{"id":4150,"date":"2022-12-10T12:48:06","date_gmt":"2022-12-10T11:48:06","guid":{"rendered":"https:\/\/www.ceessnoek.info\/?p=4150"},"modified":"2022-12-10T12:48:46","modified_gmt":"2022-12-10T11:48:46","slug":"log-2022-pruning-edges-and-gradients-to-learn-hypergraphs-from-larger-sets","status":"publish","type":"post","link":"https:\/\/www.ceessnoek.info\/index.php\/log-2022-pruning-edges-and-gradients-to-learn-hypergraphs-from-larger-sets\/","title":{"rendered":"LoG 2022: Pruning Edges and Gradients to Learn Hypergraphs from Larger Sets"},"content":{"rendered":"\n<p>The Learning on Graphs 2022 paper &#8220;<em>Pruning Edges and Gradients to Learn Hypergraphs from Larger Sets<\/em>&#8221; by David W. Zhang, Gertjan Burghouts and Cees Snoek is <a href=\"https:\/\/isis-data.science.uva.nl\/cgmsnoek\/pub\/zhang-hypergraphs-log2022.pdf\">now available<\/a>. This paper aims for set-to-hypergraph prediction, where the goal is to infer the set of relations for a given set of entities. This is a common abstraction for applications in particle physics, biological systems and combinatorial optimization. We address two common scaling problems encountered in set-to-hypergraph tasks that limit the size of the input set: the exponentially growing number of hyperedges and the run-time complexity, both leading to higher memory requirements. We make three contributions. First, we propose to predict and supervise the positive edges only, which changes the asymptotic memory scaling from exponential to linear. Second, we introduce a training method that encourages iterative refinement of the predicted hypergraph, which allows us to skip iterations in the backward pass for improved efficiency and constant memory usage. Third, we combine both contributions in a single set-to-hypergraph model that enables us to address problems with larger input set sizes. We provide ablations for our main technical contributions and show that our model outperforms prior state-of-the-art, especially for larger sets.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><a href=\"https:\/\/www.ceessnoek.info\/wp-content\/uploads\/2021\/06\/zhang-hypergraphs.png\"><img loading=\"lazy\" decoding=\"async\" width=\"1878\" height=\"322\" src=\"https:\/\/www.ceessnoek.info\/wp-content\/uploads\/2021\/06\/zhang-hypergraphs.png\" alt=\"\" class=\"wp-image-3868\" srcset=\"https:\/\/www.ceessnoek.info\/wp-content\/uploads\/2021\/06\/zhang-hypergraphs.png 1878w, https:\/\/www.ceessnoek.info\/wp-content\/uploads\/2021\/06\/zhang-hypergraphs-300x51.png 300w, https:\/\/www.ceessnoek.info\/wp-content\/uploads\/2021\/06\/zhang-hypergraphs-1024x176.png 1024w, https:\/\/www.ceessnoek.info\/wp-content\/uploads\/2021\/06\/zhang-hypergraphs-768x132.png 768w, https:\/\/www.ceessnoek.info\/wp-content\/uploads\/2021\/06\/zhang-hypergraphs-1536x263.png 1536w\" sizes=\"auto, (max-width: 1878px) 100vw, 1878px\" \/><\/a><\/figure>\n","protected":false},"excerpt":{"rendered":"<p>The Learning on Graphs 2022 paper &#8220;Pruning Edges and Gradients to Learn Hypergraphs from Larger Sets&#8221; by David W. Zhang, Gertjan Burghouts and Cees Snoek is now available. This paper aims for set-to-hypergraph prediction, where the goal is to infer the set of relations for a given set of entities. This is a common abstraction [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4],"tags":[],"class_list":["post-4150","post","type-post","status-publish","format-standard","hentry","category-science"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.3 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>LoG 2022: Pruning Edges and Gradients to Learn Hypergraphs from Larger Sets - Cees Snoek<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.ceessnoek.info\/index.php\/log-2022-pruning-edges-and-gradients-to-learn-hypergraphs-from-larger-sets\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"LoG 2022: Pruning Edges and Gradients to Learn Hypergraphs from Larger Sets - Cees Snoek\" \/>\n<meta property=\"og:description\" content=\"The Learning on Graphs 2022 paper &#8220;Pruning Edges and Gradients to Learn Hypergraphs from Larger Sets&#8221; by David W. Zhang, Gertjan Burghouts and Cees Snoek is now available. This paper aims for set-to-hypergraph prediction, where the goal is to infer the set of relations for a given set of entities. This is a common abstraction [&hellip;]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.ceessnoek.info\/index.php\/log-2022-pruning-edges-and-gradients-to-learn-hypergraphs-from-larger-sets\/\" \/>\n<meta property=\"og:site_name\" content=\"Cees Snoek\" \/>\n<meta property=\"article:published_time\" content=\"2022-12-10T11:48:06+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2022-12-10T11:48:46+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.ceessnoek.info\/wp-content\/uploads\/2021\/06\/zhang-hypergraphs.png\" \/>\n<meta name=\"author\" content=\"Cees\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Cees\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"2 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.ceessnoek.info\/index.php\/log-2022-pruning-edges-and-gradients-to-learn-hypergraphs-from-larger-sets\/\",\"url\":\"https:\/\/www.ceessnoek.info\/index.php\/log-2022-pruning-edges-and-gradients-to-learn-hypergraphs-from-larger-sets\/\",\"name\":\"LoG 2022: Pruning Edges and Gradients to Learn Hypergraphs from Larger Sets - 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