{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T14:45:39Z","timestamp":1751467539151,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":86,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,5,21]],"date-time":"2022-05-21T00:00:00Z","timestamp":1653091200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,5,21]]},"DOI":"10.1145\/3510003.3510077","type":"proceedings-article","created":{"date-parts":[[2022,7,5]],"date-time":"2022-07-05T22:42:59Z","timestamp":1657060979000},"page":"1843-1855","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Refty"],"prefix":"10.1145","author":[{"given":"Yanjie","family":"Gao","sequence":"first","affiliation":[{"name":"Microsoft Research, China"}]},{"given":"Zhengxian","family":"Li","sequence":"additional","affiliation":[{"name":"Microsoft Research, China"}]},{"given":"Haoxiang","family":"Lin","sequence":"additional","affiliation":[{"name":"Microsoft Research, China"}]},{"given":"Hongyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"The University of Newcastle, Australia"}]},{"given":"Ming","family":"Wu","sequence":"additional","affiliation":[{"name":"Shanghai Tree-Graph Blockchain Research Institute, China"}]},{"given":"Mao","family":"Yang","sequence":"additional","affiliation":[{"name":"Microsoft Research, China"}]}],"member":"320","published-online":{"date-parts":[[2022,7,5]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"TensorFlow: A System for Large-Scale Machine Learning. In 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16)","author":"Abadi Mart\u00edn","year":"2016","unstructured":"Mart\u00edn Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al. 2016. TensorFlow: A System for Large-Scale Machine Learning. In 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16). USENIX Association, Savannah, GA, 265--283. https:\/\/www.usenix.org\/conference\/osdi16\/technical-sessions\/presentation\/abadi"},{"key":"e_1_3_2_1_2_1","volume-title":"Proceedings of the 18th Conference on USENIX Security Symposium","author":"Akritidis Periklis","year":"2009","unstructured":"Periklis Akritidis, Manuel Costa, Miguel Castro, and Steven Hand. 2009. Baggy Bounds Checking: An Efficient and Backwards-Compatible Defense against out-of-Bounds Errors. In Proceedings of the 18th Conference on USENIX Security Symposium (Montreal, Canada) (SSYM '09). USENIX Association, USA, 51--66."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/1966445.1966472"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/69.43410"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3136000.3136001"},{"key":"e_1_3_2_1_7_1","volume-title":"MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems. CoRR abs\/1512.01274","author":"Chen Tianqi","year":"2015","unstructured":"Tianqi Chen, Mu Li, Yutian Li, Min Lin, Naiyan Wang, Minjie Wang, Tianjun Xiao, Bing Xu, Chiyuan Zhang, and Zheng Zhang. 2015. MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems. CoRR abs\/1512.01274 (2015). arXiv:1512.01274 http:\/\/arxiv.org\/abs\/1512.01274"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1179"},{"key":"e_1_3_2_1_9_1","unstructured":"Fran\u00e7ois Chollet. 2017. Deep Learning with Python. Manning."},{"key":"e_1_3_2_1_10_1","unstructured":"Ronan Collobert Samy Bengio and Johnny Mari\u00e9thoz. 2002. Torch: a modular machine learning software library. Idiap-RR Idiap-RR-46-2002. IDIAP."