{"type":"ScholarlyArticle","title":"Enhancing Urban Flow Maps via Neural ODEs","authors":[{"name":"Fan Zhou","slug":"fan-zhou","given":"Fan","family":"Zhou","affiliation":"School of Information and Software Engineering , University of Electronic Science and Technology of China"},{"name":"Liang Li","slug":"liang-li","given":"Liang","family":"Li","affiliation":"School of Information and Software Engineering , University of Electronic Science and Technology of China"},{"name":"Ting Zhong","slug":"ting-zhong","given":"Ting","family":"Zhong","affiliation":"School of Information and Software Engineering , University of Electronic Science and Technology of China"},{"name":"Goce Trajcevski","slug":"goce-trajcevski","given":"Goce","family":"Trajcevski","affiliation":"Iowa State University , Ames IA"},{"name":"Kunpeng Zhang","slug":"kunpeng-zhang","given":"Kunpeng","family":"Zhang","affiliation":"University of Maryland , College Park MD"},{"name":"Jiahao Wang","slug":"jiahao-wang","given":"Jiahao","family":"Wang","affiliation":"School of Information and Software Engineering , University of Electronic Science and Technology of China"}],"abstract":"Flow super-resolution (FSR) enables inferring fine-grained urban flows with coarse-grained observations and plays an important role in traffic monitoring and prediction. The existing FSR solutions rely on deep CNN models (e.g., ResNet) for learning spatial correlation, incurring excessive memory cost and numerous parameter updates. We propose to tackle the urban flows inference using dynamic systems paradigm and present a new method FODE -- FSR with Ordinary Differential Equations (ODEs). FODE extends neural ODEs by introducing an affine coupling layer to overcome the problem of numerically unstable gradient computation, which allows more accurate and efficient spatial correlation estimation, without extra memory cost. In addition, FODE provides a flexible balance between flow inference accuracy and computational efficiency. A FODE-based augmented normalization mechanism is further introduced to constrain the flow distribution with the influence of external factors. Experimental evaluations on two real-world datasets demonstrate that FODE significantly outperforms several baseline approaches.","doi":"10.24963/ijcai.2020/180","url":"https://ijcai.org/proceedings/2020/180","pdf":"https://ijcai.org/proceedings/2020/0180.pdf","markdown":"https://ijcai.org/proceedings/2020/180.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/180","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Data Mining","track":"Main track","number":180,"pages":{"first":1295,"last":1302},"keywords":["Data Mining: Mining Spatial, Temporal Data","Machine Learning Applications: Applications of Supervised Learning","Data Mining: Feature Extraction, Selection and Dimensionality Reduction"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24256/file_24256.m3u8","length_sec":294,"provider":"hls"}],"references":[{"position":1,"raw":"[Ascher et al., 1997] Uri M Ascher, Steven J Ruuth, and Raymond J Spiteri. Implicit-explicit runge-kutta methods for time-dependent partial differential equations. Applied Numerical Mathematics, 25(2-3):151-167, 1997.","title":"Implicit-explicit runge-kutta methods for time-dependent partial differential equations","authors":"Uri M Ascher, Steven J Ruuth, Raymond J Spiteri","year":"1997","venue":"Applied Numerical Mathematics","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Ba et al., 2016] Jimmy Lei Ba, Jamie Ryan Kiros, and Ge- offrey E Hinton. Layer normalization. arXiv: 1607.06450, 2016.","title":"Layer normalization","authors":"Jimmy Lei Ba, Jamie Ryan Kiros, Geoffrey E Hinton","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Butcher and Wanner, 1996] John Charles Butcher and Ger- hard Wanner. Runge-kutta methods: some historical notes. Applied Numerical Mathematics, 22(1-3):113-151, 1996.","title":"Runge-kutta methods: some historical notes","authors":"Wanner Butcher","year":"1996","venue":"Applied Numerical Mathematics","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Cai et al., 2019] Jianrui Cai, Shuhang Gu, Radu Timofte, Lei Zhang, and et al. Ntire 2019 challenge on real image super-resolution -methods and results. In CVPR Chal- lenge, pages 2211-2223, 2019.","title":"Ntire 2019 challenge on real image super-resolution -methods and results","authors":"Cai","year":"2019","venue":"CVPR Challenge","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Chen et al., 2018] Tian Qi Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud. Neural ordinary dif- ferential equations. In NeuIPS, pages 6572-6583, 2018.","title":"Neural ordinary differential