The lack of explainability limits the adoption of deep learning models in
clinical practice. While methods exist to improve the understanding of such
models, these are mainly saliency-based and developed for classification,
despite many important tasks in medical imaging being continuous regression
problems. Therefore, in this work, we present ExPeRT: an explainable
prototype-based model specifically designed for regression tasks. Our proposed
model makes a sample prediction from the distances to a set of learned
prototypes in latent space, using a weighted mean of prototype labels. The
distances in latent space are regularized to be relative to label differences,
and each of the prototypes can be visualized as a sample from the training set.
The image-level distances are further constructed from patch-level distances,
in which the patches of both images are structurally matched using optimal
transport. We demonstrate our proposed model on the task of brain age
prediction on two image datasets: adult MR and fetal ultrasound. Our approach
achieved state-of-the-art prediction performance while providing insight in the
model's reasoning process.

本论文提出了 ExPeRT：一种可解释的基于原型的回归模型，该模型通过学习的原型的距离和标签的加权平均值进行样本预测，并实现了对成人 MR 和胎儿超声图像数据集的脑龄预测的最新性能。

可解释回归的原型学习

Prototype Learning for Explainable Regression

The role of an expert in the decision-making process is crucial, as the final
recommendation depends on his disposition, clarity of mind, experience, and
knowledge of the problem. However, the recommendation also depends on their
honesty. But what if the expert is dishonest? Then, the answer on how difficult
it is to manipulate in a given case becomes essential. In the presented work,
we consider manipulation of a ranking obtained by comparing alternatives in
pairs. More specifically, we propose an algorithm for finding an almost optimal
way to swap the positions of two selected alternatives. Thanks to this, it is
possible to determine how difficult such manipulation is in a given case.
Theoretical considerations are illustrated by a practical example.

本研究考虑了专家在决策过程中的角色和专家的诚实度对决策结果的影响，提出了一种基于排名交换的算法来讨论排名数据的可操纵性，并通过实例进行了阐述。

一对替代方案的几乎最优操纵

Almost optimal manipulation of a pair of alternatives

The task of learning a reward function from expert demonstrations suffers
from high sample complexity as well as inherent limitations to what can be
learned from demonstrations in a given environment. As the samples used for
reward learning require human input, which is generally expensive, much effort
has been dedicated towards designing more sample-efficient algorithms.
Moreover, even with abundant data, current methods can still fail to learn
insightful reward functions that are robust to minor changes in the environment
dynamics. We approach these challenges differently than prior work by improving
the sample-efficiency as well as the robustness of learned rewards through
adaptively designing a sequence of demonstration environments for the expert to
act in. We formalise a framework for this environment design process in which
learner and expert repeatedly interact, and construct algorithms that actively
seek information about the rewards by carefully curating environments for the
human to demonstrate the task in.

通过适应性设计专家演示环境，改善学习效率和鲁棒性，解决基于专家演示学习和环境动力学变化下的奖励函数学习挑战。

逆强化学习环境设计

Environment Design for Inverse Reinforcement Learning

Institutions of higher learning, research institutes and other scientific
research units have abundant scientific and technological resources of experts
and scholars, and these talents with great scientific and technological
innovation ability are an important force to promote industrial upgrading. The
scientific and technological resources of experts and scholars are mainly
composed of basic attributes and scientific research achievements. The basic
attributes include information such as research interests, institutions, and
educational work experience. However, due to information asymmetry and other
reasons, the scientific and technological resources of experts and scholars
cannot be connected with the society in a timely manner, and social needs
cannot be accurately matched with experts and scholars. Therefore, it is very
necessary to build an expert and scholar information database and provide
relevant expert and scholar retrieval services. This paper sorts out the
related research work in this field from four aspects: text relation
extraction, text knowledge representation learning, text vector retrieval and
visualization system.

本文介绍了从文本关系提取、文本知识表示学习、文本向量检索和可视化系统等四个方面整理了相关研究工作，旨在建立专家和学者信息数据库并提供相关的检索服务。

专家学者科技资源检索

Retrieval of Scientific and Technological Resources for Experts and Scholars

One-stage long-tailed recognition methods improve the overall performance in
a "seesaw" manner, i.e., either sacrifice the head's accuracy for better tail
classification or elevate the head's accuracy even higher but ignore the tail.
Existing algorithms bypass such trade-off by a multi-stage training process:
pre-training on imbalanced set and fine-tuning on balanced set. Though
achieving promising performance, not only are they sensitive to the
generalizability of the pre-trained model, but also not easily integrated into
other computer vision tasks like detection and segmentation, where pre-training
of classifiers solely is not applicable. In this paper, we propose a one-stage
long-tailed recognition scheme, ally complementary experts (ACE), where the
expert is the most knowledgeable specialist in a sub-set that dominates its
training, and is complementary to other experts in the less-seen categories
without being disturbed by what it has never seen. We design a
distribution-adaptive optimizer to adjust the learning pace of each expert to
avoid over-fitting. Without special bells and whistles, the vanilla ACE
outperforms the current one-stage SOTA method by 3-10% on CIFAR10-LT,
CIFAR100-LT, ImageNet-LT and iNaturalist datasets. It is also shown to be the
first one to break the "seesaw" trade-off by improving the accuracy of the
majority and minority categories simultaneously in only one stage. Code and
trained models are at this https URL

本文提出了一种基于 ACE（Ally Complementary Experts）的单阶长尾识别方法，在不需要专门的分类器预训练的情况下，在 CIFAR10-LT，CIFAR100-LT，ImageNet-LT 和 iNaturalist 数据集上实现了显着的性能提升，打破了 “见锯齿现象” 的平衡，并同时提高了多数和少数类别的准确性。