{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T09:50:22Z","timestamp":1773481822110,"version":"3.50.1"},"reference-count":105,"publisher":"Association for Computing Machinery (ACM)","issue":"3","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2022,11]]},"abstract":"<jats:p>Outlier detection (OD) is a key machine learning task for finding rare and deviant data samples, with many time-critical applications such as fraud detection and intrusion detection. In this work, we propose TOD, the first tensor-based system for efficient and scalable outlier detection on distributed multi-GPU machines. A key idea behind TOD is decomposing complex OD applications into a small collection of basic tensor algebra operators. This decomposition enables TOD to accelerate OD computations by leveraging recent advances in deep learning infrastructure in both hardware and software. Moreover, to deploy memory-intensive OD applications on modern GPUs with limited on-device memory, we introduce two key techniques. First,<jats:italic>provable quantization<\/jats:italic>speeds up OD computations and reduces its memory footprint by automatically performing specific floating-point operations in lower precision while provably guaranteeing no accuracy loss. Second, to exploit the aggregated compute resources and memory capacity of multiple GPUs, we introduce<jats:italic>automatic batching<\/jats:italic>, which decomposes OD computations into small batches for both sequential execution on a single GPU and parallel execution across multiple GPUs.<\/jats:p><jats:p>TOD supports a diverse set of OD algorithms. Evaluation on 11 real-world and 3 synthetic OD datasets shows that TOD is on average 10.9X faster than the leading CPU-based OD system PyOD (with a maximum speedup of 38.9X), and can handle much larger datasets than existing GPU-based OD systems. In addition, TOD allows easy integration of new OD operators, enabling fast prototyping of emerging and yet-to-discovered OD algorithms.<\/jats:p>","DOI":"10.14778\/3570690.3570703","type":"journal-article","created":{"date-parts":[[2023,1,23]],"date-time":"2023-01-23T17:29:55Z","timestamp":1674494995000},"page":"546-560","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["TOD"],"prefix":"10.14778","volume":"16","author":[{"given":"Yue","family":"Zhao","sequence":"first","affiliation":[{"name":"Carnegie Mellon University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"George H.","family":"Chen","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhihao","family":"Jia","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,1,23]]},"reference":[{"key":"e_1_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 2016","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 , Manjunath Kudlur , Josh Levenberg , Rajat Monga , Sherry Moore , Derek Gordon Murray , Benoit Steiner , Paul A. Tucker , Vijay Vasudevan , Pete Warden , Martin Wicke , Yuan Yu , and Xiaoqiang Zheng . 2016 . TensorFlow: A System for Large-Scale Machine Learning. In 12th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2016 , Savannah, GA, USA, November 2--4 , 2016, Kimberly Keeton and Timothy Roscoe (Eds.). USENIX Association, 265--283. https:\/\/www.usenix.org\/conference\/osdi16\/technical-sessions\/presentation\/abadi Mart\u00edn Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek Gordon Murray, Benoit Steiner, Paul A. Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. 2016. TensorFlow: A System for Large-Scale Machine Learning. In 12th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2016, Savannah, GA, USA, November 2--4, 2016, Kimberly Keeton and Timothy Roscoe (Eds.). USENIX Association, 265--283. https:\/\/www.usenix.org\/conference\/osdi16\/technical-sessions\/presentation\/abadi"},{"key":"e_1_2_1_2_1","volume-title":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. ACM, 2485--2494","author":"Abdulaal Ahmed","year":"2021","unstructured":"Ahmed Abdulaal , Zhuanghua Liu , and Tomer Lancewicki . 2021 . Practical approach to asynchronous multivariate time series anomaly detection and localization . In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. ACM, 2485--2494 . Ahmed Abdulaal, Zhuanghua Liu, and Tomer Lancewicki. 2021. Practical approach to asynchronous multivariate time series anomaly detection and localization. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. ACM, 2485--2494."},{"key":"e_1_2_1_3_1","volume-title":"Database Systems for Advanced Applications, 15th International Conference, DASFAA","author":"Achtert Elke","year":"2010","unstructured":"Elke Achtert , Hans-Peter Kriegel , Lisa Reichert , Erich Schubert , Remigius Wojdanowski , and Arthur Zimek . 2010. Visual Evaluation of Outlier Detection Models . In Database Systems for Advanced Applications, 15th International Conference, DASFAA 2010 , Tsukuba, Japan, April 1--4, 2010, Proceedings, Part II (Lecture Notes in Computer Science), Hiroyuki Kitagawa, Yoshiharu Ishikawa, Qing Li, and Chiemi Watanabe (Eds.), Vol. 5982 . Springer , 396--399. 10.1007\/978-3-642-12098-5_34 Elke Achtert, Hans-Peter Kriegel, Lisa Reichert, Erich Schubert, Remigius Wojdanowski, and Arthur Zimek. 2010. Visual Evaluation of Outlier Detection Models. In Database Systems for Advanced Applications, 15th International Conference, DASFAA 2010, Tsukuba, Japan, April 1--4, 2010, Proceedings, Part II (Lecture Notes in Computer Science), Hiroyuki Kitagawa, Yoshiharu Ishikawa, Qing Li, and Chiemi Watanabe (Eds.), Vol. 5982. Springer, 396--399. 10.1007\/978-3-642-12098-5_34"},{"key":"e_1_2_1_4_1","volume-title":"Outlier Analysis","author":"Aggarwal Charu C.","unstructured":"Charu C. Aggarwal . 2013. Outlier Analysis . Springer . Charu C. Aggarwal. 2013. Outlier Analysis. Springer."},{"key":"e_1_2_1_5_1","volume-title":"2011 IEEE 27th international conference on data engineering. IEEE, IEEE, 399--409","author":"Aggarwal Charu C","year":"2011","unstructured":"Charu C Aggarwal , Yuchen Zhao , and S Yu Philip . 2011 . Outlier detection in graph streams . In 2011 IEEE 27th international conference on data engineering. IEEE, IEEE, 399--409 . Charu C Aggarwal, Yuchen Zhao, and S Yu Philip. 2011. Outlier detection in graph streams. In 2011 IEEE 27th international conference on data engineering. IEEE, IEEE, 399--409."},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/1735688.1735707"},{"key":"e_1_2_1_7_1","volume-title":"VLDB","volume":"9","author":"Andr\u00e9 Fabien","year":"2016","unstructured":"Fabien Andr\u00e9 , Anne-Marie Kermarrec , and Nicolas Le Scouarnec . 2016 . Cache locality is not enough: High-performance nearest neighbor search with product quantization fast scan . In VLDB , Vol. 9 . VLDB Endowment, 12. Fabien Andr\u00e9, Anne-Marie Kermarrec, and Nicolas Le Scouarnec. 2016. Cache locality is not enough: High-performance nearest neighbor search with product quantization fast scan. In VLDB, Vol. 9. VLDB Endowment, 12."},{"key":"e_1_2_1_8_1","volume-title":"Euro-Par 2010 - Parallel Processing","author":"Angiulli Fabrizio","unstructured":"Fabrizio Angiulli , Stefano Basta , Stefano Lodi , and Claudio Sartori . 2010. A Distributed Approach to Detect Outliers in Very Large Data Sets . In Euro-Par 2010 - Parallel Processing . Springer Berlin Heidelberg , Berlin, Heidelberg , 329--340. Fabrizio Angiulli, Stefano Basta, Stefano Lodi, and Claudio Sartori. 2010. A Distributed Approach to Detect Outliers in Very Large Data Sets. In Euro-Par 2010 - Parallel Processing. Springer Berlin Heidelberg, Berlin, Heidelberg, 329--340."},{"key":"e_1_2_1_9_1","doi-asserted-by":"crossref","first-page":"3256","DOI":"10.1109\/TPDS.2016.2528984","article-title":"GPU Strategies for Distance-Based Outlier Detection","volume":"27","author":"Angiulli Fabrizio","year":"2016","unstructured":"Fabrizio Angiulli , Stefano Basta , Stefano Lodi , and Claudio Sartori . 