{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T00:08:15Z","timestamp":1755907695090,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":69,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T00:00:00Z","timestamp":1731542400000},"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":[[2024,11,14]]},"DOI":"10.1145\/3677052.3698663","type":"proceedings-article","created":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T06:38:06Z","timestamp":1731566286000},"page":"10-18","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Generational Knowledge Transfer for Model Robustness &amp; Agility: Label Augmentation for Time-Sensitive Financial Services Applications"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-5002-272X","authenticated-orcid":false,"given":"Hongda","family":"Shen","sequence":"first","affiliation":[{"name":"eBay, United States"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-8016-5563","authenticated-orcid":false,"given":"Eren","family":"Kurshan","sequence":"additional","affiliation":[{"name":"Princeton University, United States"}]}],"member":"320","published-online":{"date-parts":[[2024,11,14]]},"reference":[{"key":"e_1_3_2_1_1_1","first-page":"1","article-title":"Anomaly Detection in Finance: Editors\u2019 Introduction","volume":"71","author":"Anandakrishnan Archana","year":"2017","unstructured":"Archana Anandakrishnan, Senthil Kumar, Alexander Statnikov, Tanveer Faruquie, and Di Xu. 2017. Anomaly Detection in Finance: Editors\u2019 Introduction. Proceedings of Machine Learning Research 71:1\u20137, 2017 KDD 2017: Workshop on Anomaly Detection in Finance (2017).","journal-title":"Proceedings of Machine Learning Research"},{"key":"e_1_3_2_1_2_1","volume-title":"Proceedings of the KDD 2017: Workshop on Anomaly Detection in Finance(Proceedings of Machine Learning Research, Vol.\u00a071)","author":"Anandakrishnan Archana","year":"2018","unstructured":"Archana Anandakrishnan, Senthil Kumar, Alexander Statnikov, Tanveer Faruquie, and Di Xu. 2018. Anomaly Detection in Finance: Editors\u2019 Introduction. In Proceedings of the KDD 2017: Workshop on Anomaly Detection in Finance(Proceedings of Machine Learning Research, Vol.\u00a071). PMLR, 1\u20137."},{"key":"e_1_3_2_1_3_1","volume-title":"MLOps: DevOps for Machine Learning. Towards DataScience","author":"Shah A.","year":"2020","unstructured":"A.Shah. 2020. Challenges Deploying Machine Learning Models to Production, MLOps: DevOps for Machine Learning. Towards DataScience (2020). https:\/\/towardsdatascience.com\/challenges-deploying-machine-learning-models-to-production-ded3f9009cb3"},{"key":"e_1_3_2_1_4_1","unstructured":"Jimmy Ba and Rich Caruana. 2014. Do Deep Nets Really Need to be Deep? In Advances in Neural Information Processing Systems 27. 2654\u20132662."},{"key":"e_1_3_2_1_5_1","volume-title":"Label Refinery: Improving ImageNet Classification through Label Progression. CoRR abs\/1805.02641","author":"Bagherinezhad Hessam","year":"2018","unstructured":"Hessam Bagherinezhad, Maxwell Horton, Mohammad Rastegari, and Ali Farhadi. 2018. Label Refinery: Improving ImageNet Classification through Label Progression. CoRR abs\/1805.02641 (2018)."},{"key":"e_1_3_2_1_6_1","volume-title":"Model Compression. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Bucilu\u01ce Cristian","year":"2006","unstructured":"Cristian Bucilu\u01ce, Rich Caruana, and Alexandru Niculescu-Mizil. 2006. Model Compression. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Philadelphia, PA, USA) (KDD \u201906). 535\u2013541."