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-78800-3_24"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.5555\/1770351.1770358"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2012.2211477"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3211346.3211349"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-08867-9_49"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/1159842.1159859"},{"volume-title":"A Mathematical Introduction to Logic","author":"Enderton Herbert B.","key":"e_1_3_2_1_18_1","unstructured":"Herbert B. Enderton. 2001. A Mathematical Introduction to Logic, Second edition. Elsevier."},{"key":"e_1_3_2_1_19_1","unstructured":"Matvey Ezhov. 2021. Simple dynamic seq2seq with TensorFlow. https:\/\/notebook.community\/ematvey\/tensorflow-seq2seq-tutorials\/1-seq2seq."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/114669.114670"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/113445.113468"},{"volume-title":"Proceedings of the 19th International Conference on Computer Aided Verification","author":"Ganesh Vijay","key":"e_1_3_2_1_22_1","unstructured":"Vijay Ganesh and David L. Dill. 2007. A Decision Procedure for Bit-Vectors and Arrays. In Proceedings of the 19th International Conference on Computer Aided Verification (Berlin, Germany) (CAV '07). Springer-Verlag, Berlin, Heidelberg, 519--531."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE43902.2021.00028"},{"volume-title":"Deep Learning","author":"Goodfellow Ian","key":"e_1_3_2_1_24_1","unstructured":"Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016. Deep Learning. MIT Press. http:\/\/www.deeplearningbook.org."},{"key":"e_1_3_2_1_25_1","unstructured":"Google. 2008. Protocol Buffers - Google's data interchange format. https:\/\/developers.google.com\/protocol-buffers\/."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/2950290.2950334"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3460944.3464310"},{"key":"e_1_3_2_1_28_1","volume-title":"Procedures and Parameters: An Axiomatic Approach. In Symposium on Semantics of Algorithmic Languages, E. Engeler (Ed.). Springer Berlin Heidelberg","author":"Hoare C. A. R.","year":"1971","unstructured":"C. A. R. Hoare. 1971. Procedures and Parameters: An Axiomatic Approach. In Symposium on Semantics of Algorithmic Languages, E. Engeler (Ed.). Springer Berlin Heidelberg, Berlin, Heidelberg, 102--116."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3338906.3338955"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P16-1195"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1016\/0167-6423(95)00015-1"},{"key":"e_1_3_2_1_33_1","volume-title":"Proceedings of 1997 Computing: The Australasian Theory Symposium","author":"Barry Jay C.","year":"1997","unstructured":"C. Barry Jay and Milan Sekanina. 1997. Shape Checking of Array Programs. In Proceedings of 1997 Computing: The Australasian Theory Symposium (Sydney, Australia) (CATS '97). Australian Computer Society, Inc., AUS."},{"key":"e_1_3_2_1_34_1","volume-title":"Refinement Types: A Tutorial. CoRR abs\/2010.07763","author":"Jhala Ranjit","year":"2020","unstructured":"Ranjit Jhala and Niki Vazou. 2020. Refinement Types: A Tutorial. CoRR abs\/2010.07763 (2020). arXiv:2010.07763 https:\/\/arxiv.org\/abs\/2010.07763"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/2647868.2654889"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.icte.2020.04.010"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3213846.3213849"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-64437-6_4"},{"key":"e_1_3_2_1_39_1","volume-title":"Proceedings of the 25th International Conference on Neural Information Processing Systems -","volume":"1","author":"Krizhevsky Alex","unstructured":"Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2012. ImageNet Classification with Deep Convolutional Neural Networks. In Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1 (Lake Tahoe, Nevada) (NIPS '12). Curran Associates Inc., Red Hook, NY, USA, 1097--1105."