[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-84031-en":3,"doc-seo-84031-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},84031,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Drift Happens An Empirical Study of Neural Architecture Robustness to Temporal Distribution Shift","Real-world data distributions evolve over time, creating temporal distribution shift that can undermine the reliability of deployed machine learning. The study systematically compares temporal robustness across three time-indexed domains—image classification, multi-label text classification, and text regression—using temporal drift matrices. Models are trained on cumulative historical data and evaluated on earlier and later periods to quantify cross-temporal generalization across diverse architectures from CNNs and RNNs to pretrained Transformers.","Drift Happens: An Empirical Study of Neural Architecture Robustness to Temporal Distribution Shift  \nRobin Holzinger* Riccardo Colletti*  \nDepartment of Electrical Engineering and Computer Sciences, University of California, Berkeley, USA  \nExtended version. Accepted at QCDS 2026; the proceedings version will appear in Springer LNCS.  \narXiv :2607 .05908v 1 [ cs .LG] 7 Jul 2026  \nAbstract  \nReal-world data distributions evolve over time, inducing temporal distribution shift that can substantially degrade the reliability of deployed machine learning systems. However, the extent to which architectural choices and their associated inductive biases affect temporal robustness remains insufficiently understood.  \nWe present a systematic empirical comparison of temporal robustness across three heterogeneous, time-indexed domains encompassing image classification, multi-label text classification, and text regression tasks. Using a unified evaluation framework based on temporal drift matrices, we train models on cumulative historical data and evaluate their performance on both earlier and later time periods, thereby quantifying cross-temporal generalization. Our study spans model families ranging from simple multilayer perceptrons and convolutional networks to recurrent networks and pretrained Transformer-based encoders.  \nCollectively, the results show that architectural inductive biases systematically shape temporal robustness: models whose inductive biases lead them to exploit localized, highly discriminative features attain the highest in-distribution accuracy, yet those features are often the ones that change most over time, so these models degrade fastest, while pretrained encoders that draw on coarser, more stable representations drift more gradually.  \nThese observations offer practical guidance for selecting architectures for real-world systems subject to temporal drift.  \n1. Introduction  \nMachine learning models are typically trained under the assumption that training and test data are drawn from the same distribution. In practice, this assumption rarely holds:  \n*Both authors contributed equally.  \nreal-world data evolves over time, exhibiting temporal distribution shift that can degrade model performance in ways that standard held-out evaluation fails to capture. A model that achieves high accuracy on held-out data from the sametime period may fail when deployed on future inputs.  \nWhile distribution shift is well-documented, less understood is how architectural choices influence a model’s robustness to temporal drift.  \nDo different inductive biases (the translation invariance of convolutions, the sequential modeling of recurrent nets, the attention of Transformers)  \nlead to different rates of temporal degradation? Do frozen, pretrained encoders resist temporal drift better than models trained end to end?  \nThese questions have practical implications for model selection, yet systematic comparisons across architectures and domains remain scarce.  \nThis work investigates the temporal robustness of neural classifiers across three domains: image classification (Yearbook), text regression (Amazon Reviews), and multi-label text classification (arXiv) . For each domain, we evaluate diverse architectures, from simple baselines topretrained transformers, using a unified framework based on temporal drift matrices and provide qualitative explanations for model degradation via gradient saliency maps. Our contributions are threefold:  \n• We provide a unified empirical assessment of temporal robustness across three long-range, time-indexed domains, enabling direct comparison of how neural architectures behave under temporal distribution shift.  \n• We organize time-indexed evaluation in the spirit of Wild-Time [39] into temporal drift matrices, a compact representation that quantifies cross-temporal generalization by measuring performance when training on cumulative historical data and testing on both earlier and later time periods.  \n","cbCaifkOXDISmcAR","https://ap.wps.com/l/cbCaifkOXDISmcAR","pdf",6135270,3,1,33,"English","en",105,"# Introduction\n## Temporal Distribution Shift\n# Background and Related Work\n## Temporal Distribution Shift","[{\"question\":\"What problem does the paper address?\",\"answer\":\"The paper addresses temporal distribution shift, where data and label semantics change over time, causing deployed models to degrade when evaluated on future inputs.\"},{\"question\":\"How is temporal robustness evaluated in the study?\",\"answer\":\"The study uses a unified evaluation framework based on temporal drift matrices, training on cumulative historical data and testing on both earlier and later time periods to measure cross-temporal generalization.\"},{\"question\":\"How do architectural inductive biases affect model degradation over time?\",\"answer\":\"Architectural inductive biases shape temporal robustness: models that exploit localized, highly discriminative features achieve strong in-distribution accuracy but often degrade faster, while pretrained encoders relying on more stable representations drift more gradually.\"}]",1784192139,83,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"drift-happens-an-empirical-study-of-neural-architecture-robustness-to-temporal-distribution-shift","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":20},"https://docshare.wps.com/document/research-report/",{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/drift-happens-an-empirical-study-of-neural-architecture-robustness-to-temporal-distribution-shift/84031/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-26","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the paper address?","Question",{"text":75,"@type":76},"The paper addresses temporal distribution shift, where data and label semantics change over time, causing deployed models to degrade when evaluated on future inputs.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is temporal robustness evaluated in the study?",{"text":80,"@type":76},"The study uses a unified evaluation framework based on temporal drift matrices, training on cumulative historical data and testing on both earlier and later time periods to measure cross-temporal generalization.",{"name":82,"@type":73,"acceptedAnswer":83},"How do architectural inductive biases affect model degradation over time?",{"text":84,"@type":76},"Architectural inductive biases shape temporal robustness: models that exploit localized, highly discriminative features achieve strong in-distribution accuracy but often degrade faster, while pretrained encoders relying on more stable representations drift more 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