{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T05:03:41Z","timestamp":1750309421450,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":41,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T00:00:00Z","timestamp":1729468800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["CNS2210137"],"award-info":[{"award-number":["CNS2210137"]}],"id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,10,21]]},"DOI":"10.1145\/3627673.3679751","type":"proceedings-article","created":{"date-parts":[[2024,10,20]],"date-time":"2024-10-20T19:34:21Z","timestamp":1729452861000},"page":"2534-2543","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Identifying Contemporaneous and Lagged Dependence Structures by Promoting Sparsity in Continuous-time Neural Networks"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-1131-2816","authenticated-orcid":false,"given":"Fan","family":"Wu","sequence":"first","affiliation":[{"name":"Arizona State University, Tempe, AZ, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9413-7029","authenticated-orcid":false,"given":"Woojin","family":"Cho","sequence":"additional","affiliation":[{"name":"Yonsei University, Seoul, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-9824-6701","authenticated-orcid":false,"given":"David","family":"Korotky","sequence":"additional","affiliation":[{"name":"Oregon State University, Corvallis, OR, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4154-7611","authenticated-orcid":false,"given":"Sanghyun","family":"Hong","sequence":"additional","affiliation":[{"name":"Oregon State University, Corvallis, OR, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6721-2070","authenticated-orcid":false,"given":"Donsub","family":"Rim","sequence":"additional","affiliation":[{"name":"Washington University in St. Louis, St. Louis, MO, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1268-840X","authenticated-orcid":false,"given":"Noseong","family":"Park","sequence":"additional","affiliation":[{"name":"KAIST, Daejeon, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1557-5862","authenticated-orcid":false,"given":"Kookjin","family":"Lee","sequence":"additional","affiliation":[{"name":"Arizona State University, Tempe, AZ, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,10,21]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"NIPS 2016 Deep Learning Symposium.","author":"Ba Jimmy Lei","year":"2016","unstructured":"Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. 2016. Layer normalization. In NIPS 2016 Deep Learning Symposium."},{"volume-title":"International Conference on Learning Representations.","author":"Bellot Alexis","key":"e_1_3_2_1_2_1","unstructured":"Alexis Bellot, Kim Branson, and Mihaela van der Schaar. 2022. Neural graphical modelling in continuous-time: consistency guarantees and algorithms. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_3_1","unstructured":"Ricky TQ Chen Yulia Rubanova Jesse Bettencourt and David Duvenaud. 2018. Neural ordinary differential equations. In NeurIPS. 6572--6583."},{"key":"e_1_3_2_1_4_1","volume-title":"Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio.","author":"Cho Kyunghyun","year":"2014","unstructured":"Kyunghyun Cho, Bart Van Merri\u00ebnboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","unstructured":"Clawpack Development Team. 2020. Clawpack software. https:\/\/doi.org\/10.17605\/osf.io\/kmw6h","DOI":"10.17605\/osf.io"},{"key":"e_1_3_2_1_6_1","volume-title":"NeurIPS","volume":"32","author":"Dupont Emilien","year":"2019","unstructured":"Emilien Dupont, Arnaud Doucet, and Yee Whye Teh. 2019. Augmented neural ODEs. NeurIPS, Vol. 32 (2019)."},{"key":"e_1_3_2_1_7_1","volume-title":"Rhino: Deep Causal Temporal Relationship Learning with History-dependent Noise. In The Eleventh International Conference on Learning Representations.","author":"Gong Wenbo","year":"2023","unstructured":"Wenbo Gong, Joel Jennings, Cheng Zhang, and Nick Pawlowski. 2023. Rhino: Deep Causal Temporal Relationship Learning with History-dependent Noise. In The Eleventh International Conference on Learning Representations."},{"key":"e_1_3_2_1_8_1","volume-title":"Investigating causal relations by econometric models and cross-spectral methods. Econometrica: journal of the Econometric Society","author":"Granger Clive WJ","year":"1969","unstructured":"Clive WJ Granger. 1969. Investigating causal relations by econometric models and cross-spectral methods. Econometrica: journal of the Econometric Society (1969), 424--438."