Continual Learning is considered a key step toward next-generation Artificial Intelligence. Among various methods, replay-based approaches that maintain and replay a small episodic memory of previous samples are one of the most successful strategies against catastrophic forgetting. However, since forgetting is inevitable given bounded memory and unbounded tasks, how to forget is a problem continual learning must address. Therefore, beyond simply avoiding catastrophic forgetting, an under-explored issue is how to reasonably forget while ensuring the merits of human memory, including 1. storage efficiency, 2. generalizability, and 3. some interpretability. To achieve these simultaneously, our paper proposes a new saliency-augmented memory completion framework for continual learning, inspired by recent discoveries in memory completion separation in cognitive neuroscience. Specifically, we innovatively propose to store the part of the image most important to the tasks in episodic memory by saliency map extraction and memory encoding. When learning new tasks, previous data from memory are inpainted by an adaptive data generation module, which is inspired by how humans complete episodic memory. The module's parameters are shared across all tasks and it can be jointly trained with a continual learning classifier as bilevel optimization. Extensive experiments on several continual learning and image classification benchmarks demonstrate the proposed method's effectiveness and efficiency.

As the societal impact of Deep Neural Networks (DNNs) grows, the goals for advancing DNNs become more complex and diverse, ranging from improving a conventional model accuracy metric to infusing advanced human virtues such as fairness, accountability, transparency (FaccT), and unbiasedness. Recently, techniques in Explainable Artificial Intelligence (XAI) are attracting considerable attention, and have tremendously helped Machine Learning (ML) engineers in understanding AI models. However, at the same time, we started to witness the emerging need beyond XAI among AI communities; based on the insights learned from XAI, how can we better empower ML engineers in steering their DNNs so that the model's reasonableness and performance can be improved as intended? This article provides a timely and extensive literature overview of the field Explanation-Guided Learning (EGL), a domain of techniques that steer the DNNs' reasoning process by adding regularization, supervision, or intervention on model explanations. In doing so, we first provide a formal definition of EGL and its general learning paradigm. Secondly, an overview of the key factors for EGL evaluation, as well as summarization and categorization of existing evaluation procedures and metrics for EGL are provided. Finally, the current and potential future application areas and directions of EGL are discussed, and an extensive experimental study is presented aiming at providing comprehensive comparative studies among existing EGL models in various popular application domains, such as Computer Vision (CV) and Natural Language Processing (NLP) domains.

从传统上讲，地球系统（例如天气和气候）的预测依赖于具有复杂物理模型的数值模拟，因此在计算中既昂贵又对领域专业知识的需求既昂贵。在过去十年中时空地球观察数据的爆炸性增长中，应用深度学习（DL）的数据驱动模型表明了各种地球系统预测任务的潜力。尽管在其他领域取得了广泛的成功，但作为新兴DL架构的变压器在该领域的采用量有限。在本文中，我们提出了Earthformer，这是一种用于地球系统预测的时空变压器。 Earthformer基于一个通用，灵活和有效的时空注意块，名为Cuboid的注意力。这个想法是将数据分解为立方体，并平行应用立方体级别的自我注意力。这些立方体与全球矢量的集合进一步相关。我们对MovingMnist数据集和新提出的混沌N体MNIST数据集进行了实验，以验证Cuboid注意的有效性，并找出地球形式的最佳设计。关于降水现象和El Nino/Southern振荡（ENSO）预测的两个现实基准测试的实验表明，Earthformer实现了最新的性能。

对于黑盒攻击，替代模型和受害者模型之间的差距通常很大，这表现为弱攻击性能。通过观察到，可以通过同时攻击多样的模型来提高对抗性示例的可传递性，并提出模型增强方法，这些模型通过使用转换图像模拟不同的模型。但是，空间域的现有转换不会转化为显着多样化的增强模型。为了解决这个问题，我们提出了一种新型的频谱模拟攻击，以针对正常训练和防御模型制作更容易转移的对抗性例子。具体而言，我们将频谱转换应用于输入，从而在频域中执行模型增强。从理论上讲，我们证明了从频域中得出的转换导致不同的频谱显着图，这是我们提出的指标，以反映替代模型的多样性。值得注意的是，我们的方法通常可以与现有攻击结合使用。 Imagenet数据集的广泛实验证明了我们方法的有效性，\ textit {e.g。}，攻击了九个最先进的防御模型，其平均成功率为\ textbf {95.4 \％}。我们的代码可在\ url {https://github.com/yuyang-long/ssa}中获得。

