Recently, multiple models proposed for link prediction (LP) demonstrate
impressive results on benchmark datasets. However, many popular benchmark
datasets often assume that dataset samples are drawn from the same distribution
(i.e., IID samples). In real-world situations, this assumption is often
incorrect; since uncontrolled factors may lead train and test samples to come
from separate distributions. To tackle the distribution shift problem, recent
work focuses on creating datasets that feature distribution shifts and
designing generalization methods that perform well on the new data. However,
those studies only consider distribution shifts that affect {\it node-} and
{\it graph-level} tasks, thus ignoring link-level tasks. Furthermore,
relatively few LP generalization methods exist. To bridge this gap, we
introduce a set of LP-specific data splits which utilizes structural properties
to induce a controlled distribution shift. We verify the shift's effect
empirically through evaluation of different SOTA LP methods and subsequently
couple these methods with generalization techniques. Interestingly, LP-specific
methods frequently generalize poorly relative to heuristics or basic GNN
methods. Finally, this work provides analysis to uncover insights for enhancing
LP generalization. Our code is available at:
\href{https://github.com/revolins/LPStructGen}{this https URL}

该论文通过引入针对链接预测的数据划分方式，研究了分布偏移对链接预测泛化性能的影响，并探索了提高链接预测泛化性能的方法。

图上分布偏移下链接预测方法的一般化能力研究

Understanding the Generalizability of Link Predictors Under Distribution  Shifts on Graphs

Numerous Deep Learning (DL) models have been developed for a large spectrum
of medical image analysis applications, which promises to reshape various
facets of medical practice. Despite early advances in DL model validation and
implementation, which encourage healthcare institutions to adopt them, some
fundamental questions remain: are the DL models capable of generalizing? What
causes a drop in DL model performances? How to overcome the DL model
performance drop? Medical data are dynamic and prone to domain shift, due to
multiple factors such as updates to medical equipment, new imaging workflow,
and shifts in patient demographics or populations can induce this drift over
time. In this paper, we review recent developments in generalization methods
for DL-based classification models. We also discuss future challenges,
including the need for improved evaluation protocols and benchmarks, and
envisioned future developments to achieve robust, generalized models for
medical image classification.

近年来，我们不断发展了大量用于医学图像分析的深度学习模型，但仍然存在诸多挑战，如模型泛化性能、性能下降原因和如何克服性能下降等。本研究回顾了基于深度学习的分类模型的泛化方法，并讨论了未来的挑战，包括改进评估协议和基准以及实现稳健、泛化的医学图像分类模型的未来发展。

医学图像分类的深度学习模型泛化

Generalizing deep learning models for medical image classification

Neural implicit shape representations are an emerging paradigm that offers
many potential benefits over conventional discrete representations, including
memory efficiency at a high spatial resolution. Generalizing across shapes with
such neural implicit representations amounts to learning priors over the
respective function space and enables geometry reconstruction from partial or
noisy observations. Existing generalization methods rely on conditioning a
neural network on a low-dimensional latent code that is either regressed by an
encoder or jointly optimized in the auto-decoder framework. Here, we formalize
learning of a shape space as a meta-learning problem and leverage
gradient-based meta-learning algorithms to solve this task. We demonstrate that
this approach performs on par with auto-decoder based approaches while being an
order of magnitude faster at test-time inference. We further demonstrate that
the proposed gradient-based method outperforms encoder-decoder based methods
that leverage pooling-based set encoders.

该论文讨论了神经隐式形状表示的基本概念，比传统的离散表示具有更好的记忆效率和更高的空间分辨率，并提出了一种基于梯度的元学习算法以提高形状空间的泛化能力，从而实现从部分或噪声观测数据中进行的几何重建。该方法在测试时间推断上比自动编码器的方法快一个数量级，同时超过基于编码器 - 解码器的池化网络集合编码器方法。