},{"key":"e_1_3_2_1_9_1","volume-title":"Towards Understanding Normalization in Neural ODEs. In ICLR Workshop.","author":"Gusak Julia","year":"2020","unstructured":"Julia Gusak, Larisa Markeeva, Talgat Daulbaev, Alexander Katrutsa, Andrzej Cichocki, and Ivan Oseledets. 2020. Towards Understanding Normalization in Neural ODEs. In ICLR Workshop."},{"volume-title":"The elements of statistical learning: data mining, inference, and prediction","author":"Hastie Trevor","key":"e_1_3_2_1_10_1","unstructured":"Trevor Hastie. 2009. The elements of statistical learning: data mining, inference, and prediction. Vol. 2. Springer."},{"key":"e_1_3_2_1_11_1","unstructured":"Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun. 2016. Deep residual learning for image recognition. In CVPR. 770--778."},{"key":"e_1_3_2_1_12_1","volume-title":"Long short-term memory. Neural computation","author":"Hochreiter Sepp","year":"1997","unstructured":"Sepp Hochreiter and J\u00fcrgen Schmidhuber. 1997. Long short-term memory. Neural computation, Vol. 9, 8 (1997), 1735--1780."},{"key":"e_1_3_2_1_13_1","volume-title":"Neural Laplace: Learning diverse classes of differential equations in the Laplace domain. In ICML.","author":"Holt Samuel I","year":"2022","unstructured":"Samuel I Holt, Zhaozhi Qian, and Mihaela van der Schaar. 2022. Neural Laplace: Learning diverse classes of differential equations in the Laplace domain. In ICML."},{"volume-title":"Climate Modeling with Neural Diffusion Equations","author":"Hwang Jeehyun","key":"e_1_3_2_1_14_1","unstructured":"Jeehyun Hwang, Jeongwhan Choi, Hwangyong Choi, Kookjin Lee, Dongeun Lee, and Noseong Park. 2021. Climate Modeling with Neural Diffusion Equations. In ICDM. IEEE, 230--239."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1098\/rspa.2021.0162"},{"key":"e_1_3_2_1_16_1","first-page":"5696","article-title":"Machine learning structure preserving brackets for forecasting irreversible processes","volume":"34","author":"Lee Kookjin","year":"2021","unstructured":"Kookjin Lee, Nathaniel Trask, and Panos Stinis. 2021. Machine learning structure preserving brackets for forecasting irreversible processes. NeurIPS, Vol. 34 (2021), 5696--5707.","journal-title":"NeurIPS"},{"key":"e_1_3_2_1_17_1","volume-title":"Structure-preserving Sparse Identification of Nonlinear Dynamics for Data-driven Modeling. arXiv preprint arXiv:2109.05364","author":"Lee Kookjin","year":"2021","unstructured":"Kookjin Lee, Nathaniel Trask, and Panos Stinis. 2021. Structure-preserving Sparse Identification of Nonlinear Dynamics for Data-driven Modeling. arXiv preprint arXiv:2109.05364 (2021)."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1017\/S0962492911000043"},{"volume-title":"Global Tsunami Science: Past and Future","author":"LeVeque Randall J","key":"e_1_3_2_1_19_1","unstructured":"Randall J LeVeque, Knut Waagan, Frank I Gonz\u00e1lez, Donsub Rim, and Guang Lin. 2016. Generating random earthquake events for probabilistic tsunami hazard assessment. In Global Tsunami Science: Past and Future, Volume I. Springer, 3671--3692."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00024-021-02841-9"},{"key":"e_1_3_2_1_21_1","volume-title":"Proc. Seminar on predictability","volume":"1","author":"Lorenz Edward N","year":"1996","unstructured":"Edward N Lorenz. 1996. Predictability: A problem partly solved. In Proc. Seminar on predictability, Vol. 1."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.267326"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","unstructured":"Diego Melgar. 2016. Cascadia FakeQuakes waveform data and scenario plots. https:\/\/doi.org\/10.5281\/zenodo.59943","DOI":"10.5281\/zenodo.59943"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","unstructured":"Diego Melgar. 2020. MudPy. https:\/\/doi.org\/10.5281\/zenodo.3703200","DOI":"10.5281\/zenodo.3703200"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1002\/2016JB013314"},{"key":"e_1_3_2_1_26_1","unstructured":"Takeru Miyato Toshiki Kataoka Masanori Koyama and Yuichi Yoshida. 2018. Spectral Normalization for Generative Adversarial Networks. In ICLR."},{"key":"e_1_3_2_1_27_1","first-page":"539","article-title":"MARKOV EQUIVALENCE OF MARGINALIZED LOCAL INDEPENDENCE GRAPHS","volume":"48","author":"S\u00d8REN","year":"2020","unstructured":"S\u00d8REN WENGEL MOGENSEN and NIELS RICHARD HANSEN. 2020. MARKOV EQUIVALENCE OF MARGINALIZED LOCAL INDEPENDENCE GRAPHS. The Annals of Statistics, Vol. 48, 1 (2020), 539--559.","journal-title":"The Annals of Statistics"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"crossref","unstructured":"Thibault Monsel Onofrio Semeraro Lionel Mathelin and Guillaume Charpiat. 