这样在做softmax计算的时候,每个x的值被固定在[-1,1]之间。但是这样会导致loss在高值处饱和,因为对于较大或者较小的相似度,最后都趋近于极值。因此给x加一个调节系数可以让模型更好的拟合。 与GNN结合 这里GNN的输入包括入度和出度的邻接矩阵,以及正则后的商品embedding矩阵。输出为经过n次迭代更新后的embedding。
一方面,hyper-connections提高了训练稳定性,减少了loss spike的产生: 另一方面,hyper-connections有更好的效果: 此外,比较了不同connection方式的连接矩阵,其中PTB为parallel transformer block: NGPT: Normalized Transformer with Representation Learning on the Hypersphere 本文提出nGPT方法,该方法中所有的embeddings, MLP...
For the NOCS map heads, we use two loss functions: a standard softmax loss function for classification, and the following soft L1 loss function for regression which makes learning more robust. L(y, y∗) = 1 n 5 (y − y∗)2, |y − y∗| ≤ ...
Cross-Attention Integration Local and global features are combined through cross-attention Attention weights are computed on normalized representations: A = softmax ( Q l K g T / d ) Output preserves unit norm through final normalization Representation Flow...
MPSCnnLogSoftMaxGradientNode MPSCnnLogSoftMaxNode MPSCnnLoss MPSCnnLossDataDescriptor MPSCnnLossDescriptor MPSCnnLossLabels MPSCnnLossNode MPSCnnLossType MPSCnnMultiply MPSCnnMultiplyGradient MPSCnnNeuron MPSCnnNeuronAbsolute MPSCnnNeuronAbsoluteNode MPSCnnNeuronElu MPSCnnNeuronEluNode MPSCnnNeu...
MPSCnnLogSoftMaxGradientNode MPSCnnLogSoftMaxNode MPSCnnLoss MPSCnnLossDataDescriptor MPSCnnLossDescriptor MPSCnnLossLabels MPSCnnLossNode MPSCnnLossType MPSCnnMultiply MPSCnnMultiplyGradient MPSCnnNeuron MPSCnnNeuronAbsolute MPSCnnNeuronAbsoluteNode MPSCnnNeuronElu MPSCnnNeuronEluNode MPSCnnNeuronExponential...
In this section, we introduce the proposed loss function named normalized virtual softmax loss. In addition, compare the effects of the proposed loss function and the original softmax loss. Finally, we present the whole model jointly using the NV-softmax loss and triplet loss. 4.1. Normalized...
From Table 7, it has been explored that, among the considered existing algorithms SoftMax classifier has exposed better classification performance with 75.42% accuracy. However, the proposed system has explored superior performance with 98% accuracy. This better performance has been due to the proposed...