One Venue, Two Conferences: The Separation of Chinese and American Citation Networks

@article{Zhao2022OneVT,
  title={One Venue, Two Conferences: The Separation of Chinese and American Citation Networks},
  author={Bingchen Zhao and Yuling Gu and Jessica Zosa Forde and Naomi Saphra},
  journal={ArXiv},
  year={2022},
  volume={abs/2211.12424}
}
At NeurIPS, American and Chinese institutions cite papers from each other’s regions substan-tially less than they cite endogamously. We build a citation graph to quantify this divide, compare it to European connectivity, and discuss the causes and consequences of the separation. 
[PDF] Semantic Reader

Figures from this paper

References

SHOWING 1-10 OF 20 REFERENCES

Geographic Citation Gaps in NLP Research

In a fair world, people have equitable opportunities to education, to conduct scientific research, to publish, and to get credit for their work, regardless of where they live. However, it is common…

The Chinese approach to artificial intelligence: an analysis of policy, ethics, and regulation

The main strategic areas in which China is investing in AI and the concurrent ethical debates that are delimiting its use are analyzed, to provide a more comprehensive and critical understanding of China’s AI policy.

TransReID: Transformer-based Object Re-Identification

This work first encode an image as a sequence of patches and build a transformer-based strong baseline with a few critical improvements, which achieves competitive results on several ReID benchmarks with CNN-based methods.

Scalable Person Re-identification: A Benchmark

A minor contribution, inspired by recent advances in large-scale image search, an unsupervised Bag-of-Words descriptor is proposed that yields competitive accuracy on VIPeR, CUHK03, and Market-1501 datasets, and is scalable on the large- scale 500k dataset.

PANDA: A Gigapixel-Level Human-Centric Video Dataset

  • Xueyan WangXiya Zhang Lu Fang
  • Environmental Science
    2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2020
PANDA is presented, the first gigaPixel-level humAN-centric viDeo dAtaset, for large-scale, long-term, and multi-object visual analysis, and a ‘global-to-local zoom-in’ framework, where global trajectories and local interactions are simultaneously encoded, yielding promising results.

PCANet: A Simple Deep Learning Baseline for Image Classification?

Surprisingly, for all tasks, such a seemingly naive PCANet model is on par with the state-of-the-art features either prefixed, highly hand-crafted, or carefully learned [by deep neural networks (DNNs)].

Large-Scale Pre-training for Person Re-identification with Noisy Labels

This paper develops a large-scale Pre-training frame-work utilizing Noisy Labels (PNL), which consists of three learning modules: supervised Re-ID learning, prototype-based contrastive learning, and label-guided Contrastive learning.

Exploit the Unknown Gradually: One-Shot Video-Based Person Re-identification by Stepwise Learning

This paper proposes an approach to exploiting unlabeled tracklets by gradually but steadily improving the discriminative capability of the Convolutional Neural Network feature representation via stepwise learning.

Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in Vitro

A simple semisupervised pipeline that only uses the original training set without collecting extra data, which effectively improves the discriminative ability of learned CNN embeddings and proposes the label smoothing regularization for outliers (LSRO).

Performance Measures and a Data Set for Multi-target, Multi-camera Tracking

A new pair of precision-recall measures of performance that treats errors of all types uniformly and emphasizes correct identification over sources of error are presented to help accelerate progress in multi-target, multi-camera tracking systems.