Estimating heterogeneous treatment effects across individuals has attracted
growing attention as a statistical tool for performing critical
decision-making. We propose a Bayesian inference framework that quantifies the
uncertainty in treatment effect estimation to support decision-making in a
relatively small sample size setting. Our proposed model places Gaussian
process priors on the nonparametric components of a semiparametric model called
a partially linear model. This model formulation has three advantages. First,
we can analytically compute the posterior distribution of a treatment effect
without relying on the computationally demanding posterior approximation.
Second, we can guarantee that the posterior distribution concentrates around
the true one as the sample size goes to infinity. Third, we can incorporate
prior knowledge about a treatment effect into the prior distribution, improving
the estimation efficiency. Our experimental results show that even in the small
sample size setting, our method can accurately estimate the heterogeneous
treatment effects and effectively quantify its estimation uncertainty.

提出了一种贝叶斯推断框架来量化治疗效果估计的不确定性，支持在相对较小的样本量条件下进行决策，该方法在小样本量设置中能够准确估计异质治疗效应并有效量化其估计不确定性。

基于高斯过程的部分线性模型实现异质治疗效应估计的不确定性量化

Uncertainty Quantification in Heterogeneous Treatment Effect Estimation  with Gaussian-Process-Based Partially Linear Model

The growth and success of deep learning approaches can be attributed to two
major factors: availability of hardware resources and availability of large
number of training samples. For problems with large training databases, deep
learning models have achieved superlative performances. However, there are a
lot of \textit{small sample size or $S^3$} problems for which it is not
feasible to collect large training databases. It has been observed that deep
learning models do not generalize well on $S^3$ problems and specialized
solutions are required. In this paper, we first present a review of deep
learning algorithms for small sample size problems in which the algorithms are
segregated according to the space in which they operate, i.e. input space,
model space, and feature space. Secondly, we present Dynamic Attention Pooling
approach which focuses on extracting global information from the most
discriminative sub-part of the feature map. The performance of the proposed
dynamic attention pooling is analyzed with state-of-the-art ResNet model on
relatively small publicly available datasets such as SVHN, C10, C100, and
TinyImageNet.

本文从输入空间、模型空间和特征空间三个角度出发，综述深度学习中针对小样本问题的算法，特别地介绍了动态注意力池化方法。该方法通过对特征图最具差异性的子区域进行全局信息的提取，显著提升了 ResNet 模型在 SVHN、C10、C100 和 TinyImageNet 这些小规模公开数据集上的性能。

深度学习中小样本量问题的探究

Unravelling Small Sample Size Problems in the Deep Learning World

Convolutional Neural Networks have provided state-of-the-art results in
several computer vision problems. However, due to a large number of parameters
in CNNs, they require a large number of training samples which is a limiting
factor for small sample size problems. To address this limitation, we propose
SSF-CNN which focuses on learning the structure and strength of filters. The
structure of the filter is initialized using a dictionary-based filter learning
algorithm and the strength of the filter is learned using the small sample
training data. The architecture provides the flexibility of training with both
small and large training databases and yields good accuracies even with small
size training data. The effectiveness of the algorithm is first demonstrated on
MNIST, CIFAR10, and NORB databases, with a varying number of training samples.
The results show that SSF-CNN significantly reduces the number of parameters
required for training while providing high accuracies the test databases. On
small sample size problems such as newborn face recognition and Omniglot, it
yields state-of-the-art results. Specifically, on the IIITD Newborn Face
Database, the results demonstrate improvement in rank-1 identification accuracy
by at least 10%.

本文提出了一种基于学习滤波器结构和强度的 SSF-CNN 架构来解决小样本问题，并在 MNIST、CIFAR10、NORB 和新生儿人脸识别等领域得到了表现优异的结果。