{"id":26949,"date":"2022-06-26T14:39:49","date_gmt":"2022-06-26T11:39:49","guid":{"rendered":"https:\/\/hgpu.org\/?p=26949"},"modified":"2022-06-26T14:39:49","modified_gmt":"2022-06-26T11:39:49","slug":"tntorch-tensor-network-learning-with-pytorch","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=26949","title":{"rendered":"tntorch: Tensor Network Learning with PyTorch"},"content":{"rendered":"<p>We present tntorch, a tensor learning framework that supports multiple decompositions (including Candecomp\/Parafac, Tucker, and Tensor Train) under a unified interface. With our library, the user can learn and handle low-rank tensors with automatic differentiation, seamless GPU support, and the convenience of PyTorch&#8217;s API. Besides decomposition algorithms, tntorch implements differentiable tensor algebra, rank truncation, cross-approximation, batch processing, comprehensive tensor arithmetics, and more.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We present tntorch, a tensor learning framework that supports multiple decompositions (including Candecomp\/Parafac, Tucker, and Tensor Train) under a unified interface. With our library, the user can learn and handle low-rank tensors with automatic differentiation, seamless GPU support, and the convenience of PyTorch&#8217;s API. Besides decomposition algorithms, tntorch implements differentiable tensor algebra, rank truncation, cross-approximation, [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[11,3],"tags":[1782,1673,20,2082,176,513,2020],"class_list":["post-26949","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-paper","tag-computer-science","tag-deep-learning","tag-nvidia","tag-nvidia-geforce-rtx-3090","tag-package","tag-python","tag-pytorch"],"views":1467,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/26949","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/users\/351"}],"replies":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=26949"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/26949\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=26949"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=26949"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=26949"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}