Learning classifiers using skewed or imbalanced datasets can occasionally lead to classification issues; this is a serious issue. In some cases, one class contains the majority of examples while the other, which is frequently the more important class, is nevertheless represented by a smaller proportion of examples. Using this kind of data could make many carefully designed machine-learning systems ineffective. High training fidelity was a term used to describe biases vs. all other instances of the class. The best approach to all possible remedies to this issue is typically to gain from the minority class. The article examines the most widely used methods for addressing the problem of learning with a class imbalance, including data-level, algorithm-level, hybrid, cost-sensitive learning, and deep learning, etc. including their advantages and limitations. The efficiency and performance of the classifier are assessed using a myriad of evaluation metrics.

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

将用户搜索查询与广告商实时竞标相关的关键字匹配是赞助搜索中的一个至关重要问题。在文献中，已经探索了两种广泛的方法来解决此问题：（i）在共享空间中学习查询和出价关键字的密集检索（DR），以及（ii）自然语言生成（NLG） - 学会直接生成给定查询的投标关键字。在这项工作中，我们首先对这两种方法进行了实证研究，并表明它们提供了添加剂的补充优势。特别是，从NLG检索到的很大一部分的关键字尚未由DR和反之亦然。然后，我们证明有可能将这两种方法的优势有效地结合在一个模型中。具体而言，我们提出了心脏：一种新型的多任务融合框架，在该框架中，我们共同优化共享编码器以同时执行DR和非自动性NLG。通过对30多个跨越20多种语言的搜索查询进行的广泛实验，我们表明，与使用相同的GPU计算的基线方法相比，心脏检索高质量的出价关键字40.3％。我们还证明，在单个心脏模型上推断与在两种不同的DR和NLG基线模型上推断为2倍计算一样好。此外，我们表明，接受心脏目标训练的DR模型要比接受标准对比度损失功能的训练的模型要好得多。最后，我们表明我们的心目标可以用于除赞助搜索并实现显着绩效提高以外的短文本检索任务。

本文解决了解释黑框回归模型异常预测的任务。当使用黑框模型（例如从许多传感器测量值中预测能源消耗的一个模型）时，我们通常会有某些观察到的样品可能会显着偏离其预测的情况。这可能是由于亚最佳黑盒模型，或仅仅​​是因为这些样品是异常值。无论哪种情况，理想情况下都希望计算``责任分数''，以指示输入变量负责异常输出的程度。在这项工作中，我们将此任务形式化为一个统计逆问题：给定模型偏离预期值，推断每个输入变量的责任分数。我们提出了一种称为似然补偿（LC）的新方法，该方法基于可能性原理，并计算对每个输入变量的校正。据我们所知，这是第一个计算实际有价值异常模型偏差的责任分数的原则性框架。我们将方法应用于现实世界中的建筑能源预测任务，并根据专家反馈确认其实用性。

准确且具有成本效益的水体映射对环境的理解和导航具有巨大的意义。但是，我们从此类环境特征中获得的信息数量和质量受到各种因素的限制，包括成本，时间，安全性以及现有数据收集技术的功能。水深度的测量是此类映射的重要组成部分，尤其是在可以提供导航风险或具有重要生态功能的浅层地点。例如，由于暴风雨和侵蚀，这些位置的侵蚀和沉积会导致需要重复测量的快速变化。在本文中，我们描述了使用侧扫声纳的低成本，弹性，无人自主的表面车辆用于测深的数据收集。我们讨论了用于收集导航，控制和测深数据的设备和传感器的适应，还概述了车辆设置。这款自动表面车辆已用于从印度孟买的Powai湖收集测深。

极端分类（XC）试图用最大的标签集中标记标签的子集标记数据点。通过使用稀疏，手工制作的功能的XC方法优越，用密集，学习的数据来进行深度XC，以数据点和标签的形式吸引了很多关注。负挖掘技术已成为所有深XC方法的关键组成部分，使它们可以扩展到数百万个标签。然而，尽管最近进步，但培训具有大型编码器体系结构（例如变形金刚）的深入XC模型仍然具有挑战性。本文确定，流行负面挖掘技术的内存通常迫使小型批量尺寸保持小且缓慢的训练。作为回应，本文介绍了Ngame，这是一种轻巧的迷你批次创建技术，可证明可证明准确的内部负面样品。这使得与现有负面采样技术相比，具有更大的迷你批次培训，提供更快的收敛性和更高的精度。发现Ngame的准确性比各种基准数据集的最先进方法要高16％，以进行极端分类，并且在回答搜索引擎查询以响应用户网页时检索搜索引擎查询更准确3％显示个性化广告。在流行搜索引擎的实时A/B测试中，Ngame在点击率率中的收益最高可达23％。