},{"key":"e_1_3_2_1_7_1","volume-title":"Transforming Approaches to AML and Financial Crime. McKinsey","author":"Buehler K.","year":"2019","unstructured":"K. Buehler. 2019. Transforming Approaches to AML and Financial Crime. McKinsey (2019)."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1596\/978-0-8213-8669-9"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11728"},{"key":"e_1_3_2_1_10_1","volume-title":"Cross-Layer Distillation with Semantic Calibration. CoRR abs\/2012.03236","author":"Chen Defang","year":"2020","unstructured":"Defang Chen, Jian-Ping Mei, Yuan Zhang, Can Wang, Zhe Wang, Yan Feng, and Chun Chen. 2020. Cross-Layer Distillation with Semantic Calibration. CoRR abs\/2012.03236 (2020)."},{"key":"e_1_3_2_1_11_1","unstructured":"Guobin Chen Wongun Choi Xiang Yuan Tony Han and Manmohan Chandraker. 2017. Learning Efficient Object Detection Models with Knowledge Distillation. In Advances in Neural Information Processing Systems 30."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_3_2_1_13_1","volume-title":"Explore Data. FTC Portal","author":"Federal\u00a0Trade Commission","year":"2024","unstructured":"Federal\u00a0Trade Commission. 2024. Explore Data. FTC Portal (2024). https:\/\/www.ftc.gov\/news-events\/data-visualizations\/explore-data"},{"key":"e_1_3_2_1_14_1","volume-title":"Payment Methods Report 2019: Innovations in the Way We Pay","author":"European\u00a0Payments Council","year":"2019","unstructured":"European\u00a0Payments Council. 2019. Payment Methods Report 2019: Innovations in the Way We Pay. E.U. Payments Council Report (2019)."},{"key":"e_1_3_2_1_15_1","volume-title":"Quantizations, Memory Optimizations and More. MLSys","author":"Daghaghi Shabnam","year":"2021","unstructured":"Shabnam Daghaghi, Nicholas Meisburger, Mengnan Zhao, Yong Wu, Sameh Gobriel, Charlie Tai, and Anshumali Shrivastava. 2021. Accelerating Slide Deep Learning on Modern CPUs: Vectorization, Quantizations, Memory Optimizations and More. MLSys (2021)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/1143844.1143874"},{"key":"e_1_3_2_1_17_1","volume-title":"Improved Regularization of Convolutional Neural Networks with Cutout. CoRR abs\/1708.04552","author":"Devries Terrance","year":"2017","unstructured":"Terrance Devries and Graham\u00a0W. Taylor. 2017. Improved Regularization of Convolutional Neural Networks with Cutout. CoRR abs\/1708.04552 (2017)."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1142\/S1793351X20300022"},{"key":"e_1_3_2_1_19_1","volume-title":"International Conference on Transdisciplinary AI (TransAI) (pp. 125-130)","author":"Kurshan E.","year":"2020","unstructured":"E.Kurshan, H. Shen, and H. Yu. 2020. Financial crime & fraud detection using graph computing: Application considerations & outlook. International Conference on Transdisciplinary AI (TransAI) (pp. 125-130). IEEE (2020)."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1147\/JRD.2019.2947008"},{"key":"e_1_3_2_1_21_1","volume-title":"TKD: Temporal Knowledge Distillation for Active Perception. CoRR abs\/1903.01522","author":"Farhadi Mohammad","year":"2019","unstructured":"Mohammad Farhadi and Yezhou Yang. 2019. TKD: Temporal Knowledge Distillation for Active Perception. CoRR abs\/1903.01522 (2019)."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106883"},{"key":"e_1_3_2_1_23_1","volume-title":"Transforming Paradigms: A Global AI in Financial Services Survey.","author":"Forum World\u00a0Economic","year":"2020","unstructured":"World\u00a0Economic Forum. 2020. Transforming Paradigms: A Global AI in Financial Services Survey."