基于可解释的机器学习，提出了一种名为InterOPT优化操作参数的算法，并通过优化页岩气体开发来证明。InterOpt由三个部分组成：神经网络用于构建矢量空间中实际钻孔和液压压裂过程的模拟器（即虚拟环境）；可解释的机器学习中的Sharpley价值方法用于分析每个井中地质和操作参数的影响（即单个井功能影响分析）；并进行集合随机最大似然（ENRML）以优化操作参数，以全面提高页岩气发展的效率并降低平均成本。在实验中，InterOPT根据其特定地质条件为每个井提供了不同的钻孔和破裂计划，并最终在104井的案例研究中获得了9.7％的平均成本降低9.7％。

拟合科学数据的部分微分方程（PDE）可以用可解释的机制来代表各种以数学为导向的受试者的物理定律。从科学数据中发现PDE的数据驱动的发现蓬勃发展，作为对自然界中复杂现象进行建模的新尝试，但是当前实践的有效性通常受数据的稀缺性和现象的复杂性的限制。尤其是，从低质量数据中发现具有高度非线性系数的PDE在很大程度上已经不足。为了应对这一挑战，我们提出了一种新颖的物理学指导学习方法，该方法不仅可以编码观察知识，例如初始和边界条件，而且还包含了基本的物理原理和法律来指导模型优化。我们从经验上证明，所提出的方法对数据噪声和稀疏性更为强大，并且可以将估计误差较大。此外，我们第一次能够发现具有高度非线性系数的PDE。凭借有希望的性能，提出的方法推动了PDE的边界，这可以通过机器学习模型来进行科学发现。

虽然深受深度学习在各种科学和工程问题中，由于其强大的高维非线性映射能力，但它在科学知识发现中使用有限。在这项工作中，我们提出了一种基于深度学习的框架，以发现基于高分辨率微观模拟数据的粘性重力电流的宏观控制方程，而无需先前了解基础术语。对于具有不同粘度比的两个典型方案，基于深度学习的公式完全捕获与理论上派生的术语相同的主导术语，以描述验证所提出的框架的长期渐近行为。然后获得未知的宏观方程以描述用于描述短期行为，并且最终发现了额外的深度学习补偿项。后检测的比较表明，基于深度学习的PDE实际上比理论上衍生的PDE更好地在预测长期和短期制度中预测演化粘性重力电流。此外，拟议的框架被证明是对训练的非偏见数据噪声非常稳健，这高达20％。因此，所提出的深度学习框架表明，从原始实验或模拟导致数据空间中发现了在科学语义空间中发现了未经验证的内在法律的相当潜力。

在本文中，我们提出了一种新颖的指导性扩散纯化方法，以防御对抗攻击。我们的模型在CIFAR-10数据集上的PGD-L_INF攻击（EPS = 8/255）下实现了89.62％的鲁棒精度。我们首先探讨了未引导的扩散模型与随机平滑之间的基本相关性，从而使我们能够将模型应用于认证的鲁棒性。经验结果表明，当认证的L2半径R大于0.5时，我们的模型优于随机平滑的5％。

结构分布，即组合空间的分布，通常用于学习观察到数据的潜在概率表示。然而，缩放这些模型是由高计算和内存复杂度相对于潜在表示的大小的瓶颈。诸如隐藏的马尔可夫模型（HMMS）和概率的无内容语法（PCFG）的常见模型在隐藏状态的数量中需要时间和空间二次和立方。这项工作展示了一种简单的方法来降低大类结构化模型的计算和内存复杂性。我们展示通过将中央推理步骤视为矩阵 - 矢量产品，并使用低秩约束，我们可以通过等级进行模型表达性和速度。用神经参数化结构化模型进行语言建模，复音音乐建模，无监督语法诱导和视频建模的实验表明，我们的方法在提供实用加速度的同时匹配大状态空间的标准模型的准确性。

