This work studies networked agents cooperating to track a dynamical state of nature under partial information. The proposed algorithm is a distributed Bayesian filtering algorithm for finite-state hidden Markov models (HMMs). It can be used for sequential state estimation tasks, as well as for modeling opinion formation over social networks under dynamic environments. We show that the disagreement with the optimal centralized solution is asymptotically bounded for the class of geometrically ergodic state transition models, which includes rapidly changing models. We also derive recursions for calculating the probability of error and establish convergence under Gaussian observation models. Simulations are provided to illustrate the theory and to compare against alternative approaches.

在本文中，我们考虑了分散的优化问题，在这些问题中，代理具有个人成本函数，以最大程度地减少受到子空间约束的约束，这些子空间约束需要整个网络的最小化器才能位于低维子空间中。这种约束的公式包括共识或单任务优化作为特殊情况，并允许更一般的任务相关性模型，例如多任务平滑度和耦合优化。为了应对沟通限制，我们提出并研究一种自适应分散策略，在该策略中，代理人在与邻居进行交流之前，使用差异随机量化器来压缩其估计。分析表明，在量化噪声的某些一般条件下，对于足够小的步长$ \ mu $，该策略在均方误差和平均比特率方面都是稳定的：通过减少$ \ mu $，可以将估计错误保持较小（按$ \ mu $）保持较小，而不会无限地增加比特率为$ \ mu \ rightarrow 0 $。模拟说明了理论发现和提议方法的有效性，表明可以实现分散学习，但仅需少量。

这项工作提出了一种分散的架构，其中个别代理旨在解决分类问题，同时观察不同尺寸的流特征，并从可能不同的分布产生。在社会学习的背景下，已经开发了几种有用的策略，通过跨分布式代理的本地合作解决了决策问题，并允许他们从流数据中学习。然而，传统的社会学习策略依赖于每个代理人对观察结果分布的重要知识的基本假设。在这项工作中，我们通过引入一种机器学习框架来克服这一问题，该机器学习框架利用图形的社交交互，导致分布式分类问题的完全数据驱动的解决方案。在拟议的社交机器学习（SML）策略中，存在两个阶段：在训练阶段，分类器被独立培训，以使用有限数量的训练样本来产生一组假设的信念;在预测阶段，分类器评估流媒体未标记的观察，并与邻近分类器共享他们的瞬时信仰。我们表明SML策略使得代理能够在这种高度异构的环境下一致地学习，并且即使在预测阶段决定未标记的样本时，即使在预测阶段也允许网络继续学习。预测决策用于以明显不同的方式不断地提高性能，这些方式与大多数现有的静态分类方案不同，在培训之后，未标记数据的决策不会重新用于改善未来的性能。

这项工作审查了旨在在通信约束下运行的自适应分布式学习策略。我们考虑一个代理网络，必须从持续观察流数据来解决在线优化问题。代理商实施了分布式合作策略，其中允许每个代理商与其邻居执行本地信息交换。为了应对通信约束，必须不可避免地压缩交换信息。我们提出了一种扩散策略，昵称为ACTC（适应 - 压缩 - 然后组合），其依赖于以下步骤：i）每个代理执行具有恒定步长大小的单独随机梯度更新的适应步骤; ii）一种压缩步骤，它利用最近引入的随机压缩操作员;和III）每个代理组合从其邻居接收的压缩更新的组合步骤。这项工作的区别要素如下。首先，我们专注于自适应策略，其中常数（而不是递减）阶梯大小对于实时响应非间断变化至关重要。其次，我们考虑一般的指导图表和左随机组合政策，使我们能够增强拓扑和学习之间的相互作用。第三，与对所有个人代理的成本职能承担强大的凸起的相关作品相比，我们只需要在网络水平的强大凸起，即使单个代理具有强凸的成本，剩余的代理商也不满足凸起成本。第四，我们专注于扩散（而不是共识）战略。在压缩信息的苛刻设置下，建立ACTC迭代在所需的优化器周围波动，在相邻代理之间交换的比特方面取得了显着的节省。

Testing Deep Learning (DL) based systems inherently requires large and representative test sets to evaluate whether DL systems generalise beyond their training datasets. Diverse Test Input Generators (TIGs) have been proposed to produce artificial inputs that expose issues of the DL systems by triggering misbehaviours. Unfortunately, such generated inputs may be invalid, i.e., not recognisable as part of the input domain, thus providing an unreliable quality assessment. Automated validators can ease the burden of manually checking the validity of inputs for human testers, although input validity is a concept difficult to formalise and, thus, automate. In this paper, we investigate to what extent TIGs can generate valid inputs, according to both automated and human validators. We conduct a large empirical study, involving 2 different automated validators, 220 human assessors, 5 different TIGs and 3 classification tasks. Our results show that 84% artificially generated inputs are valid, according to automated validators, but their expected label is not always preserved. Automated validators reach a good consensus with humans (78% accuracy), but still have limitations when dealing with feature-rich datasets.

