{"id":26479,"date":"2024-02-26T02:50:03","date_gmt":"2024-02-26T03:50:03","guid":{"rendered":"https:\/\/express24.ir\/d\/product\/%d9%85%d9%82%d8%a7%d9%84%d9%87-ssm-%d9%be%d8%a7%db%8c%d8%af%d8%a7%d8%b1-%da%a9%d8%a7%d9%87%d8%b4-%d9%86%d9%81%d8%b1%db%8c%d9%86-%d8%ad%d8%a7%d9%81%d8%b8%d9%87-%d8%af%d8%b1-%d9%85%d8%af%d9%84-%d9%87\/"},"modified":"2024-02-26T02:50:04","modified_gmt":"2024-02-26T03:50:04","slug":"%d9%85%d9%82%d8%a7%d9%84%d9%87-ssm-%d9%be%d8%a7%db%8c%d8%af%d8%a7%d8%b1-%da%a9%d8%a7%d9%87%d8%b4-%d9%86%d9%81%d8%b1%db%8c%d9%86-%d8%ad%d8%a7%d9%81%d8%b8%d9%87-%d8%af%d8%b1-%d9%85%d8%af%d9%84-%d9%87","status":"publish","type":"product","link":"https:\/\/express24.ir\/d\/product\/%d9%85%d9%82%d8%a7%d9%84%d9%87-ssm-%d9%be%d8%a7%db%8c%d8%af%d8%a7%d8%b1-%da%a9%d8%a7%d9%87%d8%b4-%d9%86%d9%81%d8%b1%db%8c%d9%86-%d8%ad%d8%a7%d9%81%d8%b8%d9%87-%d8%af%d8%b1-%d9%85%d8%af%d9%84-%d9%87\/","title":{"rendered":"\u0645\u0642\u0627\u0644\u0647 SSM \u067e\u0627\u06cc\u062f\u0627\u0631: \u06a9\u0627\u0647\u0634 \u0646\u0641\u0631\u06cc\u0646 \u062d\u0627\u0641\u0638\u0647 \u062f\u0631 \u0645\u062f\u0644 \u0647\u0627\u06cc \u0641\u0636\u0627\u06cc \u062d\u0627\u0644\u062a \u0627\u0632 \u0637\u0631\u06cc\u0642 \u067e\u0627\u0631\u0627\u0645\u062a\u0631\u0633\u0627\u0632\u06cc \u0645\u062c\u062f\u062f \u067e\u0627\u06cc\u062f\u0627\u0631"},"content":{"rendered":"\n\n\n\n\n\n\n\n\n\n\n
\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc <\/td>\nStableSSM: Alleviating the Curse of Memory in State-space Models through Stable Reparameterization<\/td>\n<\/tr>\n
\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc <\/td>\n\u0645\u0642\u0627\u0644\u0647 SSM \u067e\u0627\u06cc\u062f\u0627\u0631: \u06a9\u0627\u0647\u0634 \u0646\u0641\u0631\u06cc\u0646 \u062d\u0627\u0641\u0638\u0647 \u062f\u0631 \u0645\u062f\u0644 \u0647\u0627\u06cc \u0641\u0636\u0627\u06cc \u062d\u0627\u0644\u062a \u0627\u0632 \u0637\u0631\u06cc\u0642 \u067e\u0627\u0631\u0627\u0645\u062a\u0631\u0633\u0627\u0632\u06cc \u0645\u062c\u062f\u062f \u067e\u0627\u06cc\u062f\u0627\u0631<\/td>\n<\/tr>\n
\u0646\u0648\u06cc\u0633\u0646\u062f\u06af\u0627\u0646 <\/td>\nShida Wang, Qianxiao Li<\/td>\n<\/tr>\n
\u0632\u0628\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 <\/td>\n\u0627\u0646\u06af\u0644\u06cc\u0633\u06cc<\/td>\n<\/tr>\n
\u0641\u0631\u0645\u062a \u0645\u0642\u0627\u0644\u0647: <\/td>\nPDF<\/td>\n<\/tr>\n
\u062a\u0639\u062f\u0627\u062f \u0635\u0641\u062d\u0627\u062a<\/td>\n35<\/td>\n<\/tr>\n
\u062f\u0633\u062a\u0647 \u0628\u0646\u062f\u06cc \u0645\u0648\u0636\u0648\u0639\u0627\u062a <\/td>\nMachine Learning,Artificial Intelligence,Computation and Language,Dynamical Systems,\u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646 , \u0647\u0648\u0634 \u0645\u0635\u0646\u0648\u0639\u06cc , \u0645\u062d\u0627\u0633\u0628\u0627\u062a \u0648 \u0632\u0628\u0627\u0646 , \u0633\u06cc\u0633\u062a\u0645 \u0647\u0627\u06cc \u062f\u06cc\u0646\u0627\u0645\u06cc\u06a9\u06cc ,<\/td>\n<\/tr>\n
\u062a\u0648\u0636\u06cc\u062d\u0627\u062a <\/td>\nSubmitted 24 November, 2023; originally announced November 2023.<\/td>\n<\/tr>\n
\u062a\u0648\u0636\u06cc\u062d\u0627\u062a \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc <\/td>\n\u0627\u0631\u0633\u0627\u0644 \u0634\u062f\u0647 24 \u0646\u0648\u0627\u0645\u0628\u0631 2023 \u061b\u062f\u0631 \u0627\u0628\u062a\u062f\u0627 \u0646\u0648\u0627\u0645\u0628\u0631 2023 \u0627\u0639\u0644\u0627\u0645 \u0634\u062f.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u0686\u06a9\u06cc\u062f\u0647<\/h2>\n

