Neural networks have achieved remarkable empirical performance, while the
current theoretical analysis is not adequate for understanding their success,
e.g., the Neural Tangent Kernel approach fails to capture their key feature
learning ability, while recent analyses on feature learning are typically
problem-specific. This work proposes a unified analysis framework for two-layer
networks trained by gradient descent. The framework is centered around the
principle of feature learning from gradients, and its effectiveness is
demonstrated by applications in several prototypical problems, such as mixtures
of Gaussians and parity functions. The framework also sheds light on
interesting network learning phenomena such as feature learning beyond kernels
and the lottery ticket hypothesis.

神经网络理论分析不足，该研究提出了一个基于梯度的特征学习分析框架，应用于混合高斯函数和奇偶函数等问题，并揭示了网络学习现象与特征学习的关系。

神经网络通过梯度特征学习的可证实保证

Provable Guarantees for Neural Networks via Gradient Feature Learning

Learning semantic segmentation from weakly-labeled (e.g., image tags only)
data is challenging since it is hard to infer dense object regions from sparse
semantic tags. Despite being broadly studied, most current efforts directly
learn from limited semantic annotations carried by individual image or image
pairs, and struggle to obtain integral localization maps. Our work alleviates
this from a novel perspective, by exploring rich semantic contexts
synergistically among abundant weakly-labeled training data for network
learning and inference. In particular, we propose regional semantic contrast
and aggregation (RCA) . RCA is equipped with a regional memory bank to store
massive, diverse object patterns appearing in training data, which acts as
strong support for exploration of dataset-level semantic structure.
Particularly, we propose i) semantic contrast to drive network learning by
contrasting massive categorical object regions, leading to a more holistic
object pattern understanding, and ii) semantic aggregation to gather diverse
relational contexts in the memory to enrich semantic representations. In this
manner, RCA earns a strong capability of fine-grained semantic understanding,
and eventually establishes new state-of-the-art results on two popular
benchmarks, i.e., PASCAL VOC 2012 and COCO 2014.

探索丰富的语义上下文，通过区域语义对比和聚合技术实现从弱标注数据中学习语义分割，取得了当前最先进的成果。