https://www.kaggle.com/c/trackml-particle-identification/overview. CERN. TrackML throughput phase. https://competitions.codalab.org/competitions/20112. Farrell, S. et al. Novel deep learning methods for track reconstruction. Preprint at https://doi.org/10.48550/arXiv.1810.06111 (2018). Ju, X...
这种注意力机制能够自适应地调整邻居节点的重要性,使得GAT在处理复杂的图结构时更加灵活。 GGNN(Gated Graph Neural Network):在更新步骤中采用门控循环单元,使得节点特征能够有效地捕捉序列信息。GGNN 在所有节点上多次执行递归函数,当应用于大型图时可能会面临可扩展性问题。  to a Kaggledataseton healthcare provider fraud. (This dataset is currently licensed CC0: Public Domain on Kaggle. Please note that this dataset might not be accurate, and it’s ...
road network longitude range(道路网络经度范围):每个城市道路网络的经度范围。 Porto的经度范围是[-8.644531, -8.596830] NanJing的经度范围是[118.69454, 118.84454] YanCheng的经度范围是[120.1070088, 120.3560447] road segments(道路段数量):每个城市的数据集中包含的道路段数量。
算法模型组:积极组队参加kaggle等比赛,原创手把手教系列文章; 调研分析组:通过专访等方式调研大数据的应用,探索数据产品之美; 系统平台组:追踪大数据&人工智能系统平台技术前沿,对话专家; 自然语言处理组:重于实践,积极参加比赛及策划各类文本分析项目; 制造业大数据组:秉工业强国之梦,产学研政结合,挖掘数据价值; ...
This paper introduces a novel approach utilizing the Graph Neural Network (GNN) to enhance demand prediction accuracy by leveraging the spatial relationships inherent in online sales data, named SGNN. Drawing from the rich dataset provided in the fourth Kaggle competition, we construct a ...
The majority of data scientists and machine learning practitioners use relational data in their work [State of ML and Data Science 2017, Kaggle, Inc.]. But training machine learning models on data stored in relational databases requires significant data extraction and feature engineering efforts. Thes...
机器之心实测经过一些简单的超参数调整(如增加 epoch),几乎能达到与论文中一样的准确率,感兴趣的读者可自行测试一番。 参考连接: https://www.kaggle.com/kmader/mnist-graph-deep-learning https://zhuanlan.zhihu.com/p/78452993
Reddit https://www.kaggle.com/colemaclean/subreddit-interactions The processed data can be downloaded: https://www.dropbox.com/sh/hwx2347ir1worag/AABJK6IBXHNBlbvrvKqw94YKa?dl=0 Usage Generate data You need to run the file record.py first to preprocess the data to generate the tf.record...