},{"key":"e_1_3_2_1_24_1","volume-title":"Born Again Neural Networks. CoRR abs\/1805.04770","author":"Furlanello Tommaso","year":"2018","unstructured":"Tommaso Furlanello, Zachary\u00a0C. Lipton, Michael Tschannen, Laurent Itti, and Anima Anandkumar. 2018. Born Again Neural Networks. CoRR abs\/1805.04770 (2018)."},{"key":"e_1_3_2_1_25_1","volume-title":"Knowledge Distillation: A Survey. CoRR abs\/2006.05525","author":"Gou Jianping","year":"2020","unstructured":"Jianping Gou, Baosheng Yu, Stephen\u00a0John Maybank, and Dacheng Tao. 2020. Knowledge Distillation: A Survey. CoRR abs\/2006.05525 (2020)."},{"key":"e_1_3_2_1_26_1","unstructured":"Yuxian Gu Li Dong Furu Wei and Minlie Huang. 2024. MiniLLM: Knowledge Distillation of Large Language Models. arXiv:2306.08543"},{"key":"e_1_3_2_1_27_1","volume-title":"Sheikh\u00a0Musa Kaleem, Tufail Rouf, and Brejesh Lall.","author":"Habib Gousia","year":"2024","unstructured":"Gousia Habib, Tausifa jan Saleem, Sheikh\u00a0Musa Kaleem, Tufail Rouf, and Brejesh Lall. 2024. A Comprehensive Review of Knowledge Distillation in Computer Vision. arXiv:2404.00936"},{"key":"e_1_3_2_1_28_1","volume-title":"Ecommerce Account Takeover Fraud Jumps to 378% Since the Start of COVID-19 Pandemic. The Fintech Times","author":"Harrison P.","year":"2020","unstructured":"P. Harrison. 2020. Ecommerce Account Takeover Fraud Jumps to 378% Since the Start of COVID-19 Pandemic. The Fintech Times (2020). https:\/\/thefintechtimes.com\/ecommerce-account-takeover-fraud-jumps-to-378-since-the-start-of-covid-19-pandemic\/"},{"key":"e_1_3_2_1_29_1","unstructured":"S. Hasham S. Joshi and D. Mikkelsen. 2019. Financial Crime and Fraud in the Age of Cybersecurity. McKinsey and Company Report (2019)."},{"key":"e_1_3_2_1_30_1","unstructured":"J.\u00a0B. Heaton Nicholas\u00a0G. Polson and J.\u00a0H. Witte. 2016. Deep Learning in Finance. (2016). arxiv:1602.06561"},{"key":"e_1_3_2_1_31_1","volume-title":"Small Businesses Fueling Zelle\u2019s Growth. American Banker","author":"Heun David","year":"2021","unstructured":"David Heun. 2021. Small Businesses Fueling Zelle\u2019s Growth. American Banker (2021). https:\/\/www.americanbanker.com\/news\/small-businesses-fueling-zelles-growth"},{"key":"e_1_3_2_1_32_1","volume-title":"Distilling the Knowledge in a Neural Network. CoRR abs\/1503.02531","author":"Hinton Geoffrey","year":"2015","unstructured":"Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2015. Distilling the Knowledge in a Neural Network. CoRR abs\/1503.02531 (2015)."},{"key":"e_1_3_2_1_33_1","volume-title":"Label augmentation via time-based knowledge distillation for financial anomaly detection. arXiv preprint arXiv:2101.01689","author":"Shen H.","year":"2021","unstructured":"H.Shen and E.Kurshan. 2021. Label augmentation via time-based knowledge distillation for financial anomaly detection. arXiv preprint arXiv:2101.01689 (2021)."},{"key":"e_1_3_2_1_34_1","unstructured":"IEEE Computational Intelligence Society. 2019. Fraud Detection Competition. https:\/\/www.kaggle.com\/c\/ieee-fraud-detection."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW53098.2021.00111"},{"key":"e_1_3_2_1_36_1","volume-title":"From Active Learning to Reward Maximization. CoRR abs\/1811.08212","author":"Marfaing Christelle","year":"2018","unstructured":"Christelle Marfaing and Alexandre Garcia. 2018. Computer-Assisted Fraud Detection, From Active Learning to Reward Maximization. CoRR abs\/1811.08212 (2018)."},{"volume-title":"E-commerce Fraud Trends and Statistics Merchants Need to Know","year":"2024","key":"e_1_3_2_1_37_1","unstructured":"Mastercard. 