2023. Neural State-Dependent Delay Differential Equations. (2023).","DOI":"10.2139\/ssrn.4612787"},{"key":"e_1_3_2_1_29_1","volume-title":"International Conference on Artificial Intelligence and Statistics. PMLR, 1595--1605","author":"Pamfil Roxana","year":"2020","unstructured":"Roxana Pamfil, Nisara Sriwattanaworachai, Shaan Desai, Philip Pilgerstorfer, Konstantinos Georgatzis, Paul Beaumont, and Bryon Aragam. 2020. Dynotears: Structure learning from time-series data. In International Conference on Artificial Intelligence and Statistics. PMLR, 1595--1605."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"crossref","unstructured":"Neal Parikh Stephen Boyd et al. 2014. Proximal algorithms. Foundations and trends\u00ae in Optimization Vol. 1 3 (2014) 127--239.","DOI":"10.1561\/2400000003"},{"key":"e_1_3_2_1_31_1","volume-title":"ICLR 2020 Workshop. https:\/\/www.climatechange.ai\/papers\/iclr2020\/21","author":"Park Sunghyun","year":"2020","unstructured":"Sunghyun Park, Kangyeol Kim, Sookyung Kim, Joonseok Lee, Junsoo Lee, Jiwoo Lee, and Jaegul Choo. 2020. Hurricane Nowcasting with Irregular Time-step using Neural-ODE and Video Prediction. In ICLR 2020 Workshop. https:\/\/www.climatechange.ai\/papers\/iclr2020\/21"},{"key":"e_1_3_2_1_32_1","unstructured":"Adam Paszke et al. 2019. PyTorch: An Imperative Style High-Performance Deep Learning Library. In NeurIPS. https:\/\/proceedings.neurips.cc\/paper\/2019\/hash\/bdbca288fee7f92f2bfa9f7012727740-Abstract.html"},{"key":"e_1_3_2_1_33_1","volume-title":"Ricky TQ Chen, and David K Duvenaud","author":"Rubanova Yulia","year":"2019","unstructured":"Yulia Rubanova, Ricky TQ Chen, and David K Duvenaud. 2019. Latent ordinary differential equations for irregularly-sampled time series. NeurIPS, Vol. 32 (2019)."},{"key":"e_1_3_2_1_34_1","volume-title":"Conference on Uncertainty in Artificial Intelligence. PMLR, 1388--1397","author":"Runge Jakob","year":"2020","unstructured":"Jakob Runge. 2020. Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets. In Conference on Uncertainty in Artificial Intelligence. PMLR, 1388--1397."},{"key":"e_1_3_2_1_35_1","volume-title":"NeurIPS","volume":"29","author":"Salimans Tim","year":"2016","unstructured":"Tim Salimans and Durk P Kingma. 2016. Weight normalization: A simple reparameterization to accelerate training of deep neural networks. NeurIPS, Vol. 29 (2016)."},{"volume-title":"Multivariate Density Estimation: Theory, Practice, and Visualization","author":"Scott David W","key":"e_1_3_2_1_36_1","unstructured":"David W Scott. 2015. Multivariate Density Estimation: Theory, Practice, and Visualization. John Wiley & Sons."},{"key":"e_1_3_2_1_37_1","first-page":"4267","article-title":"Neural Granger causality","volume":"44","author":"Tank Alex","year":"2021","unstructured":"Alex Tank, Ian Covert, Nicholas Foti, Ali Shojaie, and Emily B Fox. 2021. Neural Granger causality. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 44, 8 (2021), 4267--4279.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_2_1_38_1","volume-title":"The Mackey-Glass Anomaly Benchmark. Version v1. 0.1. Zenodo. doi","author":"Thill Markus","year":"2020","unstructured":"Markus Thill, Wolfgang Konen, and Thomas B\u00e4ck. 2020. MarkusThill\/MGAB: The Mackey-Glass Anomaly Benchmark. Version v1. 0.1. Zenodo. doi, Vol. 10 (2020)."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.amc.2005.11.020"},{"key":"e_1_3_2_1_40_1","unstructured":"Qunxi Zhu Yao Guo and Wei Lin. 2020. Neural Delay Differential Equations. In ICLR."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i8.20911"}],"event":{"name":"CIKM '24: The 33rd ACM International Conference on Information and Knowledge Management","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval"],"location":"Boise ID USA","acronym":"CIKM '24"},"container-title":["Proceedings of the 33rd ACM International Conference on Information and Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3627673.3679751","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3627673.3679751","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:58:27Z","timestamp":1750294707000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3627673.3679751"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,21]]},"references-count":41,"alternative-id":["10.1145\/3627673.3679751","10.1145\/3627673"],"URL":"https:\/\/doi.org\/10.1145\/3627673.3679751","relation":{},"subject":[],"published":{"date-parts":[[2024,10,21]]},"assertion":[{"value":"2024-10-21","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}