Large Neighborhood Search (LNS) is a popular heuristic algorithm for solving combinatorial optimization problems (COP). It starts with an initial solution to the problem and iteratively improves it by searching a large neighborhood around the current best solution. LNS relies on heuristics to select neighborhoods to search in. In this paper, we focus on designing effective and efficient heuristics in LNS for integer linear programs (ILP) since a wide range of COPs can be represented as ILPs. Local Branching (LB) is a heuristic that selects the neighborhood that leads to the largest improvement over the current solution in each iteration of LNS. LB is often slow since it needs to solve an ILP of the same size as input. Our proposed heuristics, LB-RELAX and its variants, use the linear programming relaxation of LB to select neighborhoods. Empirically, LB-RELAX and its variants compute as effective neighborhoods as LB but run faster. They achieve state-of-the-art anytime performance on several ILP benchmarks.

随着机器学习系统的计算要求以及机器学习框架的规模和复杂性的增加，基本框架创新变得具有挑战性。尽管计算需求驱动了最近的编译器，网络和硬件的进步，但通过机器学习工具对这些进步的利用却以较慢的速度发生。这部分是由于与现有框架原型制作新的计算范式有关的困难。大型框架将机器学习研究人员和从业人员作为最终用户的优先级优先，并且很少关注能够向前推动框架的系统研究人员 - 我们认为两者都是同等重要的利益相关者。我们介绍了手电筒，这是一个开源库，旨在通过优先考虑开放式，模块化，可定制的内部设备以及最新的，可用于研究的模型和培训设置，以刺激机器学习工具和系统的创新。手电筒使系统研究人员能够快速原型并尝试机器学习计算中的新思想，并且开销低，与其他流行的机器学习框架竞争并经常超过其他流行的机器学习框架。我们将手电筒视为一种工具，可以使可以使广泛使用的图书馆受益，并使机器学习和系统研究人员更加紧密地结合在一起。手电筒可从https://github.com/flashlight/flashlight获得。

Deep learning frameworks have often focused on either usability or speed, but not both. PyTorch is a machine learning library that shows that these two goals are in fact compatible: it provides an imperative and Pythonic programming style that supports code as a model, makes debugging easy and is consistent with other popular scientific computing libraries, while remaining efficient and supporting hardware accelerators such as GPUs. In this paper, we detail the principles that drove the implementation of PyTorch and how they are reflected in its architecture. We emphasize that every aspect of PyTorch is a regular Python program under the full control of its user. We also explain how the careful and pragmatic implementation of the key components of its runtime enables them to work together to achieve compelling performance. We demonstrate the efficiency of individual subsystems, as well as the overall speed of PyTorch on several common benchmarks.

This work focuses on unsupervised representation learning in person re-identification (ReID). Recent self-supervised contrastive learning methods learn invariance by maximizing the representation similarity between two augmented views of a same image. However, traditional data augmentation may bring to the fore undesirable distortions on identity features, which is not always favorable in id-sensitive ReID tasks. In this paper, we propose to replace traditional data augmentation with a generative adversarial network (GAN) that is targeted to generate augmented views for contrastive learning. A 3D mesh guided person image generator is proposed to disentangle a person image into id-related and id-unrelated features. Deviating from previous GAN-based ReID methods that only work in id-unrelated space (pose and camera style), we conduct GAN-based augmentation on both id-unrelated and id-related features. We further propose specific contrastive losses to help our network learn invariance from id-unrelated and id-related augmentations. By jointly training the generative and the contrastive modules, our method achieves new state-of-the-art unsupervised person ReID performance on mainstream large-scale benchmarks.

Objective: Accurate visual classification of bladder tissue during Trans-Urethral Resection of Bladder Tumor (TURBT) procedures is essential to improve early cancer diagnosis and treatment. During TURBT interventions, White Light Imaging (WLI) and Narrow Band Imaging (NBI) techniques are used for lesion detection. Each imaging technique provides diverse visual information that allows clinicians to identify and classify cancerous lesions. Computer vision methods that use both imaging techniques could improve endoscopic diagnosis. We address the challenge of tissue classification when annotations are available only in one domain, in our case WLI, and the endoscopic images correspond to an unpaired dataset, i.e. there is no exact equivalent for every image in both NBI and WLI domains. Method: We propose a semi-surprised Generative Adversarial Network (GAN)-based method composed of three main components: a teacher network trained on the labeled WLI data; a cycle-consistency GAN to perform unpaired image-to-image translation, and a multi-input student network. To ensure the quality of the synthetic images generated by the proposed GAN we perform a detailed quantitative, and qualitative analysis with the help of specialists. Conclusion: The overall average classification accuracy, precision, and recall obtained with the proposed method for tissue classification are 0.90, 0.88, and 0.89 respectively, while the same metrics obtained in the unlabeled domain (NBI) are 0.92, 0.64, and 0.94 respectively. The quality of the generated images is reliable enough to deceive specialists. Significance: This study shows the potential of using semi-supervised GAN-based classification to improve bladder tissue classification when annotations are limited in multi-domain data.

