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Researcher profile

Fang Wang

6 papers in the PaperMetrix corpus

Publications

Papers by this author

  1. Research on Personalized Learning Mode Based on Network Learning Space

    2022

    In order to improve the efficiency of teaching and learning, in view of the disadvantages of low learning efficiency of learners under the traditional teaching mode, it has become an inevitable trend to explore a …

  2. News classifications based on CBA-PreambleCNN Model

    2022

    In order to solve the problems of insufficient information extraction and poor classification effect of a single deep learning model, this paper proposes a hybrid multi-neural network CBOW-BiLSTM-Attention-PreambleCNN model(The CBA-PreambleCNN for short, PreambleCNN is the …

  3. Distributed Graph Embedding with Information-Oriented Random Walks

    2023 · Proceedings of the VLDB Endowment

    Graph embedding maps graph nodes to low-dimensional vectors, and is widely adopted in machine learning tasks. The increasing availability of billion-edge graphs underscores the importance of learning efficient and effective embeddings on large graphs, such …

  4. Scientific AI systems require domain-aware robustness validation

    2026 · Cell Reports Physical Science

    AI has become an essential tool in many areas of scientific discovery. However, its robustness, particularly in high-stakes scientific applications, remains insufficiently understood. While adversarial vulnerabilities have been widely studied in image recognition, their implications …

  5. TinyBERT: Distilling BERT for Natural Language Understanding

    2019 · arXiv (Cornell University)

    Language model pre-training, such as BERT, has significantly improved the performances of many natural language processing tasks. However, pre-trained language models are usually computationally expensive, so it is difficult to efficiently execute them on resource-restricted …

  6. TinyBERT: Distilling BERT for Natural Language Understanding

    2020

    Language model pre-training, such as BERT, has significantly improved the performances of many natural language processing tasks. However, pre-trained language models are usually computationally expensive, so it is difficult to efficiently execute them on resourcerestricted …