fix: some errors in /about down, many to go
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1 changed files with 5 additions and 5 deletions
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@ -132,8 +132,8 @@ The `keepdims=True` makes `norms` shape <Math tex="(N, 1)" /> instead of <Math
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tex="(N,)" />, which is crucial. When transposed, <Math tex="(N, 1)" /> becomes
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<Math tex="(1, N)" />, allowing the broadcasting to work for column-wise
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division. Transpose does not do anything to the shape <Math tex="(N,)" />. I
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don't know why transpose works this way, but this seems like gotcha to look out
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for.
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don't know why transpose works this way, but this seems like a nasty gotcha to
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look out for.
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## Step 4: Clean Up the Graph
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@ -202,8 +202,8 @@ Traditional normalization <Math tex="\tilde{\mathbf{A}} = \mathbf{D}^{-1} \mathb
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- This is like a proper Markov chain transition matrix
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- Used in standard PageRank and TextRank
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- Supports **directed** graphs, a property useful for modeling web page
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navigation (page A links to B but B does not link back to A), but we don't
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actually need for sentence similarity where similarity of A to B is exactly
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navigation (page A links to B but B does not link back to A). We don't
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need this because similarity of sentence A to B is exactly
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the same value as B to A.
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Spectral normalization <Math tex="\tilde{\mathbf{A}} = \mathbf{D}^{-1/2} \mathbf{A} \mathbf{D}^{-1/2}" />:
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@ -223,7 +223,7 @@ Spectral normalization solves this problem. Well-connected sentences keep their
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influence proportional to connectivity.
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I asked a ML engineer to explain the same idea to give you a
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Rosetta Stone to understand their jaron.
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Rosetta Stone to understand their jargon.
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> The traditional <Math tex="\mathbf{D}^{-1} \mathbf{A}" /> approach introduces potential node bias and lacks symmetry. Spectral normalization
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> provides a more balanced representation by symmetrizing the adjacency matrix and ensuring more uniform
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