We study the sample complexity of reducing reinforcement learning to a sequence of empirical risk minimization problems over the policy space. Such reductions-based algorithms exhibit local convergence in the function space, as opposed to the parameter space for policy gradient algorithms, and thus are unaffected by the possibly non-linear or discontinuous parameterization of the policy class. We propose a variance-reduced variant of Conservative Policy Iteration that improves the sample complexity of producing a $\varepsilon$-functional local optimum from $O(\varepsilon^{-4})$ to $O(\varepsilon^{-3})$. Under state-coverage and policy-completeness assumptions, the algorithm enjoys $\varepsilon$-global optimality after sampling $O(\varepsilon^{-2})$ times, improving upon the previously established $O(\varepsilon^{-3})$ sample requirement.

室内运动计划的重点是解决通过混乱环境导航代理的问题。迄今为止，在该领域已经完成了很多工作，但是这些方法通常无法找到计算廉价的在线路径计划和路径最佳之间的最佳平衡。除此之外，这些作品通常证明是单一启动单目标世界的最佳性。为了应对这些挑战，我们为在未知室内环境中进行导航的多个路径路径计划者和控制器堆栈，在该环境中，路点将目标与机器人必须在达到目标之前必须穿越的中介点一起。我们的方法利用全球规划师（在任何瞬间找到下一个最佳航路点），本地规划师（计划通往特定航路点的路径）以及自适应模型预测性控制策略（用于强大的系统控制和更快的操作） 。我们在一组随机生成的障碍图，中间航路点和起始目标对上评估了算法，结果表明计算成本显着降低，具有高度准确性和可靠的控制。

使用深度学习的图像的手写词识别是一个有希望性能的活跃研究区域。IT实际情况，由于安全原因，可能需要在压缩域中处理手写图像。然而，对于压缩图像的处理仍然非常有限的深度学习的利用。通过在深度学习中的最新进展中，在压缩域中处理文档图像的需要，我们提出了一个HWRCNET模型，用于JPEG压缩域中的手写字识别。所提出的模型结合了基于卷积神经网络（CNN）和双向长短期存储器（BILSTM）的经常性神经网络（RNN）。基本上，我们使用压缩域图像训练模型，并遵守89.05％字识别精度和13.37％的字符错误率非常有吸引力的性能。

背景和目的：与生物医学分析相结合的人工智能（AI）方法在Pandemics期间具有关键作用，因为它有助于释放来自医疗保健系统和医生的压力压力。由于持续的Covid-19危机在具有茂密的人口和巴西和印度等测试套件中的国家恶化，放射性成像可以作为准确分类Covid-19患者的重要诊断工具，并在适当时期规定必要的治疗。通过这种动机，我们基于使用胸部X射线检测Covid-19感染肺的深度学习架构的研究。数据集：我们共收集了三种不同类标签的2470张图片，即健康的肺，普通肺炎和Covid-19感染的肺炎，其中470个X射线图像属于Covid-19类。方法：我们首先使用直方图均衡技术预处理所有图像，并使用U-Net架构进行它们。然后，VGG-16网络用于从预处理图像中的特征提取，该特征提取通过SMTE过采样技术进一步采样以实现平衡数据集。最后，使用具有10倍交叉验证的支持向量机（SVM）分类器分类类平衡功能，评估精度。结果和结论：我们的新方法结合了众所周知的预处理技术，特征提取方法和数据集平衡方法，使我们在2470 X射线图像的数据集中获得了Covid-19图像的优秀识别率为98％ 。因此，我们的模型适用于用于筛选目的的医疗保健设施。

机械通气是ICU中最广泛使用的疗法中最广泛的疗法之一。然而，尽管在麻醉与科迪德相关的终身支持中具有广泛的应用，但仍有许多有害挑战。我们将这些视为控制问题：呼吸机必须根据规定的气道压力轨迹进出患者的肺部。基于PID方法的行业标准控制器既不是最佳的也不是强大的。我们的数据驱动方法学习通过在从呼吸机收集的数据上培训的模拟器本身进行培训来控制侵入式呼吸机。该方法优于流行的加固学习算法，甚至比PID更精确且强大地控制物理呼吸机。这些结果强调了有效的数据驱动方法可以用于侵入性通风，并表明更通用的通风形式（例如，无侵入性，适应性）也可能是可享受的。

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.