2016 . GPU Strategies for Distance-Based Outlier Detection . IEEE Trans. Parallel Distributed Syst. 27 , 11 (2016), 3256 -- 3268 . Fabrizio Angiulli, Stefano Basta, Stefano Lodi, and Claudio Sartori. 2016. GPU Strategies for Distance-Based Outlier Detection. IEEE Trans. Parallel Distributed Syst. 27, 11 (2016), 3256--3268.","journal-title":"IEEE Trans. Parallel Distributed Syst."},{"key":"e_1_2_1_10_1","volume-title":"6th European Conference, PKDD 2002, Helsinki, Finland, August 19--23, 2002, Proceedings (Lecture Notes in Computer Science), Tapio Elomaa, Heikki Mannila, and Hannu Toivonen (Eds.)","volume":"2431","author":"Angiulli Fabrizio","year":"2002","unstructured":"Fabrizio Angiulli and Clara Pizzuti . 2002 . Fast Outlier Detection in High Dimensional Spaces. In Principles of Data Mining and Knowledge Discovery , 6th European Conference, PKDD 2002, Helsinki, Finland, August 19--23, 2002, Proceedings (Lecture Notes in Computer Science), Tapio Elomaa, Heikki Mannila, and Hannu Toivonen (Eds.) , Vol. 2431 . Springer, 15--26. 10.1007\/3-540-45681-3_2 Fabrizio Angiulli and Clara Pizzuti. 2002. Fast Outlier Detection in High Dimensional Spaces. In Principles of Data Mining and Knowledge Discovery, 6th European Conference, PKDD 2002, Helsinki, Finland, August 19--23, 2002, Proceedings (Lecture Notes in Computer Science), Tapio Elomaa, Heikki Mannila, and Hannu Toivonen (Eds.), Vol. 2431. Springer, 15--26. 10.1007\/3-540-45681-3_2"},{"key":"e_1_2_1_11_1","volume-title":"International Conference on Service-Oriented Computing. Springer, 97--109","author":"Ayed Fadhel","year":"2020","unstructured":"Fadhel Ayed , Lorenzo Stella , Tim Januschowski , and Jan Gasthaus . 2020 . Anomaly detection at scale: The case for deep distributional time series models . In International Conference on Service-Oriented Computing. Springer, 97--109 . Fadhel Ayed, Lorenzo Stella, Tim Januschowski, and Jan Gasthaus. 2020. Anomaly detection at scale: The case for deep distributional time series models. In International Conference on Service-Oriented Computing. Springer, 97--109."},{"key":"e_1_2_1_12_1","volume-title":"GPU-Accelerated Feature Selection for Outlier Detection Using the Local Kernel Density Ratio. In 12th IEEE International Conference on Data Mining, ICDM 2012","author":"Azmandian Fatemeh","year":"2012","unstructured":"Fatemeh Azmandian , Ayse Yilmazer , Jennifer G. Dy , Javed A. Aslam , and David R. Kaeli . 2012 . GPU-Accelerated Feature Selection for Outlier Detection Using the Local Kernel Density Ratio. In 12th IEEE International Conference on Data Mining, ICDM 2012 , Brussels, Belgium, December 10--13 , 2012 , Mohammed Javeed Zaki, Arno Siebes, Jeffrey Xu Yu, Bart Goethals, Geoffrey I. Webb, and Xindong Wu (Eds.). IEEE Computer Society, 51--60. 10.1109\/ICDM.2012.51 Fatemeh Azmandian, Ayse Yilmazer, Jennifer G. Dy, Javed A. Aslam, and David R. Kaeli. 2012. GPU-Accelerated Feature Selection for Outlier Detection Using the Local Kernel Density Ratio. In 12th IEEE International Conference on Data Mining, ICDM 2012, Brussels, Belgium, December 10--13, 2012, Mohammed Javeed Zaki, Arno Siebes, Jeffrey Xu Yu, Bart Goethals, Geoffrey I. Webb, and Xindong Wu (Eds.). IEEE Computer Society, 51--60. 10.1109\/ICDM.2012.51"},{"key":"e_1_2_1_13_1","doi-asserted-by":"crossref","unstructured":"Kanishka Bhaduri Bryan L Matthews and Chris R Giannella. 2011. Algorithms for speeding up distance-based outlier detection. In KDD. ACM 859--867. Kanishka Bhaduri Bryan L Matthews and Chris R Giannella. 2011. Algorithms for speeding up distance-based outlier detection. In KDD. ACM 859--867.","DOI":"10.1145\/2020408.2020554"},{"key":"e_1_2_1_14_1","volume-title":"Bolt: Accelerated data mining with fast vector compression. In KDD. ACM, 727--735.","author":"Blalock Davis W","year":"2017","unstructured":"Davis W Blalock and John V Guttag . 2017 . Bolt: Accelerated data mining with fast vector compression. In KDD. ACM, 727--735. Davis W Blalock and John V Guttag. 2017. Bolt: Accelerated data mining with fast vector compression. In KDD. ACM, 727--735."},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.14778\/3229863.3229865"},{"key":"e_1_2_1_16_1","first-page":"1821","article-title":"Series2graph: Graph-based subsequence anomaly detection for time series","volume":"13","author":"Boniol Paul","year":"2020","unstructured":"Paul Boniol and Themis Palpanas . 2020 . Series2graph: Graph-based subsequence anomaly detection for time series . VLDB 13 , 12 (2020), 1821 -- 1834 . Paul Boniol and Themis Palpanas. 2020. Series2graph: Graph-based subsequence anomaly detection for time series. VLDB 13, 12 (2020), 1821--1834.","journal-title":"VLDB"},{"key":"e_1_2_1_17_1","first-page":"2941","article-title":"GraphAn: Graph-based subsequence anomaly detection","volume":"13","author":"Boniol Paul","year":"2020","unstructured":"Paul Boniol , Themis Palpanas , Mohammed Meftah , and Emmanuel Remy . 2020 . GraphAn: Graph-based subsequence anomaly detection . VLDB 13 , 12 (2020), 2941 -- 2944 . Paul Boniol, Themis Palpanas, Mohammed Meftah, and Emmanuel Remy. 2020. GraphAn: Graph-based subsequence anomaly detection. VLDB 13, 12 (2020), 2941--2944.","journal-title":"VLDB"},{"key":"e_1_2_1_18_1","first-page":"1717","article-title":"SAND: streaming subsequence anomaly detection","volume":"14","author":"Boniol Paul","year":"2021","unstructured":"Paul Boniol , John Paparrizos , Themis Palpanas , and Michael J Franklin . 2021 . SAND: streaming subsequence anomaly detection . VLDB 14 , 10 (2021), 1717 -- 1729 . Paul Boniol, John Paparrizos, Themis Palpanas, and Michael J Franklin. 2021. SAND: streaming subsequence anomaly detection. VLDB 14, 10 (2021), 1717--1729.","journal-title":"VLDB"},{"key":"e_1_2_1_19_1","volume-title":"LOF: Identifying Density-Based Local Outliers.. In SIGMOD. ACM, 93--104.","author":"Breunig Markus M.","year":"2000","unstructured":"Markus M. Breunig , Hans-Peter Kriegel , Raymond T. Ng , and J\u00f6rg Sander . 2000 . LOF: Identifying Density-Based Local Outliers.. In SIGMOD. ACM, 93--104. Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng, and J\u00f6rg Sander. 2000. LOF: Identifying Density-Based Local Outliers.. In SIGMOD. ACM, 93--104."},{"key":"e_1_2_1_20_1","volume-title":"Proceedings of ECML PKDD Workshop: Languages for Data Mining and Machine Learning. ECML, 108--122","author":"Buitinck Lars","year":"2013","unstructured":"Lars Buitinck , Gilles Louppe , Mathieu Blondel , Fabian Pedregosa , Andreas Mueller , Olivier Grisel , Vlad Niculae , Peter Prettenhofer , Alexandre Gramfort , Jaques Grobler , Robert Layton , Jake VanderPlas , Arnaud Joly , Brian Holt , and Ga\u00ebl Varoquaux . 2013 . API design for machine learning software: experiences from the scikit-learn project . In Proceedings of ECML PKDD Workshop: Languages for Data Mining and Machine Learning. ECML, 108--122 . Lars Buitinck, Gilles Louppe, Mathieu Blondel, Fabian Pedregosa, Andreas Mueller, Olivier Grisel, Vlad Niculae, Peter Prettenhofer, Alexandre Gramfort, Jaques Grobler, Robert Layton, Jake VanderPlas, Arnaud Joly, Brian Holt, and Ga\u00ebl Varoquaux. 2013. API design for machine learning software: experiences from the scikit-learn project. In Proceedings of ECML PKDD Workshop: Languages for Data Mining and Machine Learning. ECML, 108--122."},{"key":"e_1_2_1_21_1","volume-title":"Barbora Micenkov\u00e1, Erich Schubert, Ira Assent, and Michael E Houle.","author":"Campos Guilherme O","year":"2016","unstructured":"Guilherme O Campos , Arthur Zimek , J\u00f6rg Sander , Ricardo JGB Campello , Barbora Micenkov\u00e1, Erich Schubert, Ira Assent, and Michael E Houle. 2016 . On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study. Data mining and knowledge discovery 30, 4 (2016), 891--927. Guilherme O Campos, Arthur Zimek, J\u00f6rg Sander, Ricardo JGB Campello, Barbora Micenkov\u00e1, Erich Schubert, Ira Assent, and Michael E Houle. 