[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"tag-黎曼幾何":3},{"tag":4,"articles":9},{"id":5,"name":6,"slug":6,"article_count":7,"description_zh":8,"description_en":8},"d5b8ee00-975d-44f8-b204-d4c85f997421","黎曼幾何",1,null,[10],{"id":11,"slug":12,"title":13,"summary":14,"category":15,"image_url":16,"cover_image":16,"language":17,"created_at":18},"53a0dc54-0371-4e40-8d5e-74e94a73840c","geometry-aware-similarity-metrics-for-neural-representations-zh","超越距離測量：用微分幾何重新理解神經網路","研究者用黎曼幾何分析神經網路表示的內在結構，揭示傳統相似度指標無法發現的深層模式。","research","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1774939902743-mij0.png","zh","2026-03-31T06:01:01.241968+00:00"]