[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85408-en":3,"doc-seo-85408-105":29,"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":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},85408,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1782109480056885918",8,"Research & Report","Hyperflux Pruning Reveals Importance","Network pruning is used to reduce inference latency and power consumption in large neural networks, yet many techniques rely on empirical heuristics without explaining why pruning decisions occur. Hyperflux presents a novel L0 formulation that models pruning as a continuously evolving system driven by flux, the gradient response to weight removal, and pressure, a global regularization pushing weights toward pruning. The method yields both microscopic (regrowth/pruning) and macroscopic (sparsity convergence) interpretability. A pressure scheduler reliably targets desired sparsities while achieving competitive accuracy.","arXiv :2504 .05349v5 [ stat .ML] 12 Jul 2026  \nHyperflux  \nPruning Reveals Importance  \nBarbulescu Eugen [Barbulescu. Oc.Eugen@student.utcluj. ro](Barbulescu. Oc.Eugen@student.utcluj. ro)  \nDepartment of Computer Science Technical University of Cluj-Napoca  \nAntonio Alexoaie [Alexoaie. Ov.Antonio@student.utcluj. ro](Alexoaie. Ov.Antonio@student.utcluj. ro)  \nDepartment of Computer Science Technical University of Cluj-Napoca  \nLucian Busoniu [lucian. busoniu@aut. ro](lucian. busoniu@aut. ro)  \nDepartment of Automation  \nTechnical University of Cluj-Napoca  \nAbstract  \nNetwork pruning is used to reduce inference latency and power consumption in large neural networks. However, most methods focus on empirical results at the expense of understanding the pruning process. We introduce Hyperflux, a novel L0 method which models pruning as a continuously evolving system determined by flux, the gradient response to a weight’s removal, and pressure, a global regularization driving weights toward pruning. By exploiting this model, Hyperflux’s pruning behavior becomes understandable at both microscopic (weight regrowth/pruning) and macroscopic (sparsity convergence, etc.) levels. We also introduce a novel pressure scheduler that reliably targets desired sparsities. Hyperflux achieves competitive results with ResNet-50, VGG-19 and DeiT-T/S on CIFAR-10, CIFAR-100 and ImageNet datasets.  \n1 Introduction  \nOverparameterization has become the norm in modern deep learning to achieve state-of-the-art performance (Neyshabur et al., 2019; Allen-Zhu et al., 2019; Li et al., 2018) . Despite clear benefits for training, this practice also increases computational and memory costs, complicating deployment on resource-constrained devices such as edge hardware, IoT platforms, and autonomous robots (Shi et al., 2016; Li et al., 2019) . Recent theoretical and empirical findings suggest that sparse subnetworks extracted from large dense models can match or exceed the accuracy of their dense counterparts (Frankle & Carbin, 2019; Zhou et al., 2019; Ma et al., 2021; Lee et al., 2019; De Jorge et al., 2021; Cho et al., 2023; Yite et al., 2023; Frantar et al. , 2024; Wang et al., 2023) and even outperform smaller dense models of equal size (Ramanujan et al., 2020; Li et al., 2020; Zhu & Gupta, 2018) . These results have created interest in network pruning as a strategy to identify minimal, high-performing subnetworks.  \nPruning has a rich history (LeCun et al., 1989; Mozer & Smolensky, 1988; Thimm & Hoppe, 1995) and continues to prove valuable for real-time applications (Han et al., 2016; Jongsoo et al., 2017; Wang et al. , 2019) . Recent methods have significantly advanced the field by resorting to a variety of strategies and heuristics, from magnitude pruning, gradient methods, and Hessian-based criteria (Han et al., 2015; 2016; LeCun et al., 1992; Singh & Alistarh, 2020; Bellec et al., 2018; Frankle & Carbin, 2019) to dynamic pruning approaches (Liu et al., 2020; Cho et al., 2023; Savarese et al., 2020; Kusupati et al., 2020; Wortsman et al. , 2019) or combinations thereof (Liu et al., 2022; Evci et al., 2020) . However, the strong interdependence between weights remains a challenge (Jin et al., 2020; Templeton et al., 2024; Lee et al., 2019; De Jorge et al., 2021; Louizos et al., 2017), as it complicates the task of determining each weight’s importance and  \nthe behavior of the pruning process. As a result, most current state-of-the-art strategies prioritize empirical results through heuristics, often at the expense of understanding the pruning process.  \nGiven this gap, we introduce Hyperflux, a novel L0 pruning method that shifts the focus from empirical results to understanding the pruning process. Our aim within Hyperflux is to answer the following questions: Why do weights get pruned or regrown? And how does the network behave as we prune it?  \nHyperflux is inspired by the principle that the value of something is not truly known until it is lost, which ha","cbCairLaywnvbLxv","https://ap.wps.com/l/cbCairLaywnvbLxv","pdf",2978107,1,28,"English","en",105,"# Introduction\n## Motivation and background\n## Hyperflux approach and core idea\n# Related work","[{\"question\":\"What is Hyperflux and what problem does it address?\",\"answer\":\"Hyperflux is a novel L0 pruning method that focuses on understanding the pruning process rather than relying only on empirical heuristics. It aims to explain why weights are pruned or regrown and how network behavior changes during pruning.\"},{\"question\":\"How does Hyperflux measure a weight’s importance?\",\"answer\":\"Hyperflux removes a weight (via a presence/masking parameter), then observes the gradient response caused by its absence, called weight flux. A global pressure term drives an L0-style evaluation that uses these fluxes to decide regrowth versus staying pruned.\"},{\"question\":\"How does Hyperflux target a specific sparsity level and what results does it achieve?\",\"answer\":\"Hyperflux introduces a pressure scheduler designed to reliably reach desired sparsities. It delivers competitive performance on ResNet-50, VGG-19, and DeiT-T/S across CIFAR-10, CIFAR-100, and ImageNet.\"}]",1784203191,71,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"hyperflux-pruning-reveals-importance","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/hyperflux-pruning-reveals-importance/85408/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","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 is Hyperflux and what problem does it address?","Question",{"text":75,"@type":76},"Hyperflux is a novel L0 pruning method that focuses on understanding the pruning process rather than relying only on empirical heuristics. It aims to explain why weights are pruned or regrown and how network behavior changes during pruning.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does Hyperflux measure a weight’s importance?",{"text":80,"@type":76},"Hyperflux removes a weight (via a presence/masking parameter), then observes the gradient response caused by its absence, called weight flux. A global pressure term drives an L0-style evaluation that uses these fluxes to decide regrowth versus staying pruned.",{"name":82,"@type":73,"acceptedAnswer":83},"How does Hyperflux target a specific sparsity level and what results does it achieve?",{"text":84,"@type":76},"Hyperflux introduces a pressure scheduler designed to reliably reach desired sparsities. 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