2016. On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study. Data mining and knowledge discovery 30, 4 (2016), 891--927."},{"key":"e_1_2_1_22_1","first-page":"1621","article-title":"Interactive outlier exploration in big data streams","volume":"7","author":"Cao Lei","year":"2014","unstructured":"Lei Cao , Qingyang Wang , and Elke A Rundensteiner . 2014 . Interactive outlier exploration in big data streams . VLDB 7 , 13 (2014), 1621 -- 1624 . Lei Cao, Qingyang Wang, and Elke A Rundensteiner. 2014. Interactive outlier exploration in big data streams. VLDB 7, 13 (2014), 1621--1624.","journal-title":"VLDB"},{"key":"e_1_2_1_23_1","first-page":"920","article-title":"Efficient discovery of sequence outlier patterns","volume":"12","author":"Cao Lei","year":"2019","unstructured":"Lei Cao , Yizhou Yan , Samuel Madden , Elke A Rundensteiner , and Mathan Gopalsamy . 2019 . Efficient discovery of sequence outlier patterns . VLDB 12 , 8 (2019), 920 -- 932 . Lei Cao, Yizhou Yan, Samuel Madden, Elke A Rundensteiner, and Mathan Gopalsamy. 2019. Efficient discovery of sequence outlier patterns. VLDB 12, 8 (2019), 920--932.","journal-title":"VLDB"},{"key":"e_1_2_1_24_1","volume-title":"VS-Quant: Per-vector Scaled Quantization for Accurate Low-Precision Neural Network Inference. CoRR","author":"Dai Steve","year":"2021","unstructured":"Steve Dai , Rangharajan Venkatesan , Haoxing Ren , Brian Zimmer , William J. Dally , and Brucek Khailany . 2021. VS-Quant: Per-vector Scaled Quantization for Accurate Low-Precision Neural Network Inference. CoRR ( 2021 ). arXiv:2102.04503 Steve Dai, Rangharajan Venkatesan, Haoxing Ren, Brian Zimmer, William J. Dally, and Brucek Khailany. 2021. VS-Quant: Per-vector Scaled Quantization for Accurate Low-Precision Neural Network Inference. CoRR (2021). arXiv:2102.04503"},{"key":"e_1_2_1_25_1","volume-title":"2009 IEEE conference on computer vision and pattern recognition. IEEE, 248--255","author":"Deng Jia","year":"2009","unstructured":"Jia Deng , Wei Dong , Richard Socher , Li-Jia Li , Kai Li , and Li Fei-Fei . 2009 . Imagenet: A large-scale hierarchical image database . In 2009 IEEE conference on computer vision and pattern recognition. IEEE, 248--255 . Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition. IEEE, 248--255."},{"key":"e_1_2_1_26_1","first-page":"238","article-title":"Discriminatory analysis. Non-parametric discrimination: Consistency properties","volume":"57","author":"Fix Evelyn","year":"1989","unstructured":"Evelyn Fix and Joseph Lawson Hodges . 1989 . Discriminatory analysis. Non-parametric discrimination: Consistency properties . International Statistical Review\/Revue Internationale de Statistique 57 , 3 (1989), 238 -- 247 . Evelyn Fix and Joseph Lawson Hodges. 1989. Discriminatory analysis. Non-parametric discrimination: Consistency properties. International Statistical Review\/Revue Internationale de Statistique 57, 3 (1989), 238--247.","journal-title":"International Statistical Review\/Revue Internationale de Statistique"},{"key":"e_1_2_1_27_1","first-page":"461","article-title":"Fast approximate nearest neighbor search with the navigating spreading-out graph","volume":"12","author":"Fu Cong","year":"2019","unstructured":"Cong Fu , Chao Xiang , Changxu Wang , and Deng Cai . 2019 . Fast approximate nearest neighbor search with the navigating spreading-out graph . VLDB 12 , 5 (2019), 461 -- 474 . Cong Fu, Chao Xiang, Changxu Wang, and Deng Cai. 2019. Fast approximate nearest neighbor search with the navigating spreading-out graph. VLDB 12, 5 (2019), 461--474.","journal-title":"VLDB"},{"key":"e_1_2_1_28_1","doi-asserted-by":"crossref","unstructured":"Tianfan Fu Cao Xiao Cheng Qian Lucas M Glass and Jimeng Sun. 2021. Probabilistic and Dynamic Molecule-Disease Interaction Modeling for Drug Discovery. In KDD. ACM 404--414. Tianfan Fu Cao Xiao Cheng Qian Lucas M Glass and Jimeng Sun. 2021. Probabilistic and Dynamic Molecule-Disease Interaction Modeling for Drug Discovery. In KDD. ACM 404--414.","DOI":"10.1145\/3447548.3467286"},{"key":"e_1_2_1_29_1","unstructured":"Yanjie Gao Yu Liu Hongyu Zhang Zhengxian Li Yonghao Zhu Haoxiang Lin and Mao Yang. 2020. Estimating GPU memory consumption of deep learning models. In ESEC\/FSE. ACM 1342--1352. Yanjie Gao Yu Liu Hongyu Zhang Zhengxian Li Yonghao Zhu Haoxiang Lin and Mao Yang. 2020. Estimating GPU memory consumption of deep learning models. In ESEC\/FSE. ACM 1342--1352."},{"key":"e_1_2_1_30_1","first-page":"1119","article-title":"Traversing large graphs on GPUs with unified memory","volume":"13","author":"Gera Prasun","year":"2020","unstructured":"Prasun Gera , Hyojong Kim , Piyush Sao , Hyesoon Kim , and David Bader . 2020 . Traversing large graphs on GPUs with unified memory . VLDB 13 , 7 (2020), 1119 -- 1133 . Prasun Gera, Hyojong Kim, Piyush Sao, Hyesoon Kim, and David Bader. 2020. Traversing large graphs on GPUs with unified memory. VLDB 13, 7 (2020), 1119--1133.","journal-title":"VLDB"},{"key":"e_1_2_1_31_1","volume-title":"Histogram-based outlier score (hbos): A fast unsupervised anomaly detection algorithm. KI","author":"Goldstein Markus","year":"2012","unstructured":"Markus Goldstein and Andreas Dengel . 2012. Histogram-based outlier score (hbos): A fast unsupervised anomaly detection algorithm. KI ( 2012 ), 59--63. Markus Goldstein and Andreas Dengel. 2012. Histogram-based outlier score (hbos): A fast unsupervised anomaly detection algorithm. KI (2012), 59--63."},{"key":"e_1_2_1_32_1","unstructured":"Ian Goodfellow Yoshua Bengio and Aaron Courville. 2016. Deep learning. MIT. Ian Goodfellow Yoshua Bengio and Aaron Courville. 2016. Deep learning. MIT."},{"key":"e_1_2_1_33_1","volume-title":"ADBench: Anomaly Detection Benchmark. arXiv preprint arXiv:2206.09426","author":"Han Songqiao","year":"2022","unstructured":"Songqiao Han , Xiyang Hu , Hailiang Huang , Mingqi Jiang , and Yue Zhao . 2022. ADBench: Anomaly Detection Benchmark. arXiv preprint arXiv:2206.09426 ( 2022 ). Songqiao Han, Xiyang Hu, Hailiang Huang, Mingqi Jiang, and Yue Zhao. 2022. ADBench: Anomaly Detection Benchmark. arXiv preprint arXiv:2206.09426 (2022)."},{"key":"e_1_2_1_34_1","doi-asserted-by":"crossref","unstructured":"Charles R Harris K Jarrod Millman St\u00e9fan J van der Walt Ralf Gommers Pauli Virtanen David Cournapeau Eric Wieser Julian Taylor Sebastian Berg Nathaniel J Smith etal 2020. Array programming with NumPy. Nature (2020). Charles R Harris K Jarrod Millman St\u00e9fan J van der Walt Ralf Gommers Pauli Virtanen David Cournapeau Eric Wieser Julian Taylor Sebastian Berg Nathaniel J Smith et al. 2020. Array programming with NumPy. Nature (2020).","DOI":"10.1038\/s41586-020-2649-2"},{"key":"e_1_2_1_35_1","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE, 770--778","author":"He Kaiming","year":"2016","unstructured":"Kaiming He , Xiangyu Zhang , Shaoqing Ren , and Jian Sun . 2016 . Deep residual learning for image recognition . In Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE, 770--778 . Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE, 770--778."},{"key":"e_1_2_1_36_1","volume-title":"A demonstration of the exathlon benchmarking platform for explainable anomaly detection. VLDB (PVLDB)","author":"Jacob Vincent","year":"2021","unstructured":"Vincent Jacob , Fei Song , Arnaud Stiegler , Bijan Rad , Yanlei Diao , and Nesime Tatbul . 2021. A demonstration of the exathlon benchmarking platform for explainable anomaly detection. VLDB (PVLDB) ( 2021 ). Vincent Jacob, Fei Song, Arnaud Stiegler, Bijan Rad, Yanlei Diao, and Nesime Tatbul. 2021. A demonstration of the exathlon benchmarking platform for explainable anomaly detection. VLDB (PVLDB) (2021)."