基于学习的方法有效地促进了图像压缩社区。同时，基于变异的自动编码器（VAE）的可变速率方法最近引起了很多关注，以避免使用一组不同的网络来用于各种压缩率。尽管已经取得了显着的性能，但一旦执行了多个压缩/减压操作，这些方法将很容易损坏，从而导致图像质量将被大幅下降并且会出现强大的伪像。因此，我们试图解决高保真的细度可变速率图像压缩的问题，并提出可逆激活变换（IAT）模块。我们以单个速率可逆神经网络（INN）模型（Qlevel）以数学可逆的方式实施IAT，并将质量级别（QLevel）送入IAT，以产生缩放和偏置张量。 IAT和QLEVEL一起为图像压缩模型提供了罚款可变速率控制的能力，同时更好地保持图像保真度。广泛的实验表明，配备了我们IAT模块的单率图像压缩模型具有实现可变速率控制而无需任何妥协的能力。并且我们的IAT包裹模型通过最新的基于学习的图像压缩方法获得了可比的利率延伸性能。此外，我们的方法的表现优于最新的可变速率图像压缩方法，尤其是在多次重新编码之后。

从随机数据中揭示隐藏的动态是一个具有挑战性的问题，因为随机性参与了数据的发展。当在许多情况下没有随机数据的轨迹时，问题就变得非常复杂。在这里，我们提出了一种方法，可以根据fokker-planck（FP）方程的弱形式有效地建模随机数据的动力学，该方程控制了布朗工艺中密度函数的演变。将高斯函数作为弱形式的FP方程式的测试函数，我们将衍生物传递到高斯函数，从而将衍生物传递到高斯函数，从而通过数据的期望值近似弱形式。使用未知术语的字典表示，将线性系统构建，然后通过回归解决，从而揭示数据的未知动力学。因此，我们以弱搭配回归（WCK）方法为其三个关键组成部分命名该方法：弱形式，高斯核的搭配和回归。数值实验表明我们的方法是灵活而快速的，它在多维问题中揭示了几秒钟内的动力学，并且可以轻松地扩展到高维数据，例如20个维度。 WCR还可以正确地识别具有可变依赖性扩散和耦合漂移的复杂任务的隐藏动力学，并且性能很强，在添加噪声的情况下，在情况下达到了高精度。

在这项工作中，我们在分配强化学习方面建立了最新的进步，以基于IQN提供模型的最新分配变体。我们通过使用GAN模型的生成器和鉴别器功能与分位数回归来实现这一目标，从而近似于状态返回分布的完整分位数。我们证明了基线数据集的性能提高-57 Atari 2600游戏。此外，我们使用算法来显示Atari游戏中风险敏感政策的最新培训表现，并通过政策优化和评估。

检测定向对象以及估计其旋转信息是用于分析遥感图像的一个关键步骤。尽管最近提出了许多方法，但大多数人直接学习在仅单独的一个（例如旋转角度）的监督下预测对象方向或仅为几（例如旋转角度）或几（例如若干坐标）地基值。在训练期间采用了关于提议和旋转信息回归的额外约束，在额外约束，在训练期间采用了更准确的对象检测。为此，我们创新地提出了一种通过Naive几何计算以一致的方式同时学习物体的水平提出，面向建议和旋转角度的机制，作为一个额外的稳定约束（参见图1）。提出了一个导向的中心先前引导标签分配策略，以进一步提高建议的质量，产生更好的性能。广泛的实验表明，配备我们的想法的模型显着优于基线，通过大幅度来实现新的最先进的结果，在推理期间没有任何额外的计算负担。我们提出的想法简单直观，可以随时实现。源代码和培训的型号涉及补充文件。

In this paper, we propose a robust 3D detector, named Cross Modal Transformer (CMT), for end-to-end 3D multi-modal detection. Without explicit view transformation, CMT takes the image and point clouds tokens as inputs and directly outputs accurate 3D bounding boxes. The spatial alignment of multi-modal tokens is performed implicitly, by encoding the 3D points into multi-modal features. The core design of CMT is quite simple while its performance is impressive. CMT obtains 73.0% NDS on nuScenes benchmark. Moreover, CMT has a strong robustness even if the LiDAR is missing. Code will be released at https://github.com/junjie18/CMT.