2024. E-commerce Fraud Trends and Statistics Merchants Need to Know in 2024. Mastercard News and Insights (2024). https:\/\/b2b.mastercard.com\/news-and-insights\/blog\/ecommerce-fraud-trends-and-statistics-merchants-need-to-know-in-2024\/"},{"key":"e_1_3_2_1_38_1","volume-title":"Regulatory Alert - Fraud, Identity Theft and Other Scams. KPMG Regulatory Insights Report","author":"Matsuo A.","year":"2024","unstructured":"A. Matsuo. 2024. Regulatory Alert - Fraud, Identity Theft and Other Scams. KPMG Regulatory Insights Report (2024). https:\/\/kpmg.com\/us\/en\/articles\/2024\/fraud-identity-theft-and-other-scams-reg-alert.html"},{"key":"e_1_3_2_1_39_1","volume-title":"On the Cusp of the Next Payments Era: Future Opportunities for Banks. McKinsey Report","author":"Company McKinsey","year":"2023","unstructured":"McKinsey and Company. 2023. On the Cusp of the Next Payments Era: Future Opportunities for Banks. McKinsey Report (2023). https:\/\/www.mckinsey.com\/industries\/financial-services\/our-insights\/the-2023-mckinsey-global-payments-report"},{"key":"e_1_3_2_1_40_1","first-page":"2592","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Forrest","year":"2016","unstructured":"Forrest N, Khalid\u00a0Ashraf Iandola, Matthew W.and\u00a0Moskewicz, and Kurt Keutzer;. 2016. FireCaffe: Near-Linear Acceleration of Deep Neural Network Training on Compute Clusters. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2592-2600 (2016)."},{"key":"e_1_3_2_1_41_1","volume-title":"NASDAQ 2024: Global Financial Crime Report, Insights at the Intersection of Financial Crime Data & Real Survivor Stories. NASDAQ (2024","author":"Verafin NASDAQ","year":"2024","unstructured":"NASDAQ and Verafin. 2024. NASDAQ 2024: Global Financial Crime Report, Insights at the Intersection of Financial Crime Data & Real Survivor Stories. NASDAQ (2024). https:\/\/nd.nasdaq.com\/rs\/303-QKM-463\/images\/2024-Global-Financial-Crime-Report-Nasdaq-Verafin-20240115.pdf"},{"volume-title":"NeurIPS 2019 Workshop on Robust AI in Financial Services: Data, Fairness, Explainability, Trustworthiness, and Privacy.","author":"Oprea A.","key":"e_1_3_2_1_42_1","unstructured":"A. Oprea, A. Gal, I. Moulinier, J.Chen, M. Veloso, E.Kurshan, S. Kumar, and T. Faruquie. 2019. NeurIPS 2019 Workshop on Robust AI in Financial Services: Data, Fairness, Explainability, Trustworthiness, and Privacy."},{"key":"e_1_3_2_1_43_1","unstructured":"PRNews. 2024. Zelle soars with $806 billion transaction volume up 28% from prior year. (2024). https:\/\/www.prnewswire.com\/news-releases\/zelle-soars-with-806-billion-transaction-volume-up-28-from-prior-year-302077432.html"},{"key":"e_1_3_2_1_44_1","volume-title":"Fraud Trends And Tectonics. Forbes","author":"Reports Forbes\u00a0Business","year":"2020","unstructured":"Forbes\u00a0Business Reports. 2020. Fraud Trends And Tectonics. Forbes (2020). https:\/\/www.forbes.com\/sites\/businessreporter\/2020\/06\/08\/fraud-trends-and-tectonics\/?sh=3a422de06d12"},{"key":"e_1_3_2_1_45_1","volume-title":"Carlo Gatta, and Yoshua Bengio","author":"Romero Adriana","year":"2014","unstructured":"Adriana Romero, Nicolas Ballas, Samira\u00a0Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio. 2014. FitNets: Hints for Thin Deep Nets. CoRR abs\/1412.6550 (2014)."},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1545"},{"key":"e_1_3_2_1_47_1","volume-title":"The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets. PLOS ONE 10 (03","author":"Saito Takaya","year":"2015","unstructured":"Takaya Saito and Marc Rehmsmeier. 