},{"key":"e_1_2_1_37_1","volume-title":"Accelerating DBSCAN Algorithm with AI Chips for Large Datasets. In ICPP 2021: 50th International Conference on Parallel Processing","author":"Ji Zhuoran","year":"2021","unstructured":"Zhuoran Ji and Cho-Li Wang . 2021 . Accelerating DBSCAN Algorithm with AI Chips for Large Datasets. In ICPP 2021: 50th International Conference on Parallel Processing , Lemont, IL, USA, August 9 - 12 , 2021, Xian-He Sun, Sameer Shende, Laxmikant V. Kal\u00e9, and Yong Chen (Eds.). ACM, 51:1--51:11. 10.1145\/3472456.3473518 Zhuoran Ji and Cho-Li Wang. 2021. Accelerating DBSCAN Algorithm with AI Chips for Large Datasets. In ICPP 2021: 50th International Conference on Parallel Processing, Lemont, IL, USA, August 9 - 12, 2021, Xian-He Sun, Sameer Shende, Laxmikant V. Kal\u00e9, and Yong Chen (Eds.). ACM, 51:1--51:11. 10.1145\/3472456.3473518"},{"key":"e_1_2_1_38_1","unstructured":"Zhihao Jia Sina Lin Mingyu Gao Matei Zaharia and Alex Aiken. 2020. Improving the Accuracy Scalability and Performance of Graph Neural Networks with Roc. In MLSys. mlsys.org. Zhihao Jia Sina Lin Mingyu Gao Matei Zaharia and Alex Aiken. 2020. Improving the Accuracy Scalability and Performance of Graph Neural Networks with Roc. In MLSys. mlsys.org."},{"key":"e_1_2_1_39_1","volume-title":"Proceedings of Machine Learning and Systems 2019","author":"Jia Zhihao","year":"2019","unstructured":"Zhihao Jia , Matei Zaharia , and Alex Aiken . 2019 . Beyond Data and Model Parallelism for Deep Neural Networks . In Proceedings of Machine Learning and Systems 2019 , MLSys 2019, Stanford, CA, USA, March 31 - April 2, 2019, Ameet Talwalkar, Virginia Smith, and Matei Zaharia (Eds.). mlsys.org. https:\/\/proceedings.mlsys.org\/book\/265.pdf Zhihao Jia, Matei Zaharia, and Alex Aiken. 2019. Beyond Data and Model Parallelism for Deep Neural Networks. In Proceedings of Machine Learning and Systems 2019, MLSys 2019, Stanford, CA, USA, March 31 - April 2, 2019, Ameet Talwalkar, Virginia Smith, and Matei Zaharia (Eds.). mlsys.org. https:\/\/proceedings.mlsys.org\/book\/265.pdf"},{"key":"e_1_2_1_40_1","first-page":"94720","article-title":"IEEE standard 754 for binary floating-point arithmetic","volume":"754","author":"Kahan William","year":"1996","unstructured":"William Kahan . 1996 . IEEE standard 754 for binary floating-point arithmetic . Lecture Notes on the Status of IEEE 754 , 94720 - 91776 (1996), 11. William Kahan. 1996. IEEE standard 754 for binary floating-point arithmetic. Lecture Notes on the Status of IEEE 754, 94720-1776 (1996), 11.","journal-title":"Lecture Notes on the Status of IEEE"},{"key":"e_1_2_1_41_1","first-page":"268","article-title":"Elle: inferring isolation anomalies from experimental observations","volume":"14","author":"Kingsbury Kyle","year":"2020","unstructured":"Kyle Kingsbury and Peter Alvaro . 2020 . Elle: inferring isolation anomalies from experimental observations . VLDB 14 , 3 (2020), 268 -- 280 . Kyle Kingsbury and Peter Alvaro. 2020. Elle: inferring isolation anomalies from experimental observations. VLDB 14, 3 (2020), 268--280.","journal-title":"VLDB"},{"key":"e_1_2_1_42_1","first-page":"1797","article-title":"Tensors: An abstraction for general data processing","volume":"14","author":"Koutsoukos Dimitrios","year":"2021","unstructured":"Dimitrios Koutsoukos , Supun Nakandala , Konstantinos Karanasos , Karla Saur , Gustavo Alonso , and Matteo Interlandi . 2021 . Tensors: An abstraction for general data processing . VLDB 14 , 10 (2021), 1797 -- 1804 . Dimitrios Koutsoukos, Supun Nakandala, Konstantinos Karanasos, Karla Saur, Gustavo Alonso, and Matteo Interlandi. 2021. Tensors: An abstraction for general data processing. VLDB 14, 10 (2021), 1797--1804.","journal-title":"VLDB"},{"key":"e_1_2_1_43_1","doi-asserted-by":"crossref","unstructured":"Hans-Peter Kriegel Matthias Schubert and Arthur Zimek. 2008. Angle-based outlier detection in high-dimensional data.. In KDD. ACM 444--452. Hans-Peter Kriegel Matthias Schubert and Arthur Zimek. 2008. Angle-based outlier detection in high-dimensional data.. In KDD. ACM 444--452.","DOI":"10.1145\/1401890.1401946"},{"key":"e_1_2_1_44_1","volume-title":"Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, NeurIPS Datasets and Benchmarks 2021","author":"Lai Kwei-Herng","year":"2021","unstructured":"Kwei-Herng Lai , Daochen Zha , Junjie Xu , Yue Zhao , Guanchu Wang , and Xia Hu . 2021 . Revisiting Time Series Outlier Detection: Definitions and Benchmarks . In Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, NeurIPS Datasets and Benchmarks 2021 , December 2021, virtual. Joaquin Vanschoren and Sai-Kit Yeung. https:\/\/datasets-benchmarks-proceedings.neurips.cc\/paper\/ 2021\/hash\/ec5decca5ed3d6b8079e2e7e7bacc9f2-Abstract-round1.html Kwei-Herng Lai, Daochen Zha, Junjie Xu, Yue Zhao, Guanchu Wang, and Xia Hu. 2021. Revisiting Time Series Outlier Detection: Definitions and Benchmarks. In Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, NeurIPS Datasets and Benchmarks 2021, December 2021, virtual. Joaquin Vanschoren and Sai-Kit Yeung. https:\/\/datasets-benchmarks-proceedings.neurips.cc\/paper\/2021\/hash\/ec5decca5ed3d6b8079e2e7e7bacc9f2-Abstract-round1.html"},{"key":"e_1_2_1_45_1","volume-title":"A Comparative Study of Anomaly Detection Schemes in Network Intrusion Detection","author":"Lazarevic Aleksandar","unstructured":"Aleksandar Lazarevic , Levent Ert\u00f6z , Vipin Kumar , Aysel Ozgur , and Jaideep Srivastava . 2003. A Comparative Study of Anomaly Detection Schemes in Network Intrusion Detection . In SDM. SIAM , 25--36. Aleksandar Lazarevic, Levent Ert\u00f6z, Vipin Kumar, Aysel Ozgur, and Jaideep Srivastava. 2003. A Comparative Study of Anomaly Detection Schemes in Network Intrusion Detection. In SDM. SIAM, 25--36."},{"key":"e_1_2_1_46_1","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1145\/3299986.3299989","article-title":"Research Issues of Outlier Detection in Trajectory Streams Using GPUs","volume":"20","author":"Leal Eleazar","year":"2018","unstructured":"Eleazar Leal and Le Gruenwald . 2018 . Research Issues of Outlier Detection in Trajectory Streams Using GPUs . SIGKDD Explor. 20 , 2 (2018), 13 -- 20 . Eleazar Leal and Le Gruenwald. 2018. Research Issues of Outlier Detection in Trajectory Streams Using GPUs. SIGKDD Explor. 20, 2 (2018), 13--20.","journal-title":"SIGKDD Explor."},{"key":"e_1_2_1_47_1","volume-title":"2019 IEEE International Solid-State Circuits Conference-(ISSCC). IEEE, 12--19","author":"LeCun Yann","year":"2019","unstructured":"Yann LeCun . 2019 . 1.1 deep learning hardware: past, present, and future . In 2019 IEEE International Solid-State Circuits Conference-(ISSCC). IEEE, 12--19 . Yann LeCun. 2019. 1.1 deep learning hardware: past, present, and future. In 2019 IEEE International Solid-State Circuits Conference-(ISSCC). IEEE, 12--19."},{"key":"e_1_2_1_48_1","volume-title":"Leman Akoglu, Vincent S. Tseng, and Christos Faloutsos.","author":"Lee Meng-Chieh","year":"2020","unstructured":"Meng-Chieh Lee , Yue Zhao , Aluna Wang , Pierre Jinghong Liang , Leman Akoglu, Vincent S. Tseng, and Christos Faloutsos. 2020 . AutoAudit: Mining Accounting and Time-Evolving Graphs. In Big Data. IEEE , 950--956. Meng-Chieh Lee, Yue Zhao, Aluna Wang, Pierre Jinghong Liang, Leman Akoglu, Vincent S. Tseng, and Christos Faloutsos. 2020. AutoAudit: Mining Accounting and Time-Evolving Graphs. In Big Data. IEEE, 950--956."},{"key":"e_1_2_1_49_1","first-page":"2999","article-title":"The art of balance: a RateupDB\u2122 experience of building a CPU\/GPU hybrid database product","volume":"14","author":"Lee Rubao","year":"2021","unstructured":"Rubao Lee , Minghong Zhou , Chi Li , Shenggang Hu , Jianping Teng , Dongyang Li , and Xiaodong Zhang . 2021 . The art of balance: a RateupDB\u2122 experience of building a CPU\/GPU hybrid database product . VLDB 14 , 12 (2021), 2999 -- 3013 . Rubao Lee, Minghong Zhou, Chi Li, Shenggang Hu, Jianping Teng, Dongyang Li, and Xiaodong Zhang. 