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.4230\/LIPIcs.ECOOP.2020.15"},{"key":"e_1_3_2_1_41_1","unstructured":"Rasmus Munk Larsen and Tatiana Shpeisman. 2019. TensorFlow Graph Optimizations."},{"key":"e_1_3_2_1_42_1","volume-title":"STORM: Refinement Types for Secure Web Applications. In 15th USENIX Symposium on Operating Systems Design and Implementation (OSDI 21)","author":"Lehmann Nico","year":"2021","unstructured":"Nico Lehmann, Rose Kunkel, Jordan Brown, Jean Yang, Niki Vazou, Nadia Polikarpova, Deian Stefan, and Ranjit Jhala. 2021. STORM: Refinement Types for Secure Web Applications. In 15th USENIX Symposium on Operating Systems Design and Implementation (OSDI 21). USENIX Association, 441--459. https:\/\/www.usenix.org\/conference\/osdi21\/presentation\/lehmann"},{"key":"e_1_3_2_1_43_1","volume-title":"Tom\u00e1s Kocisk\u00fd, Andrew W. Senior, Fumin Wang, and Phil Blunsom.","author":"Ling Wang","year":"2016","unstructured":"Wang Ling, Edward Grefenstette, Karl Moritz Hermann, Tom\u00e1s Kocisk\u00fd, Andrew W. Senior, Fumin Wang, and Phil Blunsom. 2016. Latent Predictor Networks for Code Generation. CoRR abs\/1603.06744 (2016). arXiv:1603.06744 http:\/\/arxiv.org\/abs\/1603.06744"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/3368089.3417051"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1571-0661(04)81012-0"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3426426.3434120"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/2.161279"},{"key":"e_1_3_2_1_48_1","unstructured":"Microsoft. 2021. ND-series Virtual Machines. https:\/\/docs.microsoft.com\/en-us\/azure\/virtual-machines\/nd-series."},{"volume-title":"The Definition of Standard ML","author":"Milner Robin","key":"e_1_3_2_1_49_1","unstructured":"Robin Milner, Mads Tofte, and David Macqueen. 1997. The Definition of Standard ML. MIT Press, Cambridge, MA, USA."},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/2633357.2633362"},{"key":"e_1_3_2_1_51_1","volume-title":"Olson and Dursun Delen","author":"David","year":"2008","unstructured":"David L. Olson and Dursun Delen. 2008. Advanced Data Mining Techniques (1st ed.). Springer Publishing Company, Incorporated."},{"key":"e_1_3_2_1_52_1","unstructured":"oneDNN. 2021. Understanding Memory Formats. https:\/\/oneapi-src.github.io\/oneDNN\/v2.5\/dev_guide_understanding_memory_formats.html."},{"key":"e_1_3_2_1_53_1","unstructured":"ONNX. 2017. Open Neural Network Exchange. https:\/\/onnx.ai\/."},{"key":"e_1_3_2_1_54_1","unstructured":"Stack Overflow. 2017. Negative dimension size caused by subtracting 3 from 1 for \"Conv2D\". https:\/\/stackoverflow.com\/questions\/41651628\/negative-dimension-size-caused-by-subtracting-3-from-1-for-conv2d."},{"key":"e_1_3_2_1_55_1","volume-title":"Tensor Transpose and Its Properties. CoRR abs\/1411.1503","author":"Pan Ran","year":"2014","unstructured":"Ran Pan. 2014. Tensor Transpose and Its Properties. CoRR abs\/1411.1503 (2014). arXiv:1411.1503 http:\/\/arxiv.org\/abs\/1411.1503"},{"key":"e_1_3_2_1_56_1","volume-title":"PyTorch: An Imperative Style","author":"Paszke Adam","year":"2019","unstructured":"Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019. PyTorch: An Imperative Style, High-Performance Deep Learning Library. In Advances in Neural Information Processing Systems 32, Vol. 32. Curran Associates, Inc., 8024--8035. https:\/\/proceedings.neurips.cc\/paper\/2019\/file\/bdbca288fee7f92f2bfa9f7012727740-Paper.pdf"},{"key":"e_1_3_2_1_57_1","volume-title":"Types and Programming Languages","author":"Pierce Benjamin C.","unstructured":"Benjamin C. Pierce. 