[{"data":1,"prerenderedAt":38},["ShallowReactive",2],{"post-document:\u002Fdeep_learning\u002Ftransformer\u002F2026\u002F08\u002F06\u002Fstate-space-models\u002F":3},{"id":4,"title":5,"body":6,"categories":17,"date":20,"description":21,"extension":22,"image":23,"key_concepts":23,"last_modified_at":23,"legacyPath":24,"meta":25,"navigation":27,"part":23,"path":28,"published":27,"robots":23,"seo":29,"series":23,"stem":30,"strengths":23,"summary":31,"tags":32,"tradeoffs":23,"__hash__":37},"posts\u002Fposts\u002FDeep_Learning\u002FTransformer\u002F2026-08-06-state-space-models.md","State Space Model과 Mamba",{"type":7,"value":8,"toc":13},"minimark",[9],[10,11,12],"p",{},"Attention의 계산량 문제\nState Space Model의 기본 아이디어\nSSM과 Transformer 비교\nMamba 계열 구조\nSelective State Space\nVision Mamba\nHybrid Attention–SSM\nRecurrent Model의 재등장\n긴 시퀀스 처리\nEdge 환경에서의 SSM",{"title":14,"searchDepth":15,"depth":15,"links":16},"",2,[],[18,19],"Deep_Learning","Transformer","2026-08-06 00:00:00 +0900","State Space Model의 기본 아이디어와 Mamba 계열 구조, Transformer와의 차이를 정리한다.","md",null,"\u002Fdeep_learning\u002Ftransformer\u002F2026\u002F08\u002F06\u002Fstate-space-models\u002F",{"layout":26},"post",true,"\u002Fposts\u002Fdeep_learning\u002Ftransformer\u002F2026-08-06-state-space-models",{"title":5,"description":21},"posts\u002FDeep_Learning\u002FTransformer\u002F2026-08-06-state-space-models","긴 시퀀스를 효율적으로 처리하는 SSM과 Mamba의 핵심 개념을 정리한다.",[33,34,35,36],"Deep Learning","State Space Model","Mamba","SSM","KsdogM02qEL-6hUcjbTV2fBGgJnMpwwwT9LzhPAnbAg",1788744789284]