序列模型是现代NLP系统的关键组成部分，但它们的预测难以解释。我们考虑虽然可以解释单个模型预测的基础，但是可以解释各种模型预测的上下文的模型解释。通过解决组合优化来找到顺序律师：最佳理由是输入令牌的最小子集，这些令牌将预测与完整序列相同的输出。枚举所有子集是棘手的，因此我们提出了一种高效的贪婪算法来近似这个目标。称为贪婪合理化的算法适用于任何模型。对于这种方法有效，模型应该在对上下文的不完整子集进行预测时形成兼容的条件分布。这种情况可以用短的微调步骤强制执行。我们研究语言建模与机器翻译的贪婪合理化。与现有的基线相比，贪婪合理化是最优化组合目标的，并提供最忠实的理由。在注释的顺序理由的新数据集中，贪婪的理由与人类理由最相似。

We describe an open-source toolkit for neural machine translation (NMT). The toolkit prioritizes efficiency, modularity, and extensibility with the goal of supporting NMT research into model architectures, feature representations, and source modalities, while maintaining competitive performance and reasonable training requirements. The toolkit consists of modeling and translation support, as well as detailed pedagogical documentation about the underlying techniques.

Deep learning models can achieve high accuracy when trained on large amounts of labeled data. However, real-world scenarios often involve several challenges: Training data may become available in installments, may originate from multiple different domains, and may not contain labels for training. Certain settings, for instance medical applications, often involve further restrictions that prohibit retention of previously seen data due to privacy regulations. In this work, to address such challenges, we study unsupervised segmentation in continual learning scenarios that involve domain shift. To that end, we introduce GarDA (Generative Appearance Replay for continual Domain Adaptation), a generative-replay based approach that can adapt a segmentation model sequentially to new domains with unlabeled data. In contrast to single-step unsupervised domain adaptation (UDA), continual adaptation to a sequence of domains enables leveraging and consolidation of information from multiple domains. Unlike previous approaches in incremental UDA, our method does not require access to previously seen data, making it applicable in many practical scenarios. We evaluate GarDA on two datasets with different organs and modalities, where it substantially outperforms existing techniques.

The development of social media user stance detection and bot detection methods rely heavily on large-scale and high-quality benchmarks. However, in addition to low annotation quality, existing benchmarks generally have incomplete user relationships, suppressing graph-based account detection research. To address these issues, we propose a Multi-Relational Graph-Based Twitter Account Detection Benchmark (MGTAB), the first standardized graph-based benchmark for account detection. To our knowledge, MGTAB was built based on the largest original data in the field, with over 1.55 million users and 130 million tweets. MGTAB contains 10,199 expert-annotated users and 7 types of relationships, ensuring high-quality annotation and diversified relations. In MGTAB, we extracted the 20 user property features with the greatest information gain and user tweet features as the user features. In addition, we performed a thorough evaluation of MGTAB and other public datasets. Our experiments found that graph-based approaches are generally more effective than feature-based approaches and perform better when introducing multiple relations. By analyzing experiment results, we identify effective approaches for account detection and provide potential future research directions in this field. Our benchmark and standardized evaluation procedures are freely available at: https://github.com/GraphDetec/MGTAB.

As one of the prevalent methods to achieve automation systems, Imitation Learning (IL) presents a promising performance in a wide range of domains. However, despite the considerable improvement in policy performance, the corresponding research on the explainability of IL models is still limited. Inspired by the recent approaches in explainable artificial intelligence methods, we proposed a model-agnostic explaining framework for IL models called R2RISE. R2RISE aims to explain the overall policy performance with respect to the frames in demonstrations. It iteratively retrains the black-box IL model from the randomized masked demonstrations and uses the conventional evaluation outcome environment returns as the coefficient to build an importance map. We also conducted experiments to investigate three major questions concerning frames' importance equality, the effectiveness of the importance map, and connections between importance maps from different IL models. The result shows that R2RISE successfully distinguishes important frames from the demonstrations.