2002. Types and Programming Languages (1st ed.). The MIT Press.","edition":"1"},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cola.2021.101074"},{"key":"e_1_3_2_1_59_1","unstructured":"PyTorch. 2019. Conv2d crashes with stride=0. https:\/\/github.com\/pytorch\/pytorch\/issues\/27598."},{"volume-title":"The topological sorting algorithm for the graph transformation subsystem. https:\/\/github.com\/pytorch\/pytorch\/blob\/v1.5.1\/caffe2\/core\/nomnigraph\/include\/nomnigraph\/Graph\/TopoSort.h#L26","key":"e_1_3_2_1_60_1","unstructured":"PyTorch. 2020. The topological sorting algorithm for the graph transformation subsystem. https:\/\/github.com\/pytorch\/pytorch\/blob\/v1.5.1\/caffe2\/core\/nomnigraph\/include\/nomnigraph\/Graph\/TopoSort.h#L26."},{"key":"e_1_3_2_1_61_1","unstructured":"PyTorch. 2020. The torch.nn.Conv2d API. https:\/\/pytorch.org\/docs\/1.5.1\/nn.html#conv2d."},{"key":"e_1_3_2_1_62_1","unstructured":"PyTorch. 2021. Torchvision. https:\/\/github.com\/pytorch\/vision."},{"key":"e_1_3_2_1_63_1","unstructured":"PyTorch. 2021. Translation with a Sequence to Sequence Network and Attention. https:\/\/pytorch.org\/tutorials\/intermediate\/seq2seq_translation_tutorial.html."},{"key":"e_1_3_2_1_64_1","volume-title":"Artificial Intelligence: A Modern Approach","author":"Russell Stuart","year":"2009","unstructured":"Stuart Russell and Peter Norvig. 2009. Artificial Intelligence: A Modern Approach (3rd ed.). Prentice Hall Press, USA.","edition":"3"},{"key":"e_1_3_2_1_65_1","volume-title":"Proceedings of Machine Learning and Systems, A. Smola, A. Dimakis, and I. Stoica (Eds.)","volume":"3","author":"Saeta Brennan","year":"2021","unstructured":"Brennan Saeta, Denys Shabalin, Marc Rasi, Brad Larson, Xihui Wu, Parker Schuh, Michelle Casbon, Daniel Zheng, Saleem Abdulrasool, Aleksandr Efremov, Dave Abrahams, Chris Lattner, and Richard Wei. 2021. Swift for TensorFlow: A portable, flexible platform for deep learning. In Proceedings of Machine Learning and Systems, A. Smola, A. Dimakis, and I. Stoica (Eds.), Vol. 3. 240--254."},{"key":"e_1_3_2_1_66_1","unstructured":"Sergio Guadarrama Nathan Silberman. 2016. TensorFlow-Slim: A lightweight library for defining training and evaluating complex models in TensorFlow. https:\/\/github.com\/google-research\/tf-slim."},{"key":"e_1_3_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.2168\/LMCS-5(2:10)2009"},{"volume-title":"Proceedings of the 1996 IEEE\/ACM International Conference on Computer-Aided Design","author":"Jo\u00e3o","key":"e_1_3_2_1_68_1","unstructured":"Jo\u00e3o P. Marques Silva and Karem A. Sakallah. 1997. GRASP---a New Search Algorithm for Satisfiability. In Proceedings of the 1996 IEEE\/ACM International Conference on Computer-Aided Design (San Jose, California, USA) (ICCAD '96). IEEE Computer Society, USA, 220--227."},{"key":"e_1_3_2_1_69_1","volume-title":"3rd International Conference on Learning Representations, ICLR","author":"Simonyan Karen","year":"2015","unstructured":"Karen Simonyan and Andrew Zisserman. 2015. Very Deep Convolutional Networks for Large-Scale Image Recognition. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7--9, 2015, Conference Track Proceedings, Yoshua Bengio and Yann Le Cun (Eds.). http:\/\/arxiv.org\/abs\/1409.1556"},{"key":"e_1_3_2_1_70_1","volume-title":"Proceedings of the 27th International Conference on Neural Information Processing Systems -","volume":"2","author":"Sutskever Ilya","unstructured":"Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. 