural language processing (NLP). Recently, inspired by the success of pre-trained language models (PLMs) in the generic domain, many LegalAI researchers devote their effort to applying PLMs to legal tasks. However, utilizing PLMs to address legal tasks is still…