In this paper, we investigate the long-term memory learning capabilities of state-space models (SSMs) from the perspective of parameterization. We prove that state-space models without any reparameterization exhibit a memory limitation similar to that of traditional RNNs: the target relationships that can be stably approximated by state-space models must have an exponential decaying memory. Our analysis identifies this “curse of memory” as a result of the recurrent weights converging to a stability boundary, suggesting that a reparameterization technique can be effective. To this end, we introduce a class of reparameterization techniques for SSMs that effectively lift its memory limitations. Besides improving approximation capabilities, we further illustrate that a principled choice of reparameterization scheme can also enhance optimization stability. We validate our findings using synthetic datasets and language models.<\/p>\n

\u0686\u06a9\u06cc\u062f\u0647 \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc (\u062a\u0631\u062c\u0645\u0647 \u0645\u0627\u0634\u06cc\u0646\u06cc)<\/h2>\n

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\r\n\t\u062a\u0648\u062c\u0647 \u06a9\u0646\u06cc\u062f \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0632\u0628\u0627\u0646 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc \u0627\u0633\u062a.\r\n\t<\/td>\r\n<\/tr>\r\n\t\r\n
\r\n \u0628\u0631\u0627\u06cc \u0633\u0641\u0627\u0631\u0634 \u062a\u0631\u062c\u0645\u0647 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u0628\u0647 \u06cc\u06a9\u06cc \u0627\u0632 \u0631\u0648\u0634 \u0647\u0627\u06cc \u062a\u0645\u0627\u0633\u060c \u067e\u06cc\u0627\u0645\u06a9\u060c \u062a\u0644\u06af\u0631\u0627\u0645 \u0648 \u06cc\u0627 \u0648\u0627\u062a\u0633 \u0627\u067e \u0628\u0627 \u0634\u0645\u0627\u0631\u0647 \u0632\u06cc\u0631 \u062a\u0645\u0627\u0633 \u0628\u06af\u06cc\u0631\u06cc\u062f:\r\n\t

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  • \r\n\t\t\u06a9\u06cc\u0641\u06cc\u062a \u062a\u0631\u062c\u0645\u0647 \u0628\u0633\u06cc\u0627\u0631 \u0628\u0627\u0644\u0627 \u0645\u06cc \u0628\u0627\u0634\u062f. \u0645\u0642\u0627\u0644\u0647 \u0641\u0642\u0637 \u062a\u0648\u0633\u0637 \u0645\u062a\u0631\u062c\u0645\u06cc\u0646 \u0628\u0627 \u0645\u062f\u0631\u06a9 \u062f\u0627\u0646\u0634\u06af\u0627\u0647\u06cc \u0645\u062a\u0631\u062c\u0645\u06cc \u062a\u0631\u062c\u0645\u0647 \u0645\u06cc\u200c\u0634\u0648\u062f.\r\n\t\t<\/li>\r\n\t\t
  • \r\n\t\t\u06a9\u0644\u06cc\u0647 \u062c\u062f\u0627\u0648\u0644 \u0648 \u0641\u0631\u0645\u0648\u0644 \u0647\u0627 \u0646\u06cc\u0632 \u062f\u0631 \u0641\u0627\u06cc\u0644 \u062a\u062d\u0648\u06cc\u0644\u06cc \u0648\u0631\u062f \u062f\u0631\u062c \u0645\u06cc\u200c\u0634\u0648\u0646\u062f.\r\n\t\t<\/li>\r\n\t<\/td>\r\n\t\r\n\t\r\n<\/tr>\r\n<\/table>\r\n\r\n\r\n\n","protected":false},"excerpt":{"rendered":"

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