2015. The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets. PLOS ONE 10 (03 2015)."},{"key":"e_1_3_2_1_48_1","volume-title":"a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs\/1910.01108","author":"Sanh Victor","year":"2019","unstructured":"Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2019. DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs\/1910.01108 (2019)."},{"key":"e_1_3_2_1_49_1","volume-title":"Federated Knowledge Distillation. CoRR abs\/2011.02367","author":"Seo Hyowoon","year":"2020","unstructured":"Hyowoon Seo, Jihong Park, Seungeun Oh, Mehdi Bennis, and Seong-Lyun Kim. 2020. Federated Knowledge Distillation. CoRR abs\/2011.02367 (2020)."},{"key":"e_1_3_2_1_50_1","volume-title":"Account Takeover Fraud Rates Skyrocketed 282% Over Last Year. TechRepublic","author":"Shein E.","year":"2021","unstructured":"E. Shein. 2021. Account Takeover Fraud Rates Skyrocketed 282% Over Last Year. TechRepublic (2021). https:\/\/www.techrepublic.com\/article\/account-takeover-fraud-rates-skyrocketed-282-over-last-year\/"},{"key":"e_1_3_2_1_51_1","volume-title":"Deep Q-Network-based Adaptive Alert Threshold Selection Policy for Payment Fraud Systems in Retail Banking. CoRR abs\/2010.11062","author":"Shen Hongda","year":"2020","unstructured":"Hongda Shen and Eren Kurshan. 2020. Deep Q-Network-based Adaptive Alert Threshold Selection Policy for Payment Fraud Systems in Retail Banking. CoRR abs\/2010.11062 (2020)."},{"key":"e_1_3_2_1_52_1","volume-title":"The Global Framework for Fighting Financial Crime Enhancing Effectiveness & Improving Outcomes. Deloitte Report","author":"Shepard M.","year":"2019","unstructured":"M. Shepard, T.Adams, A. Portilla, M. Ekberg, R. Wainwright, K. Jackson, T. Baumann, C. Bostock, A. Saleh, and P.\u00a0Saplains Lagoss. 2019. The Global Framework for Fighting Financial Crime Enhancing Effectiveness & Improving Outcomes. Deloitte Report (2019)."},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.vlsi.2019.09.007"},{"key":"e_1_3_2_1_54_1","volume-title":"Zelle Digital Adoption. EWS Reports","author":"Early\u00a0Warning Systems EWS","year":"2019","unstructured":"EWS Early\u00a0Warning Systems. 2019. Zelle Digital Adoption. EWS Reports (2019)."},{"key":"e_1_3_2_1_55_1","volume-title":"A Survey of FPGA Based Deep Learning Accelerators: Challenges and Opportunities. Arxiv","author":"T\u00a0Wang C\u00a0Wang","year":"2018","unstructured":"C\u00a0Wang T\u00a0Wang, X Zhou, and H Chen. 2018. A Survey of FPGA Based Deep Learning Accelerators: Challenges and Opportunities. Arxiv (2018)."},{"key":"e_1_3_2_1_56_1","unstructured":"Timeframe Analysis. 2019. Timeframe Analysis. https:\/\/www.kaggle.com\/terrypham\/transactiondt-timeframe-deduction."},{"volume-title":"Digital Fraud Attempts Spike 80% Globally From Pre-Pandemic Levels","year":"2023","key":"e_1_3_2_1_57_1","unstructured":"TransUnion. 2023. Digital Fraud Attempts Spike 80% Globally From Pre-Pandemic Levels. TransUnion News Report (2023). https:\/\/newsroom.transunion.com\/transunion-report-finds-digital-fraud-attempts-spike-80-globally-from-pre-pandemic\/"},{"volume-title":"TransUnion 2024: State of the Omnichannel Fraud Report","year":"2024","key":"e_1_3_2_1_58_1","unstructured":"TransUnion. 2024. TransUnion 2024: State of the Omnichannel Fraud Report. TransUnion (2024). https:\/\/www.transunion.com\/content\/dam\/transunion\/us\/business\/collateral\/report\/GFS-23-F158127-TruVa-2024StateofOmnichannelFraudReport-RPR-US_EN-US.pdf"},{"key":"e_1_3_2_1_59_1","volume-title":"Matthai Philipose, and Matthew Richardson.","author":"Urban Gregor","year":"2016","unstructured":"Gregor Urban, Krzysztof Geras, Samira\u00a0Ebrahimi Kahou, \u00d6zlem Aslan, Shengjie Wang, Rich Caruana, Abdel rahman Mohamed, Matthai Philipose, and Matthew Richardson. 