2014. Sequence to Sequence Learning with Neural Networks. In Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2 (Montreal, Canada) (NIPS '14). MIT Press, Cambridge, MA, USA, 3104--3112."},{"key":"e_1_3_2_1_71_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"e_1_3_2_1_72_1","unstructured":"TensorFlow. 2016. Large Strides for 1x1 Convolutions. https:\/\/github.com\/tensorflow\/tensorflow\/issues\/889."},{"key":"e_1_3_2_1_73_1","unstructured":"TensorFlow. 2018. Primitive Neural Net (NN) Operations. https:\/\/github.com\/tensorflow\/docs\/tree\/r1.8\/site\/en\/api_docs\/python\/tf\/nn."},{"key":"e_1_3_2_1_74_1","unstructured":"TensorFlow. 2019. The tf.layers.Conv2D API. https:\/\/github.com\/tensorflow\/docs\/blob\/r1.13\/site\/en\/api_docs\/python\/tf\/layers\/Conv2D.md."},{"key":"e_1_3_2_1_75_1","unstructured":"TensorFlow. 2019. The tf.nn.convolution API. https:\/\/github.com\/tensorflow\/docs\/blob\/r1.13\/site\/en\/api_docs\/python\/tf\/nn\/convolution.md."},{"key":"e_1_3_2_1_76_1","doi-asserted-by":"publisher","DOI":"10.1145\/2975585"},{"key":"e_1_3_2_1_77_1","unstructured":"Ben Trevett. 2021. PyTorch Seq2Seq. https:\/\/github.com\/bentrevett\/pytorch-seq2seq."},{"key":"e_1_3_2_1_78_1","volume-title":"ShapeFlow: Dynamic Shape Interpreter for TensorFlow. CoRR abs\/2011.13452","author":"Verma Sahil","year":"2020","unstructured":"Sahil Verma and Zhendong Su. 2020. ShapeFlow: Dynamic Shape Interpreter for TensorFlow. CoRR abs\/2011.13452 (2020). arXiv:2011.13452 https:\/\/arxiv.org\/abs\/2011.13452"},{"key":"e_1_3_2_1_79_1","unstructured":"WALA. 2021. The T. J. Watson Libraries for Analysis. https:\/\/github.com\/wala\/WALA."},{"volume-title":"The Free Encyclopedia","key":"e_1_3_2_1_80_1","unstructured":"Wikipedia. 2021. Cross-correlation --- Wikipedia, The Free Encyclopedia. http:\/\/en.wikipedia.org\/w\/index.php?title=Cross-correlation&oldid=1031522391."},{"key":"e_1_3_2_1_81_1","doi-asserted-by":"publisher","DOI":"10.1145\/277650.277732"},{"key":"e_1_3_2_1_82_1","unstructured":"Hongkun Yu Chen Chen Xianzhi Du Yeqing Li Abdullah Rashwan Le Hou Pengchong Jin Fan Yang Frederick Liu Jaeyoun Kim and Jing Li. 2020. TensorFlow Model Garden. https:\/\/github.com\/tensorflow\/models."},{"key":"e_1_3_2_1_83_1","doi-asserted-by":"publisher","DOI":"10.1145\/2934664"},{"key":"e_1_3_2_1_84_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE.2019.00086"},{"key":"e_1_3_2_1_85_1","doi-asserted-by":"publisher","DOI":"10.1145\/3377811.3380362"},{"key":"e_1_3_2_1_86_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISSRE.2019.00020"},{"key":"e_1_3_2_1_87_1","doi-asserted-by":"publisher","DOI":"10.1145\/3213846.3213866"}],"event":{"name":"ICSE '22: 44th International Conference on Software Engineering","sponsor":["SIGSOFT ACM Special Interest Group on Software Engineering","IEEE CS"],"location":"Pittsburgh Pennsylvania","acronym":"ICSE '22"},"container-title":["Proceedings of the 44th International Conference on Software Engineering"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3510003.3510077","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3510003.3510077","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:12:05Z","timestamp":1750191125000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3510003.3510077"}},"subtitle":["refinement types for valid deep learning models"],"short-title":[],"issued":{"date-parts":[[2022,5,21]]},"references-count":86,"alternative-id":["10.1145\/3510003.3510077","10.1145\/3510003"],"URL":"https:\/\/doi.org\/10.1145\/3510003.3510077","relation":{},"subject":[],"published":{"date-parts":[[2022,5,21]]},"assertion":[{"value":"2022-07-05","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}