Implicit regularization is an important way to interpret neural networks. Recent theory starts to explain implicit regularization with the model of deep matrix factorization (DMF) and analyze the trajectory of discrete gradient dynamics in the optimization process. These discrete gradient dynamics are relatively small but not infinitesimal, thus fitting well with the practical implementation of neural networks. Currently, discrete gradient dynamics analysis has been successfully applied to shallow networks but encounters the difficulty of complex computation for deep networks. In this work, we introduce another discrete gradient dynamics approach to explain implicit regularization, i.e. landscape analysis. It mainly focuses on gradient regions, such as saddle points and local minima. We theoretically establish the connection between saddle point escaping (SPE) stages and the matrix rank in DMF. We prove that, for a rank-R matrix reconstruction, DMF will converge to a second-order critical point after R stages of SPE. This conclusion is further experimentally verified on a low-rank matrix reconstruction problem. This work provides a new theory to analyze implicit regularization in deep learning.

具有大尺度图像文本对的视觉预训练（VLP）在各个领域都表现出卓越的性能。但是，Internet上的图像文本对共存通常缺乏明确的对齐信息，这对于VLP来说是次优的。建议采用现成的对象检测器来利用其他图像标签信息。但是，对象检测器是耗时的，只能识别预定义的对象类别，从而限制了模型容量。受到观察的启发，即文本包含不完整的细粒图像信息，我们介绍了Ideas，该想法代表通过在线多标签识别VLP来增加文本多样性。想法表明，可以在VLP期间共同优化从文本中提取的图像标签的多标签学习。此外，想法可以在线识别有价值的图像标签，以提供更明确的文本监督。全面的实验表明，想法可以显着提高多个下游数据集上的性能，并具有较小的额外计算成本。

机器学习模型容易受到分数过失（OOD）的示例，这种问题引起了很多关注。但是，当前的方法缺乏对不同类型的OOD数据的完全理解：有一些良性的OOD数据可以适当地适应以增强学习性能，而其他MALIGN OOD数据将严重地退化分类结果。为了利用数据，本文提出了引擎盖方法，该方法可以利用每个图像实例中的内容和样式来识别良性和恶意数据。特别是，我们通过构建结构性因果模型来设计一个变异推理框架，以使因果关系和样式特征。随后，我们通过干预过程分别提高内容和样式，分别产生恶性和良性OOD数据。良性的OOD数据包含新型样式，但持有我们感兴趣的内容，并且可以利用它们来帮助培训风格不变的模型。相比之下，MALIGN OOD数据继承了未知内容，但通过检测它们可以提高模型的稳健性，以抗欺骗异常。得益于拟议的新型解开和数据增强技术，Hood可以有效地处理未知和开放环境中的OOD示例，在三个典型的OOD应用程序中，其有效性在包括OOD检测，开放设定的半监督学习和开放设定的三个典型OOD应用程序中得到了验证域适应。

在本文中，我们介绍了VCSL（视频复制段本地化），这是一种新的综合段级注释的视频复制数据集。与受视频级注释或小规模限制的现有复制检测数据集相比，VCSL不仅具有两个段级标签的数据级，其中有160k现实的视频副本对，其中包含超过280k的本地化copied seggment对，而且还包含超过280k涵盖各种视频类别和各种视频持续时间。每个收集的视频对中的所有复制段均经过手动提取，并伴随着精确注释的启动和结束时间戳。除了数据集外，我们还提出了一种新颖的评估协议，该协议可以更好地衡量视频对之间复制重叠段的预测准确性，并在不同情况下显示出改善的适应性。通过使用拟议的数据集和评估指标对几个基线和最先进的细分级视频副本检测方法进行基准测试，我们提供了一项全面的分析，可以揭示当前方法的优势和劣势作品。 VCSL数据集，公制和基准代码均在https://github.com/alipay/vcsl上公开获得。

我们讨论VMware如何解决以下挑战来利用数据，以便操作基于ML的异常检测系统，以检测我们的软件定义数据中心（SDDC）企业部署中的性能问题：（i）由于较重依赖，标签稀缺和标签偏差不可提供的人类注释器，和（ii）数据漂移，由于不断变化的工作量模式，软件堆栈和基础硬件。我们的异常检测系统已在生产中部署多年，并已成功检测到许多主要的性能问题。我们证明通过解决这些数据挑战，我们不仅提高了我们的性能异常检测模型的准确性30％，而且还可以确保模型性能永远不会降低时间。

磁共振光谱（MRS）是揭示代谢信息的无创工具。 1H-MRS的一个挑战是低信号噪声比（SNR）。为了改善SNR，一种典型的方法是用M重复样品进行信号平均（SA）。但是，数据采集时间相应地增加了M次，并且在公共环境M = 128时，完整的临床MRS SCAN大约需要10分钟。最近，引入了深度学习以改善SNR，但大多数人将模拟数据用作培训集。这可能会阻碍MRS应用程序，因为某些潜在差异（例如获取系统的缺陷）以及模拟和体内数据之间可能存在生理和心理条件。在这里，我们提出了一种新方案，该方案纯粹使用了现实数据的重复样本。深度学习模型，拒绝长期记忆（RELSTM），旨在学习从低SNR时间域数据（24 SA）到高SNR ONE（128 SA）的映射。对7个健康受试者，2名脑肿瘤患者和1名脑梗塞患者的体内脑光谱进行实验表明，仅使用20％的重复样品，RelstM的DeNoed Spectra可以为128 SA提供可比的代谢物。与最先进的低级别去核法相比，RELSTM在量化某些重要的生物标志物时达到了较低的相对误差和cram \'er-rao下限。总而言之，RELSTM可以在快速获取（24 SA）下对光谱进行高保真降级，这对MRS临床研究很有价值。

