灰度图像着色是AI在信息恢复中的引人入胜的应用。该问题的天生性质不良的性质使其更具挑战性，因为输出可能是多模式的。目前正在使用的基于学习的方法为直接情况产生可接受的结果，但在没有明确的图形分离的情况下通常无法恢复上下文信息。同样，由于在完整图像特征上训练的单个模型不足以学习各种数据模式，因此图像遭受了颜色出血和饱和背景。为了解决这些问题，我们提出了一个基于GAN的配色框架。在我们的方法中，每个量身定制的GAN管道都会使前景（使用对象级特征）或背景（使用全图像功能）着色。前景管道采用了一个具有自我注意事项的残留无UNET作为其发电机，使用了全图像功能和可可数据集中的相应对象级特征训练。背景管道依赖于该位置数据集的全图像功能和其他培训示例。我们设计了一个基于密集的融合网络，以通过基于特征的融合来获得最终的有色图像。我们显示了通常用于评估多模式问题（例如图像着色）并使用多个感知指标对我们的框架进行广泛的绩效评估的非感知评估指标的缺点。我们的方法的表现优于大多数基于学习的方法，并且产生的结果与最新的方法相当。此外，我们进行了运行时分析，并获得了每个图像的平均推理时间24ms。

在过去的几年中，几乎没有学习的领域取得了重大改进。这种学习范式已经显示出对挑战性检测的挑战性问题的令人鼓舞的结果，在这种情况下，一般任务是应对重型阶级失衡。我们的论文提出了一种新的方法来进行几次分类，我们采用了多种预训练的卷积模型的知识基础，这些卷积模型是我们提出的几杆框架的骨干。我们的框架使用一种新颖的结合技术来提高准确性，同时大大降低了总参数计数，从而为实时实现铺平了道路。我们使用电源线缺陷检测数据集执行广泛的超参数搜索，并获得5-way 5-Shot任务的精度为92.30％。在不进一步调整的情况下，我们使用现有的最先进方法评估我们的模型，并胜过它们。

由于医疗保健是关键方面，健康保险已成为最大程度地减少医疗费用的重要计划。此后，由于保险的增加，医疗保健行业的欺诈活动大幅增加，欺诈行业已成为医疗费用上升的重要贡献者，尽管可以使用欺诈检测技术来减轻其影响。为了检测欺诈，使用机器学习技术。美国联邦政府的医疗补助和医疗保险服务中心（CMS）在本研究中使用“医疗保险D部分”保险索赔来开发欺诈检测系统。在类不平衡且高维的Medicare数据集中使用机器学习算法是一项艰巨的任务。为了紧凑此类挑战，目前的工作旨在在数据采样之后执行功能提取，然后应用各种分类算法，以获得更好的性能。特征提取是一种降低降低方法，该方法将属性转换为实际属性的线性或非线性组合，生成较小，更多样化的属性集，从而降低了尺寸。数据采样通常用于通过扩大少数族裔类的频率或降低多数类的频率以获得两种类别的出现数量大约相等的频率来解决类不平衡。通过标准性能指标评估所提出的方法。因此，为了有效地检测欺诈，本研究将自动编码器作为特征提取技术，合成少数族裔过采样技术（SMOTE）作为数据采样技术，以及各种基于决策树的分类器作为分类算法。实验结果表明，自动编码器的结合，然后在LightGBM分类器上获得SMOTE，取得了最佳的结果。

深度学习（DL）模型为各种医学成像基准挑战提供了最先进的性能，包括脑肿瘤细分（BRATS）挑战。然而，局灶性病理多隔室分割（例如，肿瘤和病变子区）的任务特别具有挑战性，并且潜在的错误阻碍DL模型转化为临床工作流程。量化不确定形式的DL模型预测的可靠性，可以实现最不确定的地区的临床审查，从而建立信任并铺平临床翻译。最近，已经引入了许多不确定性估计方法，用于DL医学图像分割任务。开发指标评估和比较不确定性措施的表现将有助于最终用户制定更明智的决策。在本研究中，我们探索并评估在Brats 2019-2020任务期间开发的公制，以对不确定量化量化（Qu-Brats），并旨在评估和排列脑肿瘤多隔室分割的不确定性估计。该公制（1）奖励不确定性估计，对正确断言产生高置信度，以及在不正确的断言处分配低置信水平的估计数，（2）惩罚导致更高百分比的无关正确断言百分比的不确定性措施。我们进一步基准测试由14个独立参与的Qu-Brats 2020的分割不确定性，所有这些都参与了主要的Brats细分任务。总体而言，我们的研究结果证实了不确定性估计提供了分割算法的重要性和互补价值，因此突出了医学图像分析中不确定性量化的需求。我们的评估代码在HTTPS://github.com/ragmeh11/qu-brats公开提供。

