Readings

1. Spatio-temporal Data Mining

1.0 Survey

• Atluri, Gowtham, Anuj Karpatne, and Vipin Kumar.. "Spatio-Temporal Data Mining: A Survey of Problems and Methods." arXiv preprint arXiv:1711.04710 (2017) [pdf]

1.1 Road Network and services prediction

[2017]

• Dong Wang, Wei Cao, Jian Li, Jieping Ye. "DeepSD: Supply-Demand Prediction for Online Car-hailing Services using Deep Neural Networks" ICDE, 2017 [pdf]

• Tong, Yongxin, et al. "The simpler the better: a unified approach to predicting original taxi demands based on large-scale online platforms." Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2017. [pdf]

• Zhang, Lingyu, et al. "A Taxi Order Dispatch Model based On Combinatorial Optimization." Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2017. [link]

• Bao, Jie, et al. "Planning Bike Lanes based on Sharing-Bikes' Trajectories." Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2017. [pdf]

[2016]

• Xu, Mengwen, Dong Wang, and Jian Li. "DESTPRE: a data-driven approach to destination prediction for taxi rides." Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 2016.[pdf]

• Xu, Mengwen, et al. "Demand driven store site selection via multiple spatial-temporal data." Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, 2016. [pdf]

[2015]

• Moretti, Fabio, et al. "Urban traffic flow forecasting through statistical and neural network bagging ensemble hybrid modeling." Neurocomputing 167 (2015): 3-7. [pdf]

• Lv, Yisheng, et al. "Traffic flow prediction with big data: a deep learning approach." IEEE Transactions on Intelligent Transportation Systems 16.2 (2015): 865-873. [pdf]

[2000-2010]

• Sun, Shiliang, Changshui Zhang, and Guoqiang Yu. "A Bayesian network approach to traffic flow forecasting." IEEE Transactions on intelligent transportation systems 7.1 (2006): 124-132. [pdf]

• Sun, Shiliang, Changshui Zhang, and Yi Zhang. "Traffic flow forecasting using a spatio-temporal bayesian network predictor." International Conference on Artificial Neural Networks. Springer, Berlin, Heidelberg, 2005. [pdf]

2. AI and Neural Network Papers

2.1 RNN / Sequence-to-Sequence in Deep Learning

• Graves, Alex. "Generating sequences with recurrent neural networks." arXiv preprint arXiv:1308.0850 (2013). [pdf] (LSTM, very nice generating result, show the power of RNN) ⭐⭐⭐⭐

• Cho, Kyunghyun, et al. "Learning phrase representations using RNN encoder-decoder for statistical machine translation." arXiv preprint arXiv:1406.1078 (2014). [pdf] (First Seq-to-Seq Paper) ⭐⭐⭐⭐

• Sutskever, Ilya, Oriol Vinyals, and Quoc V. Le. "Sequence to sequence learning with neural networks." Advances in neural information processing systems. 2014. [pdf] (Outstanding Work) ⭐⭐⭐⭐⭐

• Bahdanau, Dzmitry, KyungHyun Cho, and Yoshua Bengio. "Neural Machine Translation by Jointly Learning to Align and Translate." arXiv preprint arXiv:1409.0473 (2014). [pdf] ⭐⭐⭐⭐

• Vinyals, Oriol, and Quoc Le. "A neural conversational model." arXiv preprint arXiv:1506.05869 (2015). [pdf] (Seq-to-Seq on Chatbot) ⭐⭐⭐

2.2 Semantic Neural Network

• Wu, Ledell, et al. "StarSpace: Embed All The Things!." arXiv preprint arXiv:1709.03856 (2017). [pdf]

• Dieng, Adji B., et al. "Topicrnn: A recurrent neural network with long-range semantic dependency. " arXiv preprint arXiv:1611.01702 (2016). [pdf]

• Bahdanau, Dzmitry, Kyunghyun Cho, and Yoshua Bengio. "Neural machine translation by jointly learning to align and translate." arXiv preprint arXiv:1409.0473 (2014). [pdf]

2.3 Papers worth reading

• Graves, Alex, Greg Wayne, and Ivo Danihelka. "Neural turing machines." arXiv preprint arXiv:1410.5401 (2014). [pdf]

• Kiela, Douwe, and Léon Bottou. "Learning Image Embeddings using Convolutional Neural Networks for Improved Multi-Modal Semantics." EMNLP. 2014. [pdf]

2.4 Image and Generrative Model related papers

• He, Kaiming, et al. "Mask r-cnn." arXiv preprint arXiv:1703.06870 (2017). [pdf]
• Yang, Jianwei, et al. "LR-GAN: Layered recursive generative adversarial networks for image generation." arXiv preprint arXiv:1703.01560 (2017). [pdf]
• Fei-Fei, Li, Rob Fergus, and Pietro Perona. "Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories." Computer vision and Image understanding 106.1 (2007): 59-70. [pdf]
• Hoshen, Yedid. "Vain: Attentional multi-agent predictive modeling." Advances in Neural Information Processing Systems. 2017. [pdf]

3. Event Detection

• F Atefeh, W Khreich, "A survey of techniques for event detection in twitter" Computational Intelligence, Wiley Online Library, 2015 [pdf]

Generic Papers

• Keogh, Eamonn, et al. "An online algorithm for segmenting time series." Data Mining, 2001. ICDM 2001, Proceedings IEEE International Conference on. IEEE, 2001. [pdf]

Books

1. Deep Learning Book & Survey

• Bengio, Yoshua, Ian J. Goodfellow, and Aaron Courville. "Deep learning." An MIT Press book. (2015). [pdf] (Deep Learning Bible, you can read this book while reading following papers.) ⭐⭐⭐⭐⭐

• LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." Nature 521.7553 (2015): 436-444. [pdf] (Three Giants' Survey) ⭐⭐⭐⭐⭐