We present a simple yet effective end-to-end Video-language Pre-training (VidLP) framework, Masked Contrastive Video-language Pretraining (MAC), for video-text retrieval tasks. Our MAC aims to reduce video representation's spatial and temporal redundancy in the VidLP model by a mask sampling mechanism to improve pre-training efficiency. Comparing conventional temporal sparse sampling, we propose to randomly mask a high ratio of spatial regions and only feed visible regions into the encoder as sparse spatial sampling. Similarly, we adopt the mask sampling technique for text inputs for consistency. Instead of blindly applying the mask-then-prediction paradigm from MAE, we propose a masked-then-alignment paradigm for efficient video-text alignment. The motivation is that video-text retrieval tasks rely on high-level alignment rather than low-level reconstruction, and multimodal alignment with masked modeling encourages the model to learn a robust and general multimodal representation from incomplete and unstable inputs. Coupling these designs enables efficient end-to-end pre-training: reduce FLOPs (60% off), accelerate pre-training (by 3x), and improve performance. Our MAC achieves state-of-the-art results on various video-text retrieval datasets, including MSR-VTT, DiDeMo, and ActivityNet. Our approach is omnivorous to input modalities. With minimal modifications, we achieve competitive results on image-text retrieval tasks.

In recent years, semi-supervised graph learning with data augmentation (DA) is currently the most commonly used and best-performing method to enhance model robustness in sparse scenarios with few labeled samples. Differing from homogeneous graph, DA in heterogeneous graph has greater challenges: heterogeneity of information requires DA strategies to effectively handle heterogeneous relations, which considers the information contribution of different types of neighbors and edges to the target nodes. Furthermore, over-squashing of information is caused by the negative curvature that formed by the non-uniformity distribution and strong clustering in complex graph. To address these challenges, this paper presents a novel method named Semi-Supervised Heterogeneous Graph Learning with Multi-level Data Augmentation (HG-MDA). For the problem of heterogeneity of information in DA, node and topology augmentation strategies are proposed for the characteristics of heterogeneous graph. And meta-relation-based attention is applied as one of the indexes for selecting augmented nodes and edges. For the problem of over-squashing of information, triangle based edge adding and removing are designed to alleviate the negative curvature and bring the gain of topology. Finally, the loss function consists of the cross-entropy loss for labeled data and the consistency regularization for unlabeled data. In order to effectively fuse the prediction results of various DA strategies, the sharpening is used. Existing experiments on public datasets, i.e., ACM, DBLP, OGB, and industry dataset MB show that HG-MDA outperforms current SOTA models. Additionly, HG-MDA is applied to user identification in internet finance scenarios, helping the business to add 30% key users, and increase loans and balances by 3.6%, 11.1%, and 9.8%.

This paper presents our solution for the 2nd COVID-19 Severity Detection Competition. This task aims to distinguish the Mild, Moderate, Severe, and Critical grades in COVID-19 chest CT images. In our approach, we devise a novel infection-aware 3D Contrastive Mixup Classification network for severity grading. Specifcally, we train two segmentation networks to first extract the lung region and then the inner lesion region. The lesion segmentation mask serves as complementary information for the original CT slices. To relieve the issue of imbalanced data distribution, we further improve the advanced Contrastive Mixup Classification network by weighted cross-entropy loss. On the COVID-19 severity detection leaderboard, our approach won the first place with a Macro F1 Score of 51.76%. It significantly outperforms the baseline method by over 11.46%.

深度神经网络（DNNS）的边缘训练是持续学习的理想目标。但是，这受到训练所需的巨大计算能力的阻碍。硬件近似乘数表明，它们在获得DNN推理加速器中获得资源效率的有效性；但是，使用近似乘数的培训在很大程度上尚未开发。为了通过支持DNN培训的近似乘数来构建有效的资源加速器，需要对不同DNN体系结构和不同近似乘数进行彻底评估。本文介绍了近似值，这是一个开源框架，允许使用模拟近似乘数快速评估DNN训练和推理。近似值与TensorFlow（TF）一样用户友好，仅需要对DNN体系结构的高级描述以及近似乘数的C/C ++功能模型。我们通过使用GPU（AMSIM）上的基于基于LUT的近似浮点（FP）乘数模拟器来提高乘数在乘数级别的模拟速度。近似值利用CUDA并有效地将AMSIM集成到张量库中，以克服商业GPU中的本机硬件近似乘数的缺乏。我们使用近似值来评估使用LENET和RESNETS体系结构的小型和大型数据集（包括Imagenet）的近似乘数的DNN训练的收敛性和准确性。与FP32和BFLOAT16乘数相比，评估表明测试准确性相似的收敛行为和可忽略不计的变化。与训练和推理中基于CPU的近似乘数模拟相比，GPU加速近似值快2500倍以上。基于具有本地硬件乘数的高度优化的闭合源Cudnn/Cublas库，原始张量量仅比近似值快8倍。