Many datasets are biased, namely they contain easy-to-learn features that are highly correlated with the target class only in the dataset but not in the true underlying distribution of the data. For this reason, learning unbiased models from biased data has become a very relevant research topic in the last years. In this work, we tackle the problem of learning representations that are robust to biases. We first present a margin-based theoretical framework that allows us to clarify why recent contrastive losses (InfoNCE, SupCon, etc.) can fail when dealing with biased data. Based on that, we derive a novel formulation of the supervised contrastive loss (epsilon-SupInfoNCE), providing more accurate control of the minimal distance between positive and negative samples. Furthermore, thanks to our theoretical framework, we also propose FairKL, a new debiasing regularization loss, that works well even with extremely biased data. We validate the proposed losses on standard vision datasets including CIFAR10, CIFAR100, and ImageNet, and we assess the debiasing capability of FairKL with epsilon-SupInfoNCE, reaching state-of-the-art performance on a number of biased datasets, including real instances of biases in the wild.

在许多情况下，更简单的模型比更复杂的模型更可取，并且该模型复杂性的控制是机器学习中许多方法的目标，例如正则化，高参数调整和体系结构设计。在深度学习中，很难理解复杂性控制的潜在机制，因为许多传统措施并不适合深度神经网络。在这里，我们开发了几何复杂性的概念，该概念是使用离散的dirichlet能量计算的模型函数变异性的量度。使用理论论据和经验结果的结合，我们表明，许多常见的训练启发式方法，例如参数规范正规化，光谱规范正则化，平稳性正则化，隐式梯度正则化，噪声正则化和参数初始化的选择，都可以控制几何学复杂性，并提供一个统一的框架，以表征深度学习模型的行为。

自动分割前庭造型瘤（VS）和来自磁共振成像（MRI）的耳蜗可以促进与治疗计划。无监督的分割方法已显示出令人鼓舞的结果，而无需耗时且费力的手动标记过程。在本文中，我们提出了一种在无监督域的适应设置中进行VS和耳蜗分割的方法。具体而言，我们首先开发了跨站点的跨模式未配对的图像翻译策略，以丰富合成数据的多样性。然后，我们设计了一种基于规则的离线增强技术，以进一步最大程度地减少域间隙。最后，我们采用一个自我训练的自我配置分割框架，以获得最终结果。在Crossmoda 2022验证排行榜上，我们的方法已获得竞争性与耳蜗细分性能，平均骰子得分为0.8178 $ \ pm $ 0.0803和0.8433 $ \ pm $ 0.0293。

通过将从地面视图摄像头拍摄到从卫星或飞机上拍摄的架空图像的图像，通过将代理定位在搜索区域内，将代理定位在搜索区域内，将代理定位在搜索区域中。尽管地面图像和架空图像之间的观点差异使得跨视图地理定位具有挑战性，但假设地面代理可以使用全景相机，则取得了重大进展。例如，我们先前的工作（WAG）引入了搜索区域离散化，训练损失和粒子过滤器加权的变化，从而实现了城市规模的全景跨视图地理定位。但是，由于其复杂性和成本，全景相机并未在现有机器人平台中广泛使用。非Panoramic跨视图地理定位更适用于机器人技术，但也更具挑战性。本文介绍了受限的FOV广泛地理定位（Rewag），这是一种跨视图地理定位方法，通过创建姿势吸引的嵌入并提供将粒子姿势纳入暹罗网络，将其概括为与标准的非填充地面摄像机一起使用，以供与标准的非卧型地面摄像机一起使用。 Rewag是一种神经网络和粒子滤波器系统，能够在GPS下的环境中全球定位移动代理，仅具有探测仪和90度FOV摄像机，其本地化精度与使用全景相机实现并提高本地化精度相似的定位精度与基线视觉变压器（VIT）方法相比，100倍。一个视频亮点，该视频亮点在https://youtu.be/u_obqrt8qce上展示了几十公里的测试路径上的收敛。

在过去的十年中，我们看到了工业数据，计算能力的巨大改善以及机器学习的重大理论进步。这为在大规模非线性监控和控制问题上使用现代机器学习工具提供了机会。本文对过程行业的应用进行了对最新结果的调查。