模型不合时宜的元学习（MAML）目前是少量元学习的主要方法之一。尽管它具有有效性，但由于先天的二聚体问题结构，MAML的优化可能具有挑战性。具体而言，MAML的损失格局比其经验风险最小化的对应物更为复杂，可能的鞍点和局部最小化可能更复杂。为了应对这一挑战，我们利用了最近发明的清晰度最小化的最小化，并开发出一种清晰感的MAML方法，我们称其为Sharp MAML。我们从经验上证明，Sharp-MAML及其计算有效的变体可以胜过流行的现有MAML基准（例如，Mini-Imagenet上的$+12 \％$ $精度）。我们通过收敛速率分析和尖锐MAML的概括结合进行了经验研究。据我们所知，这是在双层学习背景下对清晰度感知最小化的第一个经验和理论研究。该代码可在https://github.com/mominabbass/sharp-maml上找到。

Although recent deep learning-based calibration methods can predict extrinsic and intrinsic camera parameters from a single image, their generalization remains limited by the number and distribution of training data samples. The huge computational and space requirement prevents convolutional neural networks (CNNs) from being implemented in resource-constrained environments. This challenge motivated us to learn a CNN gradually, by training new data while maintaining performance on previously learned data. Our approach builds upon a CNN architecture to automatically estimate camera parameters (focal length, pitch, and roll) using different incremental learning strategies to preserve knowledge when updating the network for new data distributions. Precisely, we adapt four common incremental learning, namely: LwF , iCaRL, LU CIR, and BiC by modifying their loss functions to our regression problem. We evaluate on two datasets containing 299008 indoor and outdoor images. Experiment results were significant and indicated which method was better for the camera calibration estimation.

Split Learning (SL) and Federated Learning (FL) are two prominent distributed collaborative learning techniques that maintain data privacy by allowing clients to never share their private data with other clients and servers, and fined extensive IoT applications in smart healthcare, smart cities, and smart industry. Prior work has extensively explored the security vulnerabilities of FL in the form of poisoning attacks. To mitigate the effect of these attacks, several defenses have also been proposed. Recently, a hybrid of both learning techniques has emerged (commonly known as SplitFed) that capitalizes on their advantages (fast training) and eliminates their intrinsic disadvantages (centralized model updates). In this paper, we perform the first ever empirical analysis of SplitFed's robustness to strong model poisoning attacks. We observe that the model updates in SplitFed have significantly smaller dimensionality as compared to FL that is known to have the curse of dimensionality. We show that large models that have higher dimensionality are more susceptible to privacy and security attacks, whereas the clients in SplitFed do not have the complete model and have lower dimensionality, making them more robust to existing model poisoning attacks. Our results show that the accuracy reduction due to the model poisoning attack is 5x lower for SplitFed compared to FL.

Spatial perception is a key task in several robotics applications. In general, it involves the nonlinear estimation of hidden variables that represent the state of the robot/environment. However, in the presence of outliers the standard nonlinear least squared formulation results in poor estimates. Several methods have been considered in the literature to improve the reliability of the estimation process. Most methods are based on heuristics since guaranteed global robust estimation is not generally practical due to high computational costs. Recently general purpose robust estimation heuristics have been proposed that leverage existing non-minimal solvers available for the outlier-free formulations without the need for an initial guess. In this work, we propose two similar heuristics backed by Bayesian theory. We evaluate these heuristics in practical scenarios to demonstrate their merits in different applications including 3D point cloud registration, mesh registration and pose graph optimization.

The ability to effectively reuse prior knowledge is a key requirement when building general and flexible Reinforcement Learning (RL) agents. Skill reuse is one of the most common approaches, but current methods have considerable limitations.For example, fine-tuning an existing policy frequently fails, as the policy can degrade rapidly early in training. In a similar vein, distillation of expert behavior can lead to poor results when given sub-optimal experts. We compare several common approaches for skill transfer on multiple domains including changes in task and system dynamics. We identify how existing methods can fail and introduce an alternative approach to mitigate these problems. Our approach learns to sequence existing temporally-extended skills for exploration but learns the final policy directly from the raw experience. This conceptual split enables rapid adaptation and thus efficient data collection but without constraining the final solution.It significantly outperforms many classical methods across a suite of evaluation tasks and we use a broad set of ablations to highlight the importance of differentc omponents of our method.

