Biometric authentication using gait has become a promising field due to its
unobtrusive nature. Recent approaches in model-based gait recognition
techniques utilize spatio-temporal graphs for the elegant extraction of gait
features. However, existing methods often rely on multi-scale operators for
extracting long-range relationships among joints resulting in biased weighting.
In this paper, we present HEATGait, a gait recognition system that improves the
existing multi-scale graph convolution by efficient hop-extraction technique to
alleviate the issue. Combined with preprocessing and augmentation techniques,
we propose a powerful feature extractor that utilizes ResGCN to achieve
state-of-the-art performance in model-based gait recognition on the CASIA-B
gait dataset.

本文介绍了 HEATGait—— 一种利用操作技术来提取 gait 特征的 gait 识别系统。该方法结合预处理和数据增强技术，利用 ResGCN 提取特征，在 CASIA-B gait 数据集上实现了最佳效果

基于图卷积的步态识别中的 HOP 提取邻接技术：HEATGait

HEATGait: Hop-Extracted Adjacency Technique in Graph Convolution based  Gait Recognition

Machine-learning models have demonstrated great success in learning complex
patterns that enable them to make predictions about unobserved data. In
addition to using models for prediction, the ability to interpret what a model
has learned is receiving an increasing amount of attention. However, this
increased focus has led to considerable confusion about the notion of
interpretability. In particular, it is unclear how the wide array of proposed
interpretation methods are related, and what common concepts can be used to
evaluate them.
We aim to address these concerns by defining interpretability in the context
of machine learning and introducing the Predictive, Descriptive, Relevant (PDR)
framework for discussing interpretations. The PDR framework provides three
overarching desiderata for evaluation: predictive accuracy, descriptive
accuracy and relevancy, with relevancy judged relative to a human audience.
Moreover, to help manage the deluge of interpretation methods, we introduce a
categorization of existing techniques into model-based and post-hoc categories,
with sub-groups including sparsity, modularity and simulatability. To
demonstrate how practitioners can use the PDR framework to evaluate and
understand interpretations, we provide numerous real-world examples. These
examples highlight the often under-appreciated role played by human audiences
in discussions of interpretability. Finally, based on our framework, we discuss
limitations of existing methods and directions for future work. We hope that
this work will provide a common vocabulary that will make it easier for both
practitioners and researchers to discuss and choose from the full range of
interpretation methods.

该研究呈现了机器学习的可解释性与 PDR 框架，探讨了现有解释方法的分类与优劣评估，提供了实际案例并讨论了未来研究方向。

可解释的机器学习：定义、方法和应用

Interpretable machine learning: definitions, methods, and applications

Reinforcement learning is concerned with identifying reward-maximizing
behaviour policies in environments that are initially unknown. State-of-the-art
reinforcement learning approaches, such as deep Q-networks, are model-free and
learn to act effectively across a wide range of environments such as Atari
games, but require huge amounts of data. Model-based techniques are more
data-efficient, but need to acquire explicit knowledge about the environment.
In this paper, we take a step towards using model-based techniques in
environments with a high-dimensional visual state space by demonstrating that
it is possible to learn system dynamics and the reward structure jointly. Our
contribution is to extend a recently developed deep neural network for video
frame prediction in Atari games to enable reward prediction as well. To this
end, we phrase a joint optimization problem for minimizing both video frame and
reward reconstruction loss, and adapt network parameters accordingly. Empirical
evaluations on five Atari games demonstrate accurate cumulative reward
prediction of up to 200 frames. We consider these results as opening up
important directions for model-based reinforcement learning in complex,
initially unknown environments.

本文介绍一种在高维视觉状态空间下学习动态系统和奖励函数的方法，将视觉帧预测的深度神经网络扩展为同时预测奖励，利用联合优化问题最小化奖励和视觉帧的重构误差，并在五个 Atari 游戏上经过实证评估，取得了高达 200 帧的准确累计奖励预测结果。