随着越来越多的公司存储客户的数据;一个人的各种信息分布在众多的公司数据库中。不同的工业部门对同一客户带来了不同的特点。此外，同一工业部门的不同公司携带有关客户的类似数据，具有不同的数据表示。来自不同工业部门的公司之间的合作，称为垂直合作，以及同一部门内的公司，称为横向合作，可以导致更准确的机器学习模型以及信用评分等任务中的更好的估计。但是，不同数据表示的数据隐私法规和兼容性问题是合作模型培训的巨大障碍。通过提出培训框架MICS和实验在几种数值数据集上，我们表明，公司将对与其他公司和其他工业部门的其他公司合作，共同培训更强大，准确的全球模型，而不明确分享客户的奖励私人数据。

We present a novel image inversion framework and a training pipeline to achieve high-fidelity image inversion with high-quality attribute editing. Inverting real images into StyleGAN's latent space is an extensively studied problem, yet the trade-off between the image reconstruction fidelity and image editing quality remains an open challenge. The low-rate latent spaces are limited in their expressiveness power for high-fidelity reconstruction. On the other hand, high-rate latent spaces result in degradation in editing quality. In this work, to achieve high-fidelity inversion, we learn residual features in higher latent codes that lower latent codes were not able to encode. This enables preserving image details in reconstruction. To achieve high-quality editing, we learn how to transform the residual features for adapting to manipulations in latent codes. We train the framework to extract residual features and transform them via a novel architecture pipeline and cycle consistency losses. We run extensive experiments and compare our method with state-of-the-art inversion methods. Qualitative metrics and visual comparisons show significant improvements. Code: https://github.com/hamzapehlivan/StyleRes

Artificial Intelligence (AI) and its applications have sparked extraordinary interest in recent years. This achievement can be ascribed in part to advances in AI subfields including Machine Learning (ML), Computer Vision (CV), and Natural Language Processing (NLP). Deep learning, a sub-field of machine learning that employs artificial neural network concepts, has enabled the most rapid growth in these domains. The integration of vision and language has sparked a lot of attention as a result of this. The tasks have been created in such a way that they properly exemplify the concepts of deep learning. In this review paper, we provide a thorough and an extensive review of the state of the arts approaches, key models design principles and discuss existing datasets, methods, their problem formulation and evaluation measures for VQA and Visual reasoning tasks to understand vision and language representation learning. We also present some potential future paths in this field of research, with the hope that our study may generate new ideas and novel approaches to handle existing difficulties and develop new applications.

Large-scale online recommendation systems must facilitate the allocation of a limited number of items among competing users while learning their preferences from user feedback. As a principled way of incorporating market constraints and user incentives in the design, we consider our objectives to be two-fold: maximal social welfare with minimal instability. To maximize social welfare, our proposed framework enhances the quality of recommendations by exploring allocations that optimistically maximize the rewards. To minimize instability, a measure of users' incentives to deviate from recommended allocations, the algorithm prices the items based on a scheme derived from the Walrasian equilibria. Though it is known that these equilibria yield stable prices for markets with known user preferences, our approach accounts for the inherent uncertainty in the preferences and further ensures that the users accept their recommendations under offered prices. To the best of our knowledge, our approach is the first to integrate techniques from combinatorial bandits, optimal resource allocation, and collaborative filtering to obtain an algorithm that achieves sub-linear social welfare regret as well as sub-linear instability. Empirical studies on synthetic and real-world data also demonstrate the efficacy of our strategy compared to approaches that do not fully incorporate all these aspects.

Generic motion understanding from video involves not only tracking objects, but also perceiving how their surfaces deform and move. This information is useful to make inferences about 3D shape, physical properties and object interactions. While the problem of tracking arbitrary physical points on surfaces over longer video clips has received some attention, no dataset or benchmark for evaluation existed, until now. In this paper, we first formalize the problem, naming it tracking any point (TAP). We introduce a companion benchmark, TAP-Vid, which is composed of both real-world videos with accurate human annotations of point tracks, and synthetic videos with perfect ground-truth point tracks. Central to the construction of our benchmark is a novel semi-automatic crowdsourced pipeline which uses optical flow estimates to compensate for easier, short-term motion like camera shake, allowing annotators to focus on harder sections of video. We validate our pipeline on synthetic data and propose a simple end-to-end point tracking model TAP-Net, showing that it outperforms all prior methods on our benchmark when trained on synthetic data.