2021. The art of balance: a RateupDB\u2122 experience of building a CPU\/GPU hybrid database product. VLDB 14, 12 (2021), 2999--3013.","journal-title":"VLDB"},{"key":"e_1_2_1_50_1","volume-title":"Proc. ACM Program. Lang. 2, POPL","author":"Lee Wonyeol","year":"2018","unstructured":"Wonyeol Lee , Rahul Sharma , and Alex Aiken . 2018 . On automatically proving the correctness of math.h implementations . Proc. ACM Program. Lang. 2, POPL (2018), 47:1--47:32. 10.1145\/3158135 Wonyeol Lee, Rahul Sharma, and Alex Aiken. 2018. On automatically proving the correctness of math.h implementations. Proc. ACM Program. Lang. 2, POPL (2018), 47:1--47:32. 10.1145\/3158135"},{"key":"e_1_2_1_51_1","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1145\/1122501.1122505","article-title":"Glift: Generic, efficient, random-access GPU data structures","volume":"25","author":"Lefohn Aaron E","year":"2006","unstructured":"Aaron E Lefohn , Shubhabrata Sengupta , Joe Kniss , Robert Strzodka , and John D Owens . 2006 . Glift: Generic, efficient, random-access GPU data structures . ACM Transactions on Graphics (TOG) 25 , 1 (2006), 60 -- 99 . Aaron E Lefohn, Shubhabrata Sengupta, Joe Kniss, Robert Strzodka, and John D Owens. 2006. Glift: Generic, efficient, random-access GPU data structures. ACM Transactions on Graphics (TOG) 25, 1 (2006), 60--99.","journal-title":"ACM Transactions on Graphics (TOG)"},{"key":"e_1_2_1_52_1","volume-title":"COPOD: Copula-Based Outlier Detection","author":"Li Zheng","year":"2020","unstructured":"Zheng Li , Yue Zhao , Nicola Botta , Cezar Ionescu , and Xiyang Hu . 2020 . COPOD: Copula-Based Outlier Detection . In ICDM. IEEE , 1118--1123. Zheng Li, Yue Zhao, Nicola Botta, Cezar Ionescu, and Xiyang Hu. 2020. COPOD: Copula-Based Outlier Detection. In ICDM. IEEE, 1118--1123."},{"key":"e_1_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3159580"},{"key":"e_1_2_1_54_1","volume-title":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 3280--3288","author":"Liu Can","year":"2021","unstructured":"Can Liu , Li Sun , Xiang Ao , Jinghua Feng , Qing He , and Hao Yang . 2021 . Intention-aware heterogeneous graph attention networks for fraud transactions detection . In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 3280--3288 . Can Liu, Li Sun, Xiang Ao, Jinghua Feng, Qing He, and Hao Yang. 2021. Intention-aware heterogeneous graph attention networks for fraud transactions detection. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 3280--3288."},{"key":"e_1_2_1_55_1","volume-title":"Kai Ming Ting, and Zhi-Hua Zhou","author":"Liu Fei Tony","year":"2008","unstructured":"Fei Tony Liu , Kai Ming Ting, and Zhi-Hua Zhou . 2008 . Isolation Forest. In ICDM. IEEE Computer Society , 413--422. Fei Tony Liu, Kai Ming Ting, and Zhi-Hua Zhou. 2008. Isolation Forest. In ICDM. IEEE Computer Society, 413--422."},{"key":"e_1_2_1_56_1","volume-title":"Fairness-aware Outlier Ensemble. arXiv preprint arXiv:2103.09419","author":"Liu Haoyu","year":"2021","unstructured":"Haoyu Liu , Fenglong Ma , Shibo He , Jiming Chen , and Jing Gao . 2021. Fairness-aware Outlier Ensemble. arXiv preprint arXiv:2103.09419 ( 2021 ). Haoyu Liu, Fenglong Ma, Shibo He, Jiming Chen, and Jing Gao. 2021. Fairness-aware Outlier Ensemble. arXiv preprint arXiv:2103.09419 (2021)."},{"key":"e_1_2_1_57_1","unstructured":"Kay Liu Yingtong Dou Yue Zhao Xueying Ding Xiyang Hu Ruitong Zhang Kaize Ding Canyu Chen Hao Peng Kai Shu etal 2022. Benchmarking Node Outlier Detection on Graphs. arXiv preprint arXiv:2206.10071 (2022). Kay Liu Yingtong Dou Yue Zhao Xueying Ding Xiyang Hu Ruitong Zhang Kaize Ding Canyu Chen Hao Peng Kai Shu et al. 2022. Benchmarking Node Outlier Detection on Graphs. arXiv preprint arXiv:2206.10071 (2022)."},{"key":"e_1_2_1_58_1","unstructured":"Kay Liu Yingtong Dou Yue Zhao Xueying Ding Xiyang Hu Ruitong Zhang Kaize Ding Canyu Chen Hao Peng Kai Shu etal 2022. PyGOD: A Python Library for Graph Outlier Detection. arXiv preprint arXiv:2204.12095 (2022). Kay Liu Yingtong Dou Yue Zhao Xueying Ding Xiyang Hu Ruitong Zhang Kaize Ding Canyu Chen Hao Peng Kai Shu et al. 2022. PyGOD: A Python Library for Graph Outlier Detection. arXiv preprint arXiv:2204.12095 (2022)."},{"key":"e_1_2_1_59_1","volume-title":"Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, SIGIR 2020","author":"Liu Zhiwei","year":"2020","unstructured":"Zhiwei Liu , Yingtong Dou , Philip S. Yu , Yutong Deng , and Hao Peng . 2020 . Alleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detection . In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, SIGIR 2020 , Virtual Event, China, July 25--30 , 2020, Jimmy Huang, Yi Chang, Xueqi Cheng, Jaap Kamps, Vanessa Murdock, Ji-Rong Wen, and Yiqun Liu (Eds.). ACM, 1569--1572. 10.1145\/3397271.3401253 Zhiwei Liu, Yingtong Dou, Philip S. Yu, Yutong Deng, and Hao Peng. 2020. Alleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detection. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, SIGIR 2020, Virtual Event, China, July 25--30, 2020, Jimmy Huang, Yi Chang, Xueqi Cheng, Jaap Kamps, Vanessa Murdock, Ji-Rong Wen, and Yiqun Liu (Eds.). ACM, 1569--1572. 10.1145\/3397271.3401253"},{"key":"e_1_2_1_60_1","unstructured":"Moshe Looks Marcello Herreshoff DeLesley Hutchins and Peter Norvig. 2017. Deep Learning with Dynamic Computation Graphs. In ICLR. OpenReview.net. Moshe Looks Marcello Herreshoff DeLesley Hutchins and Peter Norvig. 2017. Deep Learning with Dynamic Computation Graphs. In ICLR. OpenReview.net."},{"key":"e_1_2_1_61_1","volume-title":"Parallel Algorithms for Distance-Based and Density-Based Outliers","author":"Lozano Elio","unstructured":"Elio Lozano and Edgar Acu\u00f1a . 2005. Parallel Algorithms for Distance-Based and Density-Based Outliers . In ICDM. IEEE Computer Society , 729--732. Elio Lozano and Edgar Acu\u00f1a. 2005. Parallel Algorithms for Distance-Based and Density-Based Outliers. In ICDM. IEEE Computer Society, 729--732."},{"key":"e_1_2_1_62_1","volume-title":"KDD. ACM","author":"Manzoor Emaad","year":"2018","unstructured":"Emaad Manzoor , Hemank Lamba , and Leman Akoglu . 2018 . xstream: Outlier detection in feature-evolving data streams . In KDD. ACM , 1963--1972. Emaad Manzoor, Hemank Lamba, and Leman Akoglu. 2018. xstream: Outlier detection in feature-evolving data streams. In KDD. ACM, 1963--1972."},{"key":"e_1_2_1_63_1","volume-title":"Proc. of ML Systems Workshop in NIPS. NIPS.","author":"Meng Chen","year":"2017","unstructured":"Chen Meng , Minmin Sun , Jun Yang , Minghui Qiu , and Yang Gu . 2017 . Training deeper models by GPU memory optimization on TensorFlow . In Proc. of ML Systems Workshop in NIPS. NIPS. Chen Meng, Minmin Sun, Jun Yang, Minghui Qiu, and Yang Gu. 2017. Training deeper models by GPU memory optimization on TensorFlow. In Proc. of ML Systems Workshop in NIPS. NIPS."},{"key":"e_1_2_1_64_1","first-page":"1","article-title":"Relaxed operator fusion for in-memory databases: Making compilation, vectorization, and prefetching work together at last","volume":"11","author":"Menon Prashanth","year":"2017","unstructured":"Prashanth Menon , Todd C Mowry , and Andrew Pavlo . 2017 . Relaxed operator fusion for in-memory databases: Making compilation, vectorization, and prefetching work together at last . VLDB 11 , 1 (2017), 1 -- 13 . Prashanth Menon, Todd C Mowry, and Andrew Pavlo. 