We propose a novel approach for interpreting deep embeddings in the context
of patient clustering. We evaluate our approach on a dataset of participants
with type 2 diabetes from the UK Biobank, and demonstrate clinically meaningful
insights into disease progression patterns.

本篇研究提出了一种新的深度嵌入解释方法用于患者聚类，以英国生物银行中的 2 型糖尿病参与者数据为评估标准，展示了对疾病进展模式的临床意义上有意义的见解。

深度嵌入解释疾病进展聚类

Interpreting deep embeddings for disease progression clustering

The focus in machine learning has branched beyond training classifiers on a
single task to investigating how previously acquired knowledge in a source
domain can be leveraged to facilitate learning in a related target domain,
known as inductive transfer learning. Three active lines of research have
independently explored transfer learning using neural networks. In weight
transfer, a model trained on the source domain is used as an initialization
point for a network to be trained on the target domain. In deep metric
learning, the source domain is used to construct an embedding that captures
class structure in both the source and target domains. In few-shot learning,
the focus is on generalizing well in the target domain based on a limited
number of labeled examples. We compare state-of-the-art methods from these
three paradigms and also explore hybrid adapted-embedding methods that use
limited target-domain data to fine tune embeddings constructed from
source-domain data. We conduct a systematic comparison of methods in a variety
of domains, varying the number of labeled instances available in the target
domain ($k$), as well as the number of target-domain classes. We reach three
principal conclusions: (1) Deep embeddings are far superior, compared to weight
transfer, as a starting point for inter-domain transfer or model re-use (2) Our
hybrid methods robustly outperform every few-shot learning and every deep
metric learning method previously proposed, with a mean error reduction of 34%
over state-of-the-art. (3) Among loss functions for discovering embeddings, the
histogram loss (Ustinova & Lempitsky, 2016) is most robust. We hope our results
will motivate a unification of research in weight transfer, deep metric
learning, and few-shot learning.

探讨了机器学习中的归纳迁移学习，比较了基于权重转移、深度度量学习和少样本学习三种方法的最新研究，提出了一种基于少量目标域数据微调基于源域数据的嵌入的混合适应嵌入方法，证明了它们在各种域和不同数量和类别的标记数据中的有效性。

适应性深度嵌入：k- Shot 归纳迁移学习方法的综合

Adapted Deep Embeddings: A Synthesis of Methods for $k$-Shot Inductive  Transfer Learning

Deep embeddings answer one simple question: How similar are two images?
Learning these embeddings is the bedrock of verification, zero-shot learning,
and visual search. The most prominent approaches optimize a deep convolutional
network with a suitable loss function, such as contrastive loss or triplet
loss. While a rich line of work focuses solely on the loss functions, we show
in this paper that selecting training examples plays an equally important role.
We propose distance weighted sampling, which selects more informative and
stable examples than traditional approaches. In addition, we show that a simple
margin based loss is sufficient to outperform all other loss functions. We
evaluate our approach on the Stanford Online Products, CAR196, and the
CUB200-2011 datasets for image retrieval and clustering, and on the LFW dataset
for face verification. Our method achieves state-of-the-art performance on all
of them.

本文提出了基于距离加权采样的深度嵌入方法，用于图像检索、聚类和人脸验证任务，取得了所有数据集上的最优性能。

深度嵌入学习中的采样问题

Sampling Matters in Deep Embedding Learning

We suggest a loss for learning deep embeddings. The new loss does not
introduce parameters that need to be tuned and results in very good embeddings
across a range of datasets and problems. The loss is computed by estimating two
distribution of similarities for positive (matching) and negative
(non-matching) sample pairs, and then computing the probability of a positive
pair to have a lower similarity score than a negative pair based on the
estimated similarity distributions. We show that such operations can be
performed in a simple and piecewise-differentiable manner using 1D histograms
with soft assignment operations. This makes the proposed loss suitable for
learning deep embeddings using stochastic optimization. In the experiments, the
new loss performs favourably compared to recently proposed alternatives.

本论文提出了一种用于学习深度嵌入的损失函数，该函数使用正负样本对的相似度分布来计算概率，不需要调整参数，适合使用随机优化方法进行优化，并在实验中表现良好。