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对比损失函数 #3

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malajikuai opened this issue Jun 7, 2022 · 1 comment
Closed

对比损失函数 #3

malajikuai opened this issue Jun 7, 2022 · 1 comment

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@malajikuai
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W = NNs.defineRandomNameParam([args.latdim, args.latdim])
pckHyperULat = tf.nn.l2_normalize(tf.nn.embedding_lookup(hyperULats[i], uniqUids), axis=1) @ W
这里的W是一个参数矩阵，为什么计算损失的时候需要用到这个矩阵？这个矩阵是可学习的吗？
@akaxlh
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akaxlh commented Jun 7, 2022

Yes, W is a learnable parametric matrix. It is empirically used to eliminate the (potential) gap between the hypergraph embedding space and the GNN embedding space.

@akaxlh akaxlh closed this as completed Jun 13, 2022
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@akaxlh @malajikuai