2017. Relaxed operator fusion for in-memory databases: Making compilation, vectorization, and prefetching work together at last. VLDB 11, 1 (2017), 1--13.","journal-title":"VLDB"},{"key":"e_1_2_1_65_1","first-page":"114","article-title":"EMOGI: efficient memory-access for out-of-memory graph-traversal in GPUs","volume":"14","author":"Min Seung Won","year":"2020","unstructured":"Seung Won Min , Vikram Sharma Mailthody , Zaid Qureshi , Jinjun Xiong , Eiman Ebrahimi , and Wen-mei Hwu. 2020 . EMOGI: efficient memory-access for out-of-memory graph-traversal in GPUs . VLDB 14 , 2 (2020), 114 -- 127 . Seung Won Min, Vikram Sharma Mailthody, Zaid Qureshi, Jinjun Xiong, Eiman Ebrahimi, and Wen-mei Hwu. 2020. EMOGI: efficient memory-access for out-of-memory graph-traversal in GPUs. VLDB 14, 2 (2020), 114--127.","journal-title":"VLDB"},{"key":"e_1_2_1_66_1","first-page":"2087","article-title":"Large graph convolutional network training with GPU-oriented data communication architecture","volume":"14","author":"Min Seung Won","year":"2021","unstructured":"Seung Won Min , Kun Wu , Sitao Huang , Mert Hidayeto\u011flu , Jinjun Xiong , Eiman Ebrahimi , Deming Chen , and Wen Mei Hwu . 2021 . Large graph convolutional network training with GPU-oriented data communication architecture . VLDB 14 , 11 (2021), 2087 -- 2100 . Seung Won Min, Kun Wu, Sitao Huang, Mert Hidayeto\u011flu, Jinjun Xiong, Eiman Ebrahimi, Deming Chen, and Wen Mei Hwu. 2021. Large graph convolutional network training with GPU-oriented data communication architecture. VLDB 14, 11 (2021), 2087--2100.","journal-title":"VLDB"},{"key":"e_1_2_1_67_1","volume-title":"Markus Weimer, and Matteo Interlandi.","author":"Nakandala Supun","year":"2020","unstructured":"Supun Nakandala , Karla Saur , Gyeong-In Yu , Konstantinos Karanasos , Carlo Curino , Markus Weimer, and Matteo Interlandi. 2020 . A Tensor Compiler for Unified Machine Learning Prediction Serving. In OSDI. OSDI , 899--917. Supun Nakandala, Karla Saur, Gyeong-In Yu, Konstantinos Karanasos, Carlo Curino, Markus Weimer, and Matteo Interlandi. 2020. A Tensor Compiler for Unified Machine Learning Prediction Serving. In OSDI. OSDI, 899--917."},{"key":"e_1_2_1_68_1","volume-title":"Proceedings of the 27th ACM Symposium on Operating Systems Principles (Huntsville","author":"Narayanan Deepak","year":"2019","unstructured":"Deepak Narayanan , Aaron Harlap , Amar Phanishayee , Vivek Seshadri , Nikhil R. Devanur , Gregory R. Ganger , Phillip B. Gibbons , and Matei Zaharia . 2019 . PipeDream: Generalized Pipeline Parallelism for DNN Training . In Proceedings of the 27th ACM Symposium on Operating Systems Principles (Huntsville , Ontario, Canada) (SOSP '19). Association for Computing Machinery, New York, NY, USA, 1--15. 10.1145\/3341301.3359646 Deepak Narayanan, Aaron Harlap, Amar Phanishayee, Vivek Seshadri, Nikhil R. Devanur, Gregory R. Ganger, Phillip B. Gibbons, and Matei Zaharia. 2019. PipeDream: Generalized Pipeline Parallelism for DNN Training. In Proceedings of the 27th ACM Symposium on Operating Systems Principles (Huntsville, Ontario, Canada) (SOSP '19). Association for Computing Machinery, New York, NY, USA, 1--15. 10.1145\/3341301.3359646"},{"key":"e_1_2_1_69_1","unstructured":"Henry Neeb and Christopher Kurrus. 2016. Distributed k-nearest neighbors. Henry Neeb and Christopher Kurrus. 2016. Distributed k-nearest neighbors."},{"key":"e_1_2_1_70_1","unstructured":"Graham Neubig Yoav Goldberg and Chris Dyer. 2017. On-the-fly Operation Batching in Dynamic Computation Graphs. In NeurIPS. 3971--3981. Graham Neubig Yoav Goldberg and Chris Dyer. 2017. On-the-fly Operation Batching in Dynamic Computation Graphs. In NeurIPS. 3971--3981."},{"key":"e_1_2_1_71_1","volume-title":"IEEE International Workshop on Computational Intelligence and Applications (IWCIA). IEEE, 65--70","author":"Oku Junki","year":"2014","unstructured":"Junki Oku , Keiichi Tamura , and Hajime Kitakami . 2014 . Parallel processing for distance-based outlier detection on a multi-core CPU . In IEEE International Workshop on Computational Intelligence and Applications (IWCIA). IEEE, 65--70 . Junki Oku, Keiichi Tamura, and Hajime Kitakami. 2014. Parallel processing for distance-based outlier detection on a multi-core CPU. In IEEE International Workshop on Computational Intelligence and Applications (IWCIA). IEEE, 65--70."},{"key":"e_1_2_1_72_1","first-page":"1","article-title":"Distance-based outlier detection: consolidation and renewed bearing","volume":"3","author":"Orair Gustavo H","year":"2010","unstructured":"Gustavo H Orair , Carlos HC Teixeira , Wagner Meira Jr , Ye Wang , and Srinivasan Parthasarathy . 2010 . Distance-based outlier detection: consolidation and renewed bearing . VLDB 3 , 1 -- 2 (2010), 1469--1480. Gustavo H Orair, Carlos HC Teixeira, Wagner Meira Jr, Ye Wang, and Srinivasan Parthasarathy. 2010. Distance-based outlier detection: consolidation and renewed bearing. VLDB 3, 1--2 (2010), 1469--1480.","journal-title":"VLDB"},{"key":"e_1_2_1_73_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-005-0014-6"},{"key":"e_1_2_1_74_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3439950","article-title":"Deep learning for anomaly detection: A review","volume":"54","author":"Pang Guansong","year":"2021","unstructured":"Guansong Pang , Chunhua Shen , Longbing Cao , and Anton Van Den Hengel . 2021 . Deep learning for anomaly detection: A review . CSUR 54 , 2 (2021), 1 -- 38 . Guansong Pang, Chunhua Shen, Longbing Cao, and Anton Van Den Hengel. 2021. Deep learning for anomaly detection: A review. CSUR 54, 2 (2021), 1--38.","journal-title":"CSUR"},{"key":"e_1_2_1_75_1","volume-title":"LOCI: Fast Outlier Detection Using the Local Correlation Integral","author":"Papadimitriou Spiros","year":"2003","unstructured":"Spiros Papadimitriou , Hiroyuki Kitagawa , Phillip B. Gibbons , and Christos Faloutsos . 2003 . LOCI: Fast Outlier Detection Using the Local Correlation Integral . In ICDE. IEEE Computer Society , 315--326. Spiros Papadimitriou, Hiroyuki Kitagawa, Phillip B. Gibbons, and Christos Faloutsos. 2003. LOCI: Fast Outlier Detection Using the Local Correlation Integral. In ICDE. IEEE Computer Society, 315--326."},{"key":"e_1_2_1_76_1","first-page":"8026","article-title":"Pytorch: An imperative style, high-performance deep learning library","volume":"32","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 , 2019 . Pytorch: An imperative style, high-performance deep learning library . NeuIPS 32 (2019), 8026 -- 8037 . Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. NeuIPS 32 (2019), 8026--8037.","journal-title":"NeuIPS"},{"key":"e_1_2_1_77_1","volume-title":"Scikit-learn: Machine learning in Python. the Journal of machine Learning research 12","author":"Pedregosa Fabian","year":"2011","unstructured":"Fabian Pedregosa , Ga\u00ebl Varoquaux , Alexandre Gramfort , Vincent Michel , Bertrand Thirion , Olivier Grisel , Mathieu Blondel , Peter Prettenhofer , Ron Weiss , Vincent Dubourg , 2011 . Scikit-learn: Machine learning in Python. the Journal of machine Learning research 12 (2011), 2825--2830. Fabian Pedregosa, Ga\u00ebl Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al. 2011. Scikit-learn: Machine learning in Python. the Journal of machine Learning research 12 (2011), 2825--2830."},{"key":"e_1_2_1_78_1","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1007\/s10994-015-5521-0","article-title":"Loda: Lightweight on-line detector of anomalies","volume":"102","author":"Pevn\u00fd Tom\u00e1s","year":"2016","unstructured":"Tom\u00e1s Pevn\u00fd . 2016 . Loda: Lightweight on-line detector of anomalies . Mach. Learn. 102 , 2 (2016), 275 -- 304 . Tom\u00e1s Pevn\u00fd. 2016. Loda: Lightweight on-line detector of anomalies. Mach. Learn. 102, 2 (2016), 275--304.","journal-title":"Mach. Learn."},{"key":"e_1_2_1_79_1","volume-title":"Python machine learning","author":"Raschka Sebastian","unstructured":"Sebastian Raschka . 2015. Python machine learning . Packt publishing ltd. Sebastian Raschka. 2015. Python machine learning. Packt publishing ltd."},{"key":"e_1_2_1_80_1","volume-title":"Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining. ACM, 3009--3017","author":"Ren Hansheng","year":"2019","unstructured":"Hansheng Ren , Bixiong Xu , Yujing Wang , Chao Yi , Congrui Huang , Xiaoyu Kou , Tony Xing , Mao Yang , Jie Tong , and Qi Zhang . 