发现表面电阻率的传统调查方法是耗时的和劳动量的。很少有研究重点是使用遥感数据和深度学习技术找到电阻率/电导率。在这一工作中，我们通过应用各种深度学习方法评估了表面电阻率和合成孔径雷达（SAR）之间的相关性，并在美国Coso地热区域中测试了我们的假设。为了检测电阻率，使用了UAVSAR获得的L波段全偏光SAR数据，并将MT（MagnEtoteltolarics）反向电阻率数据用作地面真相。我们进行了实验，以比较各种深度学习体系结构，并建议使用双输入UNET（DI-UNET）体系结构。 Di-Unet使用深度学习架构使用完整的极化SAR数据来预测电阻率，并承诺对传统方法进行快速调查。我们提出的方法实现了从SAR数据中映射MT电阻率的结果。

随着社交软件和多媒体技术的持续发展，图像已成为传播信息和社交的重要载体。如何全面评估图像已成为最近研究的重点。传统的图像美学评估方法通常采用单个数值总体评估评分，该评估具有一定的主观性，无法再满足更高的美学要求。在本文中，我们构建了一个称为Aesthetic混合数据集的新图像属性数据集，该数据集具有属性（AMD-A）和设计融合的外部属性功能。此外，我们还提出了一种有效的方法，用于在混合多属性数据集上进行图像美学属性评估，并通过使用ExtisticNet-B0作为骨干网络来构建多任务网络体系结构。我们的模型可以实现美学分类，整体评分和属性评分。在每个子网络中，我们通过ECA通道注意模块改进特征提取。至于最终的整体评分，我们采用了教师学习网络的想法，并使用分类子网络来指导美学的整体细粒回归。实验结果，使用思维螺旋式的结果表明，我们提出的方法可以有效地改善美学整体和属性评估的性能。

We introduce Argoverse 2 (AV2) - a collection of three datasets for perception and forecasting research in the self-driving domain. The annotated Sensor Dataset contains 1,000 sequences of multimodal data, encompassing high-resolution imagery from seven ring cameras, and two stereo cameras in addition to lidar point clouds, and 6-DOF map-aligned pose. Sequences contain 3D cuboid annotations for 26 object categories, all of which are sufficiently-sampled to support training and evaluation of 3D perception models. The Lidar Dataset contains 20,000 sequences of unlabeled lidar point clouds and map-aligned pose. This dataset is the largest ever collection of lidar sensor data and supports self-supervised learning and the emerging task of point cloud forecasting. Finally, the Motion Forecasting Dataset contains 250,000 scenarios mined for interesting and challenging interactions between the autonomous vehicle and other actors in each local scene. Models are tasked with the prediction of future motion for "scored actors" in each scenario and are provided with track histories that capture object location, heading, velocity, and category. In all three datasets, each scenario contains its own HD Map with 3D lane and crosswalk geometry - sourced from data captured in six distinct cities. We believe these datasets will support new and existing machine learning research problems in ways that existing datasets do not. All datasets are released under the CC BY-NC-SA 4.0 license.

Object movement identification is one of the most researched problems in the field of computer vision. In this task, we try to classify a pixel as foreground or background. Even though numerous traditional machine learning and deep learning methods already exist for this problem, the two major issues with most of them are the need for large amounts of ground truth data and their inferior performance on unseen videos. Since every pixel of every frame has to be labeled, acquiring large amounts of data for these techniques gets rather expensive. Recently, Zhao et al. [1] proposed one of a kind Arithmetic Distribution Neural Network (ADNN) for universal background subtraction which utilizes probability information from the histogram of temporal pixels and achieves promising results. Building onto this work, we developed an intelligent video surveillance system that uses ADNN architecture for motion detection, trims the video with parts only containing motion, and performs anomaly detection on the trimmed video.