Row 63073

Row ID: 63073 | Dataset Entry | Axioma AXP Content Repository

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This page contains data entry 63073 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.

The claim is paper's not mine. Basically their reasoning is bsplines are local so adding more control points to encode new information will not effect old information. This contrasts with standard neural networks, where each weight change effects networks response to all inputs, therefore fitting to new information may result in catastrophic forgetting of old. Btw, weights are not adjusted, only the activation functions are. Their parameters are control points of bsplines, not weights.

FieldValue
text The claim is paper's not mine. Basically their reasoning is bsplines are local so adding more control points to encode new information will not effect old information. This contrasts with standard neural networks, where each weight change effects networks response to all inputs, therefore fitting to new information may result in catastrophic forgetting of old. Btw, weights are not adjusted, only the activation functions are. Their parameters are control points of bsplines, not weights.
label r/machinelearning
dataType comment
communityName r/MachineLearning
datetime 2024-05-23
username_encoded Z0FBQUFBQm5Lak1hN0NGVUVuUlBPc1NIb3hpdDVSdzBabTN6ODk5ckdhZGJXYjhTa2d2N1F0NU92U1ZaMnJOY3M2SWxvMzhKRHpWZV9zZDBwOGN1NkxJaFBuWjdRa21OT1dIdTBWZUxMaFBIMjhvcVQ3ZlY1azg9
url_encoded Z0FBQUFBQm5Lak9xSkpQWDd5bWIxUUp2Uk1hZzNVNVYxY3MtWGdSb05WWURKSGRzRVhnWlhlekc0bzB6emNUX0gwNGdnSlZnZGNKWU56UWtkM0xnSEVQQWE3b2plUDdjVm5WdmpqYVk2YURScW0xMkJ3aG5JN0FMM2t2ampPam52NzJZbzBHeW5lQXc2SEpZNGdLc3F5el9SUFpJQjB3bXpSdXVuNzVuWlIxLTRWN2cwZ3lSMG9zLWI4SDlSdmFpOVN4UHV5Y21vb1RkT053WVZLclp6WENReUVSUkdMWTdHZz09

Raw Record

{
  "text": "The claim is paper's not mine. Basically their reasoning is bsplines are local so adding more control points to encode new information will not effect old information. This contrasts with standard neural networks, where each weight change effects networks response to all inputs, therefore fitting to new information may result in catastrophic forgetting of old.\nBtw, weights are not adjusted, only the activation functions are. Their parameters are control points of bsplines, not weights.",
  "label": "r/machinelearning",
  "dataType": "comment",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-23",
  "username_encoded": "Z0FBQUFBQm5Lak1hN0NGVUVuUlBPc1NIb3hpdDVSdzBabTN6ODk5ckdhZGJXYjhTa2d2N1F0NU92U1ZaMnJOY3M2SWxvMzhKRHpWZV9zZDBwOGN1NkxJaFBuWjdRa21OT1dIdTBWZUxMaFBIMjhvcVQ3ZlY1azg9",
  "url_encoded": "Z0FBQUFBQm5Lak9xSkpQWDd5bWIxUUp2Uk1hZzNVNVYxY3MtWGdSb05WWURKSGRzRVhnWlhlekc0bzB6emNUX0gwNGdnSlZnZGNKWU56UWtkM0xnSEVQQWE3b2plUDdjVm5WdmpqYVk2YURScW0xMkJ3aG5JN0FMM2t2ampPam52NzJZbzBHeW5lQXc2SEpZNGdLc3F5el9SUFpJQjB3bXpSdXVuNzVuWlIxLTRWN2cwZ3lSMG9zLWI4SDlSdmFpOVN4UHV5Y21vb1RkT053WVZLclp6WENReUVSUkdMWTdHZz09"
}
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