The use of needles to access sites within organs is fundamental to many interventional medical procedures both for diagnosis and treatment. Safe and accurate navigation of a needle through living tissue to an intra-tissue target is currently often challenging or infeasible due to the presence of anatomical obstacles in the tissue, high levels of uncertainty, and natural tissue motion (e.g., due to breathing). Medical robots capable of automating needle-based procedures in vivo have the potential to overcome these challenges and enable an enhanced level of patient care and safety. In this paper, we show the first medical robot that autonomously navigates a needle inside living tissue around anatomical obstacles to an intra-tissue target. Our system leverages an aiming device and a laser-patterned highly flexible steerable needle, a type of needle capable of maneuvering along curvilinear trajectories to avoid obstacles. The autonomous robot accounts for anatomical obstacles and uncertainty in living tissue/needle interaction with replanning and control and accounts for respiratory motion by defining safe insertion time windows during the breathing cycle. We apply the system to lung biopsy, which is critical in the diagnosis of lung cancer, the leading cause of cancer-related death in the United States. We demonstrate successful performance of our system in multiple in vivo porcine studies and also demonstrate that our approach leveraging autonomous needle steering outperforms a standard manual clinical technique for lung nodule access.

在过去的十年中，修剪神经网络已经流行，当时证明可以安全地从现代神经网络中安全地删除大量权重，而不会损害准确性。从那时起，已经提出了许多修剪方法，每种方法都比以前更好。如今，许多最先进的技术（SOTA）技术依赖于使用重要性得分的复杂修剪方法，通过反向传播获得反馈或在其他等方面获得基于启发式的修剪规则。我们质疑这种引入复杂性的模式，以获得更好的修剪结果。我们对这些SOTA技术基准针对全球幅度修剪（全球MP）（一个天真的修剪基线），以评估是否确实需要复杂性来实现更高的性能。全球MP按其幅度顺序排列权重，并修理最小的权重。因此，它以香草形式是最简单的修剪技术之一。令人惊讶的是，我们发现香草全球MP的表现优于所有其他SOTA技术，并取得了新的SOTA结果。它还可以在拖叉稀疏方面取得良好的性能，当以逐渐修剪的方式进行修剪时，我们发现这是增强的。我们还发现，全球MP在具有卓越性能的任务，数据集和模型之间可以推广。此外，许多修剪算法以高稀疏速率遇到的一个常见问题，即可以通过设置要保留在每层中的最小权重阈值来轻松固定在全球MP中。最后，与许多其他SOTA技术不同，全球MP不需要任何其他特定算法的超参数，并且非常简单地调整和实施。我们在各种模型（WRN-28-8，Resnet-32，Resnet-50，Mobilenet-V1和FastGrnn）和多个数据集（CIFAR-10，Imagenet和HAR-2）上展示了我们的发现。代码可在https://github.com/manasgupta-1/globalmp上找到。

通过脑电图信号的情绪分类取得了许多进步。但是，诸如缺乏数据和学习重要特征和模式之类的问题始终是具有在计算和预测准确性方面改进的领域。这项工作分析了基线机器学习分类器在DEAP数据集上的性能以及一种表格学习方法，该方法提供了最新的可比结果，从而利用了性能提升，这是由于其深度学习架构而无需部署重型神经网络。

发现广泛使用的深度学习模型的稳健性差。几乎没有噪音可以欺骗最先进的模型来做出错误的预测。尽管有很多高性能攻击生成方法，但其中大多数直接在原始数据中添加了扰动，并使用L_P规范对其进行测量；这可能会破坏数据的主要结构，从而产生无效的攻击。在本文中，我们提出了一个黑框攻击，该攻击不是修改原始数据，而是修改由自动编码器提取的数据的潜在特征；然后，我们测量语义空间中的噪音以保护数据的语义。我们在MNIST和CIFAR-10数据集上训练了自动编码器，并使用遗传算法发现了最佳的对抗扰动。我们的方法在MNIST和CIFAR-10数据集的前100个数据上获得了100％的攻击成功率，而扰动率较小。

深度学习越来越多地在医疗保健中获得迅速采用，以帮助改善患者的结果。在医学图像分析中，需要进行广泛的培训，以获得必要的专业知识，以成为值得信赖的从业者。但是，尽管深度学习技术继续提供最先进的预测性能，但阻碍医疗保健中这一进展的主要挑战之一是这些模型推理机制的不透明性质。因此，归因在建立对利益相关者的信心中对深度学习模型为临床决策做出的预测的信心至关重要。这项工作试图回答以下问题：深神网络模型在医学图像中学到什么？从这个角度来看，我们使用基于自适应路径的梯度积分技术提出了一个新颖的归因框架。结果表明，通过允许他们了解输入预测相关结构，发现新的生物标志物并揭示潜在的模型偏见来提高领域专家的信任，以改善医疗保健结果。