规模一直是改善机器学习绩效的主要驱动力，了解规模定律对于可持续模型质量绩效增长，长期资源计划和开​​发有效的系统基础架构以支持大规模模型的战略规划至关重要。在本文中，我们研究了DLRM样式推荐模型的经验缩放定律，特别是点击率（CTR）。我们观察到具有功率定律的模型质量尺度以及模型大小，数据大小和用于培训的计算量的常数。我们通过比较沿这些轴的不同缩放方案来表征沿三个不同资源维度的缩放效率，即数据，参数和计算。我们表明，对于正在研究的模型体系结构，参数缩放量不超出蒸汽，直到出现较高表现的模型体系结构之前，数据缩放是前进的路径。本研究解决的关键研究问题包括：建议模型规模是否可以可持续地按照规模定律预测？还是我们远离规模定律的预测？缩放的限制是什么？扩展法对长期硬件/系统开发的含义是什么？

随着在高风险决策中引入机器学习，确保算法公平已成为越来越重要的问题。为此，已经提出了许多关于公平性的数学定义，并且已经开发了多种优化技术，所有这些都旨在最大化明确的公平概念。但是，公平解决方案取决于训练数据的质量，并且对噪声高度敏感。最近的研究表明，鲁棒性（模型在看不见的数据上表现良好的能力）在解决新问题时应使用的策略类型起着重要作用，因此，测量这些策略的鲁棒性已成为一种基本问题。因此，在这项工作中，我们提出了一个新标准，以衡量各种公平优化策略的鲁棒性 - \ textit {稳健性比率}。我们使用三种最受欢迎​​的公平策略在五个最受欢迎的公平定义方面，在五个基准标记公平数据集上进行了多次广泛的实验。我们的实验从经验上表明，依赖阈值优化的公平方法对所有评估的数据集中的噪声非常敏感，尽管大多数表现优于其他方法。这与其他两种方法相反，这对于低噪声方案而言不太公平，但对于高噪声方案而言更公平。据我们所知，我们是第一个定量评估公平优化策略的鲁棒性的人。这可以作为选择各种数据集的最合适的公平策略的指南。

基于决策攻击对现实世界应用程序构成严重威胁，因为它将目标模型视为黑盒子，并且仅访问硬预测标签。最近已经努力减少查询的数量;然而，现有的基于决策攻击仍需要数千个疑问以产生良好的质量的对抗性示例。在这项工作中，我们发现一个良性样本，当前和下一个逆势示例可以自然地构建子空间中的三角形以获得任何迭代攻击。基于诸如SINES的规律，我们提出了一种新颖的三角形攻击（TA）来通过利用较长侧总是与任何三角形的较大角度相对的几何信息来优化扰动。然而，直接在输入图像上施加这样的信息是无效的，因为它不能彻底探索高维空间中输入样本的邻域。为了解决这个问题，TA优化低频空间中的扰动，以获得由于此类几何特性的一般性而有效减少。对ImageNet DataSet的广泛评估表明，TA在1,000个查询中实现了更高的攻击成功率，并且需要更少的查询，以在各种扰动预算下实现相同的攻击成功率，而不是现有的基于决策攻击。具有如此高的效率，我们进一步展示了TA在真实世界API上的适用性，即腾讯云API。

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.

Compressed videos often exhibit visually annoying artifacts, known as Perceivable Encoding Artifacts (PEAs), which dramatically degrade video visual quality. Subjective and objective measures capable of identifying and quantifying various types of PEAs are critical in improving visual quality. In this paper, we investigate the influence of four spatial PEAs (i.e. blurring, blocking, bleeding, and ringing) and two temporal PEAs (i.e. flickering and floating) on video quality. For spatial artifacts, we propose a visual saliency model with a low computational cost and higher consistency with human visual perception. In terms of temporal artifacts, self-attention based TimeSFormer is improved to detect temporal artifacts. Based on the six types of PEAs, a quality metric called Saliency-Aware Spatio-Temporal Artifacts Measurement (SSTAM) is proposed. Experimental results demonstrate that the proposed method outperforms state-of-the-art metrics. We believe that SSTAM will be beneficial for optimizing video coding techniques.

We propose a distributionally robust return-risk model for Markov decision processes (MDPs) under risk and reward ambiguity. The proposed model optimizes the weighted average of mean and percentile performances, and it covers the distributionally robust MDPs and the distributionally robust chance-constrained MDPs (both under reward ambiguity) as special cases. By considering that the unknown reward distribution lies in a Wasserstein ambiguity set, we derive the tractable reformulation for our model. In particular, we show that that the return-risk model can also account for risk from uncertain transition kernel when one only seeks deterministic policies, and that a distributionally robust MDP under the percentile criterion can be reformulated as its nominal counterpart at an adjusted risk level. A scalable first-order algorithm is designed to solve large-scale problems, and we demonstrate the advantages of our proposed model and algorithm through numerical experiments.

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.

As one of the most important psychic stress reactions, micro-expressions (MEs), are spontaneous and transient facial expressions that can reveal the genuine emotions of human beings. Thus, recognizing MEs (MER) automatically is becoming increasingly crucial in the field of affective computing, and provides essential technical support in lie detection, psychological analysis and other areas. However, the lack of abundant ME data seriously restricts the development of cutting-edge data-driven MER models. Despite the recent efforts of several spontaneous ME datasets to alleviate this problem, it is still a tiny amount of work. To solve the problem of ME data hunger, we construct a dynamic spontaneous ME dataset with the largest current ME data scale, called DFME (Dynamic Facial Micro-expressions), which includes 7,526 well-labeled ME videos induced by 671 participants and annotated by more than 20 annotators throughout three years. Afterwards, we adopt four classical spatiotemporal feature learning models on DFME to perform MER experiments to objectively verify the validity of DFME dataset. In addition, we explore different solutions to the class imbalance and key-frame sequence sampling problems in dynamic MER respectively on DFME, so as to provide a valuable reference for future research. The comprehensive experimental results show that our DFME dataset can facilitate the research of automatic MER, and provide a new benchmark for MER. DFME will be published via https://mea-lab-421.github.io.