2019 . Time-series anomaly detection service at microsoft . In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining. ACM, 3009--3017 . Hansheng Ren, Bixiong Xu, Yujing Wang, Chao Yi, Congrui Huang, Xiaoyu Kou, Tony Xing, Mao Yang, Jie Tong, and Qi Zhang. 2019. Time-series anomaly detection service at microsoft. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining. ACM, 3009--3017."},{"key":"e_1_2_1_81_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.websem.2015.06.004"},{"key":"e_1_2_1_82_1","volume-title":"Deep Semi-Supervised Anomaly Detection. In 8th International Conference on Learning Representations, ICLR 2020","author":"Ruff Lukas","year":"2020","unstructured":"Lukas Ruff , Robert A. Vandermeulen , Nico G\u00f6rnitz , Alexander Binder , Emmanuel M\u00fcller , Klaus-Robert M\u00fcller , and Marius Kloft . 2020 . Deep Semi-Supervised Anomaly Detection. In 8th International Conference on Learning Representations, ICLR 2020 , Addis Ababa, Ethiopia, April 26--30 , 2020. OpenReview.net. https:\/\/openreview.net\/forum?id=HkgH0TEYwH Lukas Ruff, Robert A. Vandermeulen, Nico G\u00f6rnitz, Alexander Binder, Emmanuel M\u00fcller, Klaus-Robert M\u00fcller, and Marius Kloft. 2020. Deep Semi-Supervised Anomaly Detection. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26--30, 2020. OpenReview.net. https:\/\/openreview.net\/forum?id=HkgH0TEYwH"},{"key":"e_1_2_1_83_1","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.1162\/089976601750264965","article-title":"Estimating the Support of a High-Dimensional Distribution","volume":"13","author":"Sch\u00f6lkopf Bernhard","year":"2001","unstructured":"Bernhard Sch\u00f6lkopf , John C. Platt , John Shawe-Taylor , Alexander J. Smola , and Robert C. Williamson . 2001 . Estimating the Support of a High-Dimensional Distribution . Neural Comput. 13 , 7 (2001), 1443 -- 1471 . Bernhard Sch\u00f6lkopf, John C. Platt, John Shawe-Taylor, Alexander J. Smola, and Robert C. Williamson. 2001. Estimating the Support of a High-Dimensional Distribution. Neural Comput. 13, 7 (2001), 1443--1471.","journal-title":"Neural Comput."},{"key":"e_1_2_1_84_1","volume-title":"Generalized Outlier Detection with Flexible Kernel Density Estimates","author":"Schubert Erich","unstructured":"Erich Schubert , Arthur Zimek , and Hans-Peter Kriegel . 2014. Generalized Outlier Detection with Flexible Kernel Density Estimates . In SDM. SIAM , 542--550. Erich Schubert, Arthur Zimek, and Hans-Peter Kriegel. 2014. Generalized Outlier Detection with Flexible Kernel Density Estimates. In SDM. SIAM, 542--550."},{"key":"e_1_2_1_86_1","unstructured":"Petru Sincraian. 2021. PyOD Download Statistics. https:\/\/pepy.tech\/project\/pyod. Accessed: 2021-09-09. Petru Sincraian. 2021. PyOD Download Statistics. https:\/\/pepy.tech\/project\/pyod. Accessed: 2021-09-09."},{"key":"e_1_2_1_87_1","first-page":"1","article-title":"Benchmarking the Nvidia GPU Lineage: From Early K80 to Modern A100 with Asynchronous Memory Transfers. In HEART '21","volume":"9","author":"Svedin Martin","year":"2021","unstructured":"Martin Svedin , Steven Wei Der Chien , Gibson Chikafa , Niclas Jansson , and Artur Podobas . 2021 . Benchmarking the Nvidia GPU Lineage: From Early K80 to Modern A100 with Asynchronous Memory Transfers. In HEART '21 . ACM , 9 : 1 -- 9 :6. Martin Svedin, Steven Wei Der Chien, Gibson Chikafa, Niclas Jansson, and Artur Podobas. 2021. Benchmarking the Nvidia GPU Lineage: From Early K80 to Modern A100 with Asynchronous Memory Transfers. In HEART '21. ACM, 9:1--9:6.","journal-title":"ACM"},{"key":"e_1_2_1_88_1","first-page":"1016","article-title":"From anomaly detection to rumour detection using data streams of social platforms","volume":"12","author":"Tam Nguyen Thanh","year":"2019","unstructured":"Nguyen Thanh Tam , Matthias Weidlich , Bolong Zheng , Hongzhi Yin , Nguyen Quoc Viet Hung , and Bela Stantic . 2019 . From anomaly detection to rumour detection using data streams of social platforms . VLDB 12 , 9 (2019), 1016 -- 1029 . Nguyen Thanh Tam, Matthias Weidlich, Bolong Zheng, Hongzhi Yin, Nguyen Quoc Viet Hung, and Bela Stantic. 2019. From anomaly detection to rumour detection using data streams of social platforms. VLDB 12, 9 (2019), 1016--1029.","journal-title":"VLDB"},{"key":"e_1_2_1_89_1","volume-title":"Ada Wai-Chee Fu, and David Wai-Lok Cheung","author":"Tang Jian","year":"2002","unstructured":"Jian Tang , Zhixiang Chen , Ada Wai-Chee Fu, and David Wai-Lok Cheung . 2002 . Enhancing Effectiveness of Outlier Detections for Low Density Patterns. In PAKDD (Lecture Notes in Computer Science), Vol. 2336 . Springer , 535--548. Jian Tang, Zhixiang Chen, Ada Wai-Chee Fu, and David Wai-Lok Cheung. 2002. Enhancing Effectiveness of Outlier Detections for Low Density Patterns. In PAKDD (Lecture Notes in Computer Science), Vol. 2336. Springer, 535--548."},{"key":"e_1_2_1_90_1","volume-title":"Proceedings of the 2020 International Conference on Management of Data, SIGMOD Conference 2020, online conference [Portland, OR, USA], June 14--19","author":"Toliopoulos Theodoros","year":"2020","unstructured":"Theodoros Toliopoulos , Christos Bellas , Anastasios Gounaris , and Apostolos Papadopoulos . 2020 . PROUD: PaRallel OUtlier Detection for Streams . In Proceedings of the 2020 International Conference on Management of Data, SIGMOD Conference 2020, online conference [Portland, OR, USA], June 14--19 , 2020, David Maier, Rachel Pottinger, AnHai Doan, Wang-Chiew Tan, Abdussalam Alawini, and Hung Q. Ngo (Eds.). ACM, 2717--2720. 10.1145\/33 18464.3384688 Theodoros Toliopoulos, Christos Bellas, Anastasios Gounaris, and Apostolos Papadopoulos. 2020. PROUD: PaRallel OUtlier Detection for Streams. In Proceedings of the 2020 International Conference on Management of Data, SIGMOD Conference 2020, online conference [Portland, OR, USA], June 14--19, 2020, David Maier, Rachel Pottinger, AnHai Doan, Wang-Chiew Tan, Abdussalam Alawini, and Hung Q. Ngo (Eds.). ACM, 2717--2720. 10.1145\/3318464.3384688"},{"key":"e_1_2_1_91_1","first-page":"141","article-title":"Real-time distance-based outlier detection in data streams","volume":"14","author":"Tran Luan","year":"2020","unstructured":"Luan Tran , Min Y Mun , and Cyrus Shahabi . 2020 . Real-time distance-based outlier detection in data streams . VLDB 14 , 2 (2020), 141 -- 153 . Luan Tran, Min Y Mun, and Cyrus Shahabi. 2020. Real-time distance-based outlier detection in data streams. VLDB 14, 2 (2020), 141--153.","journal-title":"VLDB"},{"key":"e_1_2_1_92_1","volume-title":"Unity: Accelerating DNN Training Through Joint Optimization of Algebraic Transformations and Parallelization. In 16th USENIX Symposium on Operating Systems Design and Implementation (OSDI 22)","author":"Unger Colin","year":"2022","unstructured":"Colin Unger , Zhihao Jia , Wei Wu , Sina Lin , Mandeep Baines , Carlos Efrain Quintero Narvaez , Vinay Ramakrishnaiah , Nirmal Prajapati , Pat McCormick , Jamaludin Mohd-Yusof , Xi Luo , Dheevatsa Mudigere , Jongsoo Park , Misha Smelyanskiy , and Alex Aiken . 2022 . Unity: Accelerating DNN Training Through Joint Optimization of Algebraic Transformations and Parallelization. In 16th USENIX Symposium on Operating Systems Design and Implementation (OSDI 22) . USENIX Association, Carlsbad, CA, 267--284. https:\/\/www.usenix.org\/conference\/osdi22\/presentation\/unger Colin Unger, Zhihao Jia, Wei Wu, Sina Lin, Mandeep Baines, Carlos Efrain Quintero Narvaez, Vinay Ramakrishnaiah, Nirmal Prajapati, Pat McCormick, Jamaludin Mohd-Yusof, Xi Luo, Dheevatsa Mudigere, Jongsoo Park, Misha Smelyanskiy, and Alex Aiken. 