脑肿瘤是最常见和最致命的疾病，可在所有年龄组中发现。通常，采用MRI模态来通过放射科医师鉴定和诊断肿瘤。肿瘤区域的正确鉴定及其类型可以帮助诊断随访治疗计划的肿瘤。然而，对于任何分析这种扫描的放射科学家是一种复杂且耗时的任务。基于深度学习的计算机辅助诊断系统的动机，本文提出了使用MRI图像对脑肿瘤区域进行分类和分割脑肿瘤区域的多任务注意力引导的编码器。Mag-Net培训和评估了图的图解数据集，包括冠状，轴向和矢状瘤，具有3种肿瘤脑膜瘤，胶质瘤和垂体肿瘤。通过详尽的实验试验，模型与现有最先进的模型相比，实现了有希望的结果，同时在其他最先进的模型中具有至少数量的培训参数。

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.

The machine translation mechanism translates texts automatically between different natural languages, and Neural Machine Translation (NMT) has gained attention for its rational context analysis and fluent translation accuracy. However, processing low-resource languages that lack relevant training attributes like supervised data is a current challenge for Natural Language Processing (NLP). We incorporated a technique known Active Learning with the NMT toolkit Joey NMT to reach sufficient accuracy and robust predictions of low-resource language translation. With active learning, a semi-supervised machine learning strategy, the training algorithm determines which unlabeled data would be the most beneficial for obtaining labels using selected query techniques. We implemented two model-driven acquisition functions for selecting the samples to be validated. This work uses transformer-based NMT systems; baseline model (BM), fully trained model (FTM) , active learning least confidence based model (ALLCM), and active learning margin sampling based model (ALMSM) when translating English to Hindi. The Bilingual Evaluation Understudy (BLEU) metric has been used to evaluate system results. The BLEU scores of BM, FTM, ALLCM and ALMSM systems are 16.26, 22.56 , 24.54, and 24.20, respectively. The findings in this paper demonstrate that active learning techniques helps the model to converge early and improve the overall quality of the translation system.

We study the problem of planning under model uncertainty in an online meta-reinforcement learning (RL) setting where an agent is presented with a sequence of related tasks with limited interactions per task. The agent can use its experience in each task and across tasks to estimate both the transition model and the distribution over tasks. We propose an algorithm to meta-learn the underlying structure across tasks, utilize it to plan in each task, and upper-bound the regret of the planning loss. Our bound suggests that the average regret over tasks decreases as the number of tasks increases and as the tasks are more similar. In the classical single-task setting, it is known that the planning horizon should depend on the estimated model's accuracy, that is, on the number of samples within task. We generalize this finding to meta-RL and study this dependence of planning horizons on the number of tasks. Based on our theoretical findings, we derive heuristics for selecting slowly increasing discount factors, and we validate its significance empirically.

As language models have grown in parameters and layers, it has become much harder to train and infer with them on single GPUs. This is severely restricting the availability of large language models such as GPT-3, BERT-Large, and many others. A common technique to solve this problem is pruning the network architecture by removing transformer heads, fully-connected weights, and other modules. The main challenge is to discern the important parameters from the less important ones. Our goal is to find strong metrics for identifying such parameters. We thus propose two strategies: Cam-Cut based on the GradCAM interpretations, and Smooth-Cut based on the SmoothGrad, for calculating the importance scores. Through this work, we show that our scoring functions are able to assign more relevant task-based scores to the network parameters, and thus both our pruning approaches significantly outperform the standard weight and gradient-based strategies, especially at higher compression ratios in BERT-based models. We also analyze our pruning masks and find them to be significantly different from the ones obtained using standard metrics.