Continual Learning, also known as Lifelong or Incremental Learning, has recently gained renewed interest among the Artificial Intelligence research community. Recent research efforts have quickly led to the design of novel algorithms able to reduce the impact of the catastrophic forgetting phenomenon in deep neural networks. Due to this surge of interest in the field, many competitions have been held in recent years, as they are an excellent opportunity to stimulate research in promising directions. This paper summarizes the ideas, design choices, rules, and results of the challenge held at the 3rd Continual Learning in Computer Vision (CLVision) Workshop at CVPR 2022. The focus of this competition is the complex continual object detection task, which is still underexplored in literature compared to classification tasks. The challenge is based on the challenge version of the novel EgoObjects dataset, a large-scale egocentric object dataset explicitly designed to benchmark continual learning algorithms for egocentric category-/instance-level object understanding, which covers more than 1k unique main objects and 250+ categories in around 100k video frames.

Recovery of true color from underwater images is an ill-posed problem. This is because the wide-band attenuation coefficients for the RGB color channels depend on object range, reflectance, etc. which are difficult to model. Also, there is backscattering due to suspended particles in water. Thus, most existing deep-learning based color restoration methods, which are trained on synthetic underwater datasets, do not perform well on real underwater data. This can be attributed to the fact that synthetic data cannot accurately represent real conditions. To address this issue, we use an image to image translation network to bridge the gap between the synthetic and real domains by translating images from synthetic underwater domain to real underwater domain. Using this multimodal domain adaptation technique, we create a dataset that can capture a diverse array of underwater conditions. We then train a simple but effective CNN based network on our domain adapted dataset to perform color restoration. Code and pre-trained models can be accessed at https://github.com/nehamjain10/TRUDGCR

我们介绍了de bruijn图神经网络（DBGNNS），这是一种新颖的时间感知图神经网络体系结构，用于动态图上的时间分辨数据。我们的方法解释了动态图的因果拓扑中展开的时间流行模式，该模式由因果步行确定，即节点可以随着时间的时间影响彼此的链接序列。我们的架构建立在多层de bruijn图的多层上，这是一个迭代的线图结构，其中d de bruijn图中的节点k表示长度k-1的步行，而边缘则表示长度k的步行。我们开发了一个图形神经网络体系结构，该架构利用de bruijn图来实现遵循非马克维亚动力学的消息传递方案，该方案使我们能够在动态图的因果拓扑中学习模式。解决de bruijn图形不同订单k的问题可用于建模相同的数据集，我们进一步应用统计模型选择以确定用于消息传递的最佳图形拓扑。合成和经验数据集的评估表明，DBGNN可以利用动态图中的时间模式，从而大大改善了监督节点分类任务中的性能。

我们提出了一种系统解决方案，以实现使用热图像和惯性测量的飞行机器人团队的数据效率，分散的状态估计。每个机器人可以独立飞行，并在可能的情况下交换数据以完善其状态估计。我们的系统前端应用在线光度校准以完善热图像，从而增强功能跟踪并放置识别。我们的系统后端使用协方差融合策略来忽略代理之间的互相关，以降低内存使用和计算成本。通信管道使用本地汇总的描述符（VLAD）的向量来构建需要较低带宽使用情况的请求响应策略。我们在合成数据和现实世界数据上测试我们的协作方法。我们的结果表明，相对于个人代理方法，该提出的方法最多可提高46％的轨迹估计，同时减少多达89％的通信交换。数据集和代码将发布给公众，扩展了已经发布的JPL XVIO库。

可以使用几种技术来解决沿规定路径的最佳运动计划，但是大多数技术没有考虑到与环境接触时最终效用器所施加的扳手。当无法获得环境的动态模型时，就不存在合并方法来考虑相互作用的效果。无论要优化的特定性能指数如何，本文都提出了一种策略，将外部扳手包括在最佳计划算法中，考虑到任务规格。此过程是针对最小时间轨迹实例化的，并在接纳控制下执行交互任务的真实机器人进行了验证。结果证明，最终效应器扳手的包含会影响计划的轨迹，实际上改变了操纵器的动态能力。