2022. Unity: Accelerating DNN Training Through Joint Optimization of Algebraic Transformations and Parallelization. In 16th USENIX Symposium on Operating Systems Design and Implementation (OSDI 22). USENIX Association, Carlsbad, CA, 267--284. https:\/\/www.usenix.org\/conference\/osdi22\/presentation\/unger"},{"key":"e_1_2_1_93_1","volume-title":"PET: Optimizing Tensor Programs with Partially Equivalent Transformations and Automated Corrections. In OSDI. OSDI, 37--54.","author":"Wang Haojie","year":"2021","unstructured":"Haojie Wang , Jidong Zhai , Mingyu Gao , Zixuan Ma , Shizhi Tang , Liyan Zheng , Yuanzhi Li , Kaiyuan Rong , Yuanyong Chen , and Zhihao Jia . 2021 . PET: Optimizing Tensor Programs with Partially Equivalent Transformations and Automated Corrections. In OSDI. OSDI, 37--54. Haojie Wang, Jidong Zhai, Mingyu Gao, Zixuan Ma, Shizhi Tang, Liyan Zheng, Yuanzhi Li, Kaiyuan Rong, Yuanyong Chen, and Zhihao Jia. 2021. PET: Optimizing Tensor Programs with Partially Equivalent Transformations and Automated Corrections. In OSDI. OSDI, 37--54."},{"key":"e_1_2_1_94_1","doi-asserted-by":"publisher","DOI":"10.14778\/3476249.3476255"},{"key":"e_1_2_1_95_1","first-page":"3603","article-title":"DeltaPQ: lossless product quantization code compression for high dimensional similarity search","volume":"13","author":"Wang Runhui","year":"2020","unstructured":"Runhui Wang and Dong Deng . 2020 . DeltaPQ: lossless product quantization code compression for high dimensional similarity search . VLDB 13 , 13 (2020), 3603 -- 3616 . Runhui Wang and Dong Deng. 2020. DeltaPQ: lossless product quantization code compression for high dimensional similarity search. VLDB 13, 13 (2020), 3603--3616.","journal-title":"VLDB"},{"key":"e_1_2_1_96_1","first-page":"215","article-title":"PPQ-trajectory: spatio-temporal quantization for querying in large trajectory repositories","volume":"14","author":"Wang Shuang","year":"2020","unstructured":"Shuang Wang and Hakan Ferhatosmanoglu . 2020 . PPQ-trajectory: spatio-temporal quantization for querying in large trajectory repositories . VLDB 14 , 2 (2020), 215 -- 227 . Shuang Wang and Hakan Ferhatosmanoglu. 2020. PPQ-trajectory: spatio-temporal quantization for querying in large trajectory repositories. VLDB 14, 2 (2020), 215--227.","journal-title":"VLDB"},{"key":"e_1_2_1_97_1","unstructured":"Yizhou Yan Lei Cao Caitlin Kulhman and Elke Rundensteiner. 2017. Distributed local outlier detection in big data. In KDD. ACM 1225--1234. Yizhou Yan Lei Cao Caitlin Kulhman and Elke Rundensteiner. 2017. Distributed local outlier detection in big data. In KDD. ACM 1225--1234."},{"key":"e_1_2_1_98_1","first-page":"1303","article-title":"NETS: extremely fast outlier detection from a data stream via set-based processing","volume":"12","author":"Yoon Susik","year":"2019","unstructured":"Susik Yoon , Jae-Gil Lee , and Byung Suk Lee . 2019 . NETS: extremely fast outlier detection from a data stream via set-based processing . VLDB 12 , 11 (2019), 1303 -- 1315 . Susik Yoon, Jae-Gil Lee, and Byung Suk Lee. 2019. NETS: extremely fast outlier detection from a data stream via set-based processing. VLDB 12, 11 (2019), 1303--1315.","journal-title":"VLDB"},{"key":"e_1_2_1_99_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2980765.2980767","article-title":"A survey on social media anomaly detection","volume":"18","author":"Yu Rose","year":"2016","unstructured":"Rose Yu , Huida Qiu , Zhen Wen , ChingYung Lin , and Yan Liu . 2016 . A survey on social media anomaly detection . SIGKDD Explorations 18 (2016), 1 -- 14 . Rose Yu, Huida Qiu, Zhen Wen, ChingYung Lin, and Yan Liu. 2016. A survey on social media anomaly detection. SIGKDD Explorations 18 (2016), 1--14.","journal-title":"SIGKDD Explorations"},{"key":"e_1_2_1_100_1","first-page":"1046","article-title":"Time series data cleaning: From anomaly detection to anomaly repairing","volume":"10","author":"Zhang Aoqian","year":"2017","unstructured":"Aoqian Zhang , Shaoxu Song , Jianmin Wang , and Philip S Yu . 2017 . Time series data cleaning: From anomaly detection to anomaly repairing . VLDB 10 , 10 (2017), 1046 -- 1057 . Aoqian Zhang, Shaoxu Song, Jianmin Wang, and Philip S Yu. 2017. Time series data cleaning: From anomaly detection to anomaly repairing. VLDB 10, 10 (2017), 1046--1057.","journal-title":"VLDB"},{"key":"e_1_2_1_101_1","volume-title":"Sparx: Distributed Outlier Detection at Scale. arXiv preprint arXiv:2206.01281","author":"Zhang Sean","year":"2022","unstructured":"Sean Zhang , Varun Ursekar , and Leman Akoglu . 2022 . Sparx: Distributed Outlier Detection at Scale. arXiv preprint arXiv:2206.01281 (2022). Sean Zhang, Varun Ursekar, and Leman Akoglu. 2022. Sparx: Distributed Outlier Detection at Scale. arXiv preprint arXiv:2206.01281 (2022)."},{"key":"e_1_2_1_102_1","unstructured":"Yue Zhao. 2021. PyOD Citation Statistics. https:\/\/scholar.google.ca\/scholar?cites=3726241381117726876&as_sdt=5 39&sciodt=0 39&hl=en. Accessed: 2021-09-09. Yue Zhao. 2021. PyOD Citation Statistics. https:\/\/scholar.google.ca\/scholar?cites=3726241381117726876&as_sdt=5 39&sciodt=0 39&hl=en. Accessed: 2021-09-09."},{"key":"e_1_2_1_103_1","volume-title":"Proceedings of Machine Learning and Systems 2021","author":"Zhao Yue","year":"2021","unstructured":"Yue Zhao , Xiyang Hu , Cheng Cheng , Cong Wang , Changlin Wan , Wen Wang , Jianing Yang , Haoping Bai , Zheng Li , Cao Xiao , Yunlong Wang , Zhi Qiao , Jimeng Sun , and Leman Akoglu . 2021 . SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection . In Proceedings of Machine Learning and Systems 2021 , MLSys 2021, virtual, April 5 --9 , 2021, Alex Smola, Alex Dimakis, and Ion Stoica (Eds.). mlsys.org. https:\/\/proceedings.mlsys.org\/paper\/2021\/hash\/98dce83da57b0395e163467c9dae521b-Abstract.html Yue Zhao, Xiyang Hu, Cheng Cheng, Cong Wang, Changlin Wan, Wen Wang, Jianing Yang, Haoping Bai, Zheng Li, Cao Xiao, Yunlong Wang, Zhi Qiao, Jimeng Sun, and Leman Akoglu. 2021. SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection. In Proceedings of Machine Learning and Systems 2021, MLSys 2021, virtual, April 5--9, 2021, Alex Smola, Alex Dimakis, and Ion Stoica (Eds.). mlsys.org. https:\/\/proceedings.mlsys.org\/paper\/2021\/hash\/98dce83da57b0395e163467c9dae521b-Abstract.html"},{"key":"e_1_2_1_104_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611975673.66"},{"key":"e_1_2_1_105_1","volume-title":"PyOD: A Python Toolbox for Scalable Outlier Detection. JMLR 20","author":"Zhao Yue","year":"2019","unstructured":"Yue Zhao , Zain Nasrullah , and Zheng Li. 2019. PyOD: A Python Toolbox for Scalable Outlier Detection. JMLR 20 ( 2019 ), 96:1--96:7. Yue Zhao, Zain Nasrullah, and Zheng Li. 2019. PyOD: A Python Toolbox for Scalable Outlier Detection. JMLR 20 (2019), 96:1--96:7."},{"key":"e_1_2_1_106_1","doi-asserted-by":"crossref","unstructured":"Qiwei Zhong Yang Liu Xiang Ao Binbin Hu Jinghua Feng Jiayu Tang and Qing He. 2020. Financial defaulter detection on online credit payment via multi-view attributed heterogeneous information network. In WWW. ACM 785--795. Qiwei Zhong Yang Liu Xiang Ao Binbin Hu Jinghua Feng Jiayu Tang and Qing He. 2020. Financial defaulter detection on online credit payment via multi-view attributed heterogeneous information network. In WWW. ACM 785--795.","DOI":"10.1145\/3366423.3380159"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3570690.3570703","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,12]],"date-time":"2024-10-12T20:16:26Z","timestamp":1728764186000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3570690.3570703"}},"subtitle":["GPU-Accelerated Outlier Detection via Tensor Operations"],"short-title":[],"issued":{"date-parts":[[2022,11]]},"references-count":105,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2022,11]]}},"alternative-id":["10.14778\/3570690.3570703"],"URL":"https:\/\/doi.org\/10.14778\/3570690.3570703","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2022,11]]},"assertion":[{"value":"2023-01-23","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}