equations","authors":"Tian Qi, Chen, Yulia Rubanova, Jesse Bettencourt, David K Duvenaud","year":"2018","venue":"NeuIPS","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[De Brouwer et al., 2019] Edward De Brouwer, Jaak Simm, Adam Arany, and Yves Moreau. Gru-ode-bayes -contin- uous modeling of sporadically-observed time series. In NeurIPS, pages 7377-7388, 2019.","title":"Gru-ode-bayes -continuous modeling of sporadically-observed time series","authors":"Edward De Brouwer, Jaak Simm, Adam Arany, Yves Moreau","year":"2019","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Dinh et al., 2015] Laurent Dinh, David Krueger, and Yoshua Bengio. NICE: non-linear independent compo- nents estimation. In ICLR, 2015.","title":"NICE: non-linear independent components estimation","authors":"Laurent Dinh, David Krueger, Yoshua Bengio","year":"2015","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Dinh et al., 2017] Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio. Density estimation using real NVP. In ICLR, 2017.","title":"Density estimation using real NVP","authors":"Laurent Dinh, Jascha Sohl-Dickstein, Samy Bengio","year":"2017","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Dong et al., 2015] Chao Dong, Chen Change Loy, Kaiming He, and Xiaoou Tang. Image super-resolution using deep convolutional networks. TPAMI, 38(2):295-307, 2015.","title":"Image super-resolution using deep convolutional networks","authors":"Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang","year":"2015","venue":"TPAMI","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[E, 2017] Weinan E. A proposal on machine learning via dynamical systems. Communications in Mathematics and Statistics, 5(1):1-11, 2017.","title":"A proposal on machine learning via dynamical systems","authors":"E Weinan","year":"2017","venue":"Communications in Mathematics and Statistics","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Gholami et al., 2019] Amir Gholami, Kurt Keutzer, and George Biros. Anode: Unconditionally accurate memory- efficient gradients for neural odes. In IJCAI, pages 730- 736, 2019.","title":"Anode: Unconditionally accurate memoryefficient gradients for neural odes","authors":"Amir Gholami, Kurt Keutzer, George Biros","year":"2019","venue":"IJCAI","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Grathwohl et al., 2019] Will Grathwohl, Ricky T. Q. Chen, Jesse Bettencourt, Ilya Sutskever, and David Duvenaud. FFJORD: free-form continuous dynamics for scalable re- versible generative models. In ICLR, 2019.","title":"FFJORD: free-form continuous dynamics for scalable reversible generative models","authors":"Grathwohl","year":"2019","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[He et al., 2016] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recog- nition. In CVPR, pages 770-778, 2016.","title":"Deep residual learning for image recognition","authors":"Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun","year":"2016","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[He et al., 2019] Xiangyu He, Zitao Mo, Peisong Wang, Yang Liu, Mingyuan Yang, and Jian Cheng. Ode-inspired network design for single image super-resolution. In CVPR, pages 1732-1741, 2019.","title":"Ode-inspired network design for single image super-resolution","authors":"Xiangyu He, Zitao Mo, Peisong Wang, Yang Liu, Mingyuan Yang, Jian Cheng","year":"2019","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Heinonen and Lähdesmäki, 2019] Markus Heinonen and Harri Lähdesmäki. ODE 2 VAE: Deep Generative Second Order ODEs with Bayesian Neural Networks. In NeurIPS, pages 13412-13421, 2019.","title":"ODE 2 VAE: Deep Generative Second Order ODEs with Bayesian Neural Networks","authors":"Lähdesmäki Heinonen, Markus Heinonen, Harri Lähdesmäki","year":"2019","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Kim et al., 2016] Jiwon Kim, Jung Kwon Lee, and Kyoung Mu Lee. Accurate image super-resolution using very deep convolutional networks. In CVPR, pages 1646-1654, 2016.","title":"Accurate image super-resolution using very deep convolutional networks","authors":"Jiwon Kim, Jung Kwon Lee, Kyoung Mu, Lee","year":"2016","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Kingma and Ba, 2014] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv: 1412.6980, 2014.","title":"Adam: A method for stochastic optimization","authors":"P Diederik, Jimmy Kingma, Ba","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Kingma and Dhariwal, 2018] Durk P Kingma and Prafulla Dhariwal. Glow: Generative flow with invertible 1x1 con- volutions. In NIPS, pages 10236-10245, 2018.","title":"Glow: Generative flow with invertible 1x1 convolutions","authors":"Dhariwal ; Kingma, P Durk, Prafulla Kingma, Dhariwal","year":"2018","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Ledig et al., 2017] Christian Ledig, Lucas Theis, Ferenc Huszár, Caballero, and et al. Photo-realistic single image super-resolution using a generative adversarial network. In CVPR, pages 105-114, 2017.","title":"Photo-realistic single image super-resolution using a generative adversarial network","authors":"Ledig","year":"2017","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Liang et al., 2019] Yuxuan Liang, Kun Ouyang, Lin Jing, Sijie Ruan, Ye Liu, Junbo Zhang, David S Rosenblum, and Yu Zheng. Urbanfm: Inferring fine-grained urban flows. In KDD, pages 3132-3142, 2019.","title":"Urbanfm: Inferring fine-grained urban flows","authors":"Yuxuan Liang, Kun Ouyang, Lin Jing, Sijie Ruan, Ye Liu, Junbo Zhang, David S Rosenblum, Yu Zheng","year":"2019","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Liu et al., 2019a] Ning Liu, Rui Ma, Yue Wang, and Lin Zhang. Inferring fine-grained air pollution map via a spa- tiotemporal super-resolution scheme. In UbiqComp, pages 498-504, 2019.","title":"Inferring fine-grained air pollution map via a spatiotemporal super-resolution scheme","authors":"Liu","year":"2019","venue":"UbiqComp","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Liu et al., 2019b] Xuanqing Liu, Tesi Xiao, Si Si, Qin Cao, Sanjiv Kumar, and Cho-Jui Hsieh. Neural SDE: Stabi- lizing neural ode networks with stochastic noise. arXiv: 1906.02355v1, 2019.","title":"Neural SDE: Stabilizing neural ode networks with stochastic noise","authors":"Liu","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Lu et al., 2018] Yiping Lu, Aoxiao Zhong, Quanzheng Li, and Bin Dong. Beyond finite layer neural networks: Bridg- ing deep architectures and numerical differential equa- tions. In ICML, pages 3282-3291, 2018.","title":"Beyond finite layer neural networks: Bridging deep architectures and numerical differential equations","authors":"Yiping Lu, Aoxiao Zhong, Quanzheng Li, Bin Dong","year":"2018","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Poli et al., 2019] Michael Poli, Stefano Massaroli, Jun- young Park, Atsushi Yamashita, Hajime Asama, and Jinkyoo Park. Graph neural ordinary differential equa- tions. arXiv: 1911.07532v1, 2019.","title":"Graph neural ordinary differential equations","authors":"Michael Poli, Stefano Massaroli, Junyoung Park, Atsushi Yamashita, Hajime Asama, Jinkyoo Park","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Rubanova et al., 2019] Yulia Rubanova, Ricky T.Q. Chen, and David Duvenaud. Latent ordinary differential equa- tions for irregularly-sampled time series. In NeurIPS, pages 5321-5331, 2019.","title":"Latent ordinary differential equations for irregularly-sampled time series","authors":"Yulia Rubanova, Ricky T Q Chen, David Duvenaud","year":"2019","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Shi et al., 2016] Wenzhe Shi, Jose Caballero, Ferenc Huszár, Johannes Totz, Andrew P Aitken, Rob Bishop, Daniel Rueckert, and Zehan Wang. Real-time single im- age and video super-resolution using an efficient sub-pixel convolutional neural network. In CVPR, pages 1874- 1883, 2016.","title":"Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network","authors":"Shi","year":"2016","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Wang et al., 2019] Zhihao Wang, Jian Chen, and Steven CH Hoi. Deep learning for image super-resolution: A survey. arXiv:1902.06068, 2019.","title":"Deep learning for image super-resolution: A survey","authors":"Zhihao Wang, Jian Chen, Steven Ch Hoi","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Zhang et al., 2017] Junbo Zhang, Yu Zheng, and Dekang Qi. Deep spatio-temporal residual networks for citywide crowd flows prediction. In AAAI, pages 1655-1661, 2017.","title":"Deep spatio-temporal residual networks for citywide crowd flows prediction","authors":"Junbo Zhang, Yu Zheng, Dekang Qi","year":"2017","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Zhang et al., 2018] Yulun Zhang, Yapeng Tian, Yu Kong, Bineng Zhong, and Yun Fu. Residual dense network for image super-resolution. In CVPR, pages 2472-2481, 2018.","title":"Residual dense network for image super-resolution","authors":"Zhang","year":"2018","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Zhang et al., 2019] Tianjun Zhang, Zhewei Yao, Amir Gho- lami, Kurt Keutzer, Joseph Gonzalez, George Biros, and Michael Mahoney. Anodev2: A coupled neural ode evolu- tion framework. In NeurIPS, 2019.","title":"Anodev2: A coupled neural ode evolution framework","authors":"Zhang","year":"2019","venue":"NeurIPS","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}