2016. Do Deep Convolutional Nets Really Need to be Deep (Or Even Convolutional)?ArXiv abs\/1603.05691 (2016)."},{"key":"e_1_3_2_1_60_1","volume-title":"Fartash Faghri, Raviteja Vemulapalli, Mehrdad Farajtabar, Sachin Mehta, Mohammad Rastegari, Oncel Tuzel, and Hadi Pouransari.","author":"Wang Haoxiang","year":"2024","unstructured":"Haoxiang Wang, Pavan Kumar\u00a0Anasosalu Vasu, Fartash Faghri, Raviteja Vemulapalli, Mehrdad Farajtabar, Sachin Mehta, Mohammad Rastegari, Oncel Tuzel, and Hadi Pouransari. 2024. SAM-CLIP: Merging Vision Foundation Models Towards Semantic and Spatial Understanding. 3635\u20133647."},{"key":"e_1_3_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.01563"},{"key":"e_1_3_2_1_62_1","unstructured":"Xiaohan Xu Ming Li Chongyang Tao Tao Shen Reynold Cheng Jinyang Li Can Xu Dacheng Tao and Tianyi Zhou. 2024. A Survey on Knowledge Distillation of Large Language Models. arXiv:2402.13116"},{"key":"e_1_3_2_1_63_1","volume-title":"Essence Knowledge Distillation for Speech Recognition. CoRR","author":"Yang Zhenchuan","year":"2019","unstructured":"Zhenchuan Yang, Chun Zhang, Weibin Zhang, Jianxiu Jin, and Dongpeng Chen. 2019. Essence Knowledge Distillation for Speech Recognition. CoRR (2019)."},{"volume-title":"Zelle Closes 2020 with Record $307 Billion Sent on 1.2 Billion Transactions","year":"2021","key":"e_1_3_2_1_64_1","unstructured":"Zelle. 2021. Zelle Closes 2020 with Record $307 Billion Sent on 1.2 Billion Transactions. Zelle Press Releases (2021). https:\/\/www.zellepay.com\/press-releases\/zeller-closes-2020-record-307-billion-sent-12-billion-transactions"},{"key":"e_1_3_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1145\/2684746.2689060"},{"key":"e_1_3_2_1_66_1","volume-title":"GPU-acceleration for Large-scale Tree Boosting. Arxiv","author":"Zhang Huan","year":"2017","unstructured":"Huan Zhang, Si Si, and Cho-Jui Hsieh. 2017. GPU-acceleration for Large-scale Tree Boosting. Arxiv (2017)."},{"key":"e_1_3_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00776"},{"key":"e_1_3_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1201\/b12207"},{"key":"e_1_3_2_1_69_1","volume-title":"Data-Free Knowledge Distillation for Heterogeneous Federated Learning. CoRR abs\/2105.10056","author":"Zhu Zhuangdi","year":"2021","unstructured":"Zhuangdi Zhu, Junyuan Hong, and Jiayu Zhou. 2021. Data-Free Knowledge Distillation for Heterogeneous Federated Learning. CoRR abs\/2105.10056 (2021)."}],"event":{"name":"ICAIF '24: 5th ACM International Conference on AI in Finance","acronym":"ICAIF '24","location":"Brooklyn NY USA"},"container-title":["Proceedings of the 5th ACM International Conference on AI in Finance"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3677052.3698663","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3677052.3698663","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T17:14:24Z","timestamp":1755882864000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3677052.3698663"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,14]]},"references-count":69,"alternative-id":["10.1145\/3677052.3698663","10.1145\/3677052"],"URL":"https:\/\/doi.org\/10.1145\/3677052.3698663","relation":{},"subject":[],"published":{"date-parts":[[2024,11,14]]},"assertion":[{"value":"2024-11-14","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}