Disentanglement of constituent factors of a sensory signal is central to perception and cognition and hence is a critical task for future artificial intelligence systems. In this paper, we present a compute engine capable of efficiently factorizing holographic perceptual representations by exploiting the computation-in-superposition capability of brain-inspired hyperdimensional computing and the intrinsic stochasticity associated with analog in-memory computing based on nanoscale memristive devices. Such an iterative in-memory factorizer is shown to solve at least five orders of magnitude larger problems that cannot be solved otherwise, while also significantly lowering the computational time and space complexity. We present a large-scale experimental demonstration of the factorizer by employing two in-memory compute chips based on phase-change memristive devices. The dominant matrix-vector multiply operations are executed at O(1) thus reducing the computational time complexity to merely the number of iterations. Moreover, we experimentally demonstrate the ability to factorize visual perceptual representations reliably and efficiently.

Low-rank and sparse decomposition based methods find their use in many applications involving background modeling such as clutter suppression and object tracking. While Robust Principal Component Analysis (RPCA) has achieved great success in performing this task, it can take hundreds of iterations to converge and its performance decreases in the presence of different phenomena such as occlusion, jitter and fast motion. The recently proposed deep unfolded networks, on the other hand, have demonstrated better accuracy and improved convergence over both their iterative equivalents as well as over other neural network architectures. In this work, we propose a novel deep unfolded spatiotemporal RPCA (DUST-RPCA) network, which explicitly takes advantage of the spatial and temporal continuity in the low-rank component. Our experimental results on the moving MNIST dataset indicate that DUST-RPCA gives better accuracy when compared with the existing state of the art deep unfolded RPCA networks.

在社交媒体中发现进攻性语言是社交媒体面临的主要挑战之一。研究人员提出了许多高级方法来完成这项任务。在本报告中，我们尝试利用他们的方法中的学习，并结合我们的想法以改进它们。我们在对进攻推文分类中成功实现了74％的准确性。我们还列出了社交媒体界的滥用内容检测中的即将到来的挑战。

目标：探索深度学习算法进一步简化和优化尿道板（UP）质量评估的能力，使用板客观评分工具（POST），旨在提高Hypospadias修复中提高评估的客观性和可重复性。方法：五个关键的邮政地标是由专家在691图像数据集中的专家标记，该数据集接受了原发性杂质修复的青春期前男孩。然后，该数据集用于开发和验证基于深度学习的地标检测模型。提出的框架始于瞥见和检测，其中输入图像是使用预测的边界框裁剪的。接下来，使用深层卷积神经网络（CNN）体系结构来预测五个邮政标记的坐标。然后，这些预测的地标用于评估远端催化性远端的质量。结果：所提出的模型准确地定位了gan区域，平均平均精度（地图）为99.5％，总体灵敏度为99.1％。在预测地标的坐标时，达到了0.07152的归一化平均误差（NME），平均平方误差（MSE）为0.001，在0.1 nme的阈值下为20.2％的故障率。结论：此深度学习应用程序在使用邮政评估质量时表现出鲁棒性和高精度。使用国际多中心基于图像的数据库进行进一步评估。外部验证可以使深度学习算法受益，并导致更好的评估，决策和对手术结果的预测。

这项研究开发了一个无人驾驶系统（UASS）的框架，以监测高层建筑项目中未受保护的边缘和开口附近的跌落危险系统。开发并测试了一个三步基于机器学习的框架，以检测UAS捕获的图像的护栏柱。首先，对护栏探测器进行了培训，以定位支撑护栏的职位的候选位置。由于从实际的工作现场收集的此过程中使用了图像，因此确定了几个错误检测。因此，在以下步骤中引入了其他约束，以滤除错误检测。其次，研究团队将水平线检测器应用于图像，以正确检测地板并删除离地板不近的检测。最后，由于每个帖子之间安装了护栏柱，它们之间的分布差异大致，因此它们之间的空间被估算并用于找到两个帖子之间最有可能的距离。研究团队使用了开发方法的各种组合来监视高层建筑项目的捕获图像中的护栏系统。比较精度和召回指标表明，级联分类器通过落地检测和护栏间距估计来取得更好的性能。研究结果表明，拟议的护栏识别系统可以改善护栏的评估，并促进安全工程师确定高层建筑项目中跌落危害的任务。