尽管在现代深度神经网络（DNN）中的解释技术取得了快速的进步，其中主要重点是处理“如何产生解释”，但先进的研究问题，这些问题研究了解释本身的质量（例如，解释是否准确” ）并提高解释质量（例如，“如何调整模型以在解释不准确时生成更准确的解释”）仍然相对较小。为了指导该模型朝着更好的解释，解释监督的技术（在模型解释中增加了监督信号）已开始对提高深度神经网络的普遍性和内在解释性的影响显示出令人鼓舞的影响。然而，由于几个固有的挑战，有关监督解释的研究，特别是在通过显着图代表的基于视觉的应用中，正处于早期阶段：1）人类解释注释边界的不准确，2）人类解释注释区域的不完整， 3）人类注释和模型解释图之间的数据分布不一致。为了应对挑战，我们提出了一个通用的RES框架，用于通过开发一个新的目标来指导视觉解释，该目标可以处理人类注释不准确的边界，不完整的区域和不一致的分布，并具有对模型通用性的理论理由。在两个现实世界图像数据集上进行的广泛实验证明了该框架在增强解释的合理性和骨干DNNS模型的性能方面的有效性。

作为表示空间信息的基本数据格式，深度图广泛用于信号处理和计算机视觉字段中。通过激光扫描仪或激光器等设备的快速发展，生产大量的高精度深度图。因此，迫切需要探索具有更好的压缩比的新型压缩方法，用于高精度深度图。利用广泛的深入学习环境，我们提出了一种高精度深度图的端到端学习的无损压缩方法。整个过程由两个子过程组成，命名为深度映射的预处理和处理的深度图的深度无损压缩。深度无损压缩网络由两个子网组成，名为Locky压缩网络和无损压缩网络。我们利用伪剩余的概念来指导剩余分配的发电，避免引入上下文模型。我们的端到端无损压缩网络通过工程编解码器实现竞争性能，并且计算成本低。

In this paper, we study the problem of knowledge-intensive text-to-SQL, in which domain knowledge is necessary to parse expert questions into SQL queries over domain-specific tables. We formalize this scenario by building a new Chinese benchmark KnowSQL consisting of domain-specific questions covering various domains. We then address this problem by presenting formulaic knowledge, rather than by annotating additional data examples. More concretely, we construct a formulaic knowledge bank as a domain knowledge base and propose a framework (ReGrouP) to leverage this formulaic knowledge during parsing. Experiments using ReGrouP demonstrate a significant 28.2% improvement overall on KnowSQL.

Weakly-supervised object localization aims to indicate the category as well as the scope of an object in an image given only the image-level labels. Most of the existing works are based on Class Activation Mapping (CAM) and endeavor to enlarge the discriminative area inside the activation map to perceive the whole object, yet ignore the co-occurrence confounder of the object and context (e.g., fish and water), which makes the model inspection hard to distinguish object boundaries. Besides, the use of CAM also brings a dilemma problem that the classification and localization always suffer from a performance gap and can not reach their highest accuracy simultaneously. In this paper, we propose a casual knowledge distillation method, dubbed KD-CI-CAM, to address these two under-explored issues in one go. More specifically, we tackle the co-occurrence context confounder problem via causal intervention (CI), which explores the causalities among image features, contexts, and categories to eliminate the biased object-context entanglement in the class activation maps. Based on the de-biased object feature, we additionally propose a multi-teacher causal distillation framework to balance the absorption of classification knowledge and localization knowledge during model training. Extensive experiments on several benchmarks demonstrate the effectiveness of KD-CI-CAM in learning clear object boundaries from confounding contexts and addressing the dilemma problem between classification and localization performance.

Dynamic treatment regimes assign personalized treatments to patients sequentially over time based on their baseline information and time-varying covariates. In mobile health applications, these covariates are typically collected at different frequencies over a long time horizon. In this paper, we propose a deep spectral Q-learning algorithm, which integrates principal component analysis (PCA) with deep Q-learning to handle the mixed frequency data. In theory, we prove that the mean return under the estimated optimal policy converges to that under the optimal one and establish its rate of convergence. The usefulness of our proposal is further illustrated via simulations and an application to a diabetes dataset.

Nowadays, time-stamped web documents related to a general news query floods spread throughout the Internet, and timeline summarization targets concisely summarizing the evolution trajectory of events along the timeline. Unlike traditional document summarization, timeline summarization needs to model the time series information of the input events and summarize important events in chronological order. To tackle this challenge, in this paper, we propose a Unified Timeline Summarizer (UTS) that can generate abstractive and extractive timeline summaries in time order. Concretely, in the encoder part, we propose a graph-based event encoder that relates multiple events according to their content dependency and learns a global representation of each event. In the decoder part, to ensure the chronological order of the abstractive summary, we propose to extract the feature of event-level attention in its generation process with sequential information remained and use it to simulate the evolutionary attention of the ground truth summary. The event-level attention can also be used to assist in extracting summary, where the extracted summary also comes in time sequence. We augment the previous Chinese large-scale timeline summarization dataset and collect a new English timeline dataset. Extensive experiments conducted on these datasets and on the out-of-domain Timeline 17 dataset show that UTS achieves state-of-the-art performance in terms of both automatic and human evaluations.