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.

Learning feature interactions is the key to success for the large-scale CTR prediction and recommendation. In practice, handcrafted feature engineering usually requires exhaustive searching. In order to reduce the high cost of human efforts in feature engineering, researchers propose several deep neural networks (DNN)-based approaches to learn the feature interactions in an end-to-end fashion. However, existing methods either do not learn both vector-wise interactions and bit-wise interactions simultaneously, or fail to combine them in a controllable manner. In this paper, we propose a new model, xDeepInt, based on a novel network architecture called polynomial interaction network (PIN) which learns higher-order vector-wise interactions recursively. By integrating subspace-crossing mechanism, we enable xDeepInt to balance the mixture of vector-wise and bit-wise feature interactions at a bounded order. Based on the network architecture, we customize a combined optimization strategy to conduct feature selection and interaction selection. We implement the proposed model and evaluate the model performance on three real-world datasets. Our experiment results demonstrate the efficacy and effectiveness of xDeepInt over state-of-the-art models. We open-source the TensorFlow implementation of xDeepInt: https://github.com/yanyachen/xDeepInt.

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.

Witnessing the impressive achievements of pre-training techniques on large-scale data in the field of computer vision and natural language processing, we wonder whether this idea could be adapted in a grab-and-go spirit, and mitigate the sample inefficiency problem for visuomotor driving. Given the highly dynamic and variant nature of the input, the visuomotor driving task inherently lacks view and translation invariance, and the visual input contains massive irrelevant information for decision making, resulting in predominant pre-training approaches from general vision less suitable for the autonomous driving task. To this end, we propose PPGeo (Policy Pre-training via Geometric modeling), an intuitive and straightforward fully self-supervised framework curated for the policy pretraining in visuomotor driving. We aim at learning policy representations as a powerful abstraction by modeling 3D geometric scenes on large-scale unlabeled and uncalibrated YouTube driving videos. The proposed PPGeo is performed in two stages to support effective self-supervised training. In the first stage, the geometric modeling framework generates pose and depth predictions simultaneously, with two consecutive frames as input. In the second stage, the visual encoder learns driving policy representation by predicting the future ego-motion and optimizing with the photometric error based on current visual observation only. As such, the pre-trained visual encoder is equipped with rich driving policy related representations and thereby competent for multiple visuomotor driving tasks. Extensive experiments covering a wide span of challenging scenarios have demonstrated the superiority of our proposed approach, where improvements range from 2% to even over 100% with very limited data. Code and models will be available at https://github.com/OpenDriveLab/PPGeo.

Interview has been regarded as one of the most crucial step for recruitment. To fully prepare for the interview with the recruiters, job seekers usually practice with mock interviews between each other. However, such a mock interview with peers is generally far away from the real interview experience: the mock interviewers are not guaranteed to be professional and are not likely to behave like a real interviewer. Due to the rapid growth of online recruitment in recent years, recruiters tend to have online interviews, which makes it possible to collect real interview data from real interviewers. In this paper, we propose a novel application named EZInterviewer, which aims to learn from the online interview data and provides mock interview services to the job seekers. The task is challenging in two ways: (1) the interview data are now available but still of low-resource; (2) to generate meaningful and relevant interview dialogs requires thorough understanding of both resumes and job descriptions. To address the low-resource challenge, EZInterviewer is trained on a very small set of interview dialogs. The key idea is to reduce the number of parameters that rely on interview dialogs by disentangling the knowledge selector and dialog generator so that most parameters can be trained with ungrounded dialogs as well as the resume data that are not low-resource. Evaluation results on a real-world job interview dialog dataset indicate that we achieve promising results to generate mock interviews. With the help of EZInterviewer, we hope to make mock interview practice become easier for job seekers.