You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: Siamese Networks Koch et al. (2015) can be interpreted asa single layer message-passing iteration of our model, and using the same initialnode embedding (5) {\bf x}_{i}^{(0)}=(\phi(x_{i}),h_{i}) , using a non-trainableedge feature\varphi({\bf x}_{i},{\bf x}_{j})=\|\phi(x_{i})-\phi(x_{j})\|~{},~{}\tilde{A}^{(0)}=\text{softmax}(-\varphi)~{},and resulting label estimation\hat{Y}_{*}=\sum_{j}\tilde{A}_{*,j}^{(0)}\langle{\bf x}_{j}^{(0)},u\rangle~{},with u selecting the label field from {\bf x}. In this model,the learning is reduced to learning image embeddings \phi(x_{i}) whoseeuclidean metric is consistent with the label similarities. Since then, great progress has been done in one-shot learning. Koch et al. (2015) presented a deep-learning model based on computing the pair-wise distance between samples using Siamese Networks, then, this learned distance can be used to solve one-shot problems by k-nearest neighbors classification. Vinyals et al. (2016) Presented an end-to-end trainable k-nearest neighbors using the cosine distance, they also introduced a contextual mechanism using an attention LSTM model that takes into account all the samples of the subset \mathcal{T} when computing the pair-wise distance between samples. Snell et al. (2017) extended the work from Vinyals et al. (2016), by using euclidean distance instead of cosine which provided significant improvements, they also build a prototype representation of each class for the few-shot learning scenario. Mehrotra & Dukkipati (2017) trained a deep residual network together with a generative model to approximate the pair-wise distance between samples.

Question: What is the difference between Siamese Network and our works?
Learned distance from Siamese network can be used to solve one-shot problems. This network can play a role as a single layer message-passing iteration of our model.