尽管深度神经网络能够在各个领域中实现最先进的性能，但他们的培训通常需要对数据集的许多通行证进行迭代。但是，由于计算和内存约束和潜在的隐私问题，在数据到达流中的许多现实情况下，存储和访问所有数据都是不切实际的。在本文中，我们研究了一通学习的问题，其中模型是在未重新验证之前对数据进行依次到达数据的培训。通过越来越多参数化模型的使用，我们开发了正交递归拟合（ORFIT），这是一种用于一通学习的算法，旨在完全适合每个新数据点，同时在更改参数的方向上，导致对先前预测的最小变化参数数据点。通过这样做，我们在自适应过滤和机器学习中桥接了两种看似不同的算法，即递归最小二乘（RLS）算法和正交梯度下降（OGD）。我们的算法通过通过增量主组件分析（IPCA）利用流数据的结构来有效地使用内存。此外，我们表明，对于过度参数的线性模型，我们算法获得的参数矢量是随机梯度下降（SGD）在标准的多通用设置中收敛到的。最后，我们将结果推广到高度参数化模型的非线性设置，这与深度学习有关。我们的实验显示了与基准相比，提出的方法的有效性。

当今一些最先进的深度学习模型的出色表现在某种程度上是由于在大型数据集上进行了广泛的（自我）监督的对比预处理。相比之下，该网络是通过成对的正（相似）和负（不同的）数据点呈现的，并经过培训以找到每个数据点的嵌入向量，即一个表示形式，可以进一步调整各种下游任务。为了将这些模型安全地部署在关键的决策系统中，至关重要的是要使他们衡量其不确定性或可靠性。然而，由于训练对比模型的成对性质，并且在输出（抽象嵌入矢量）上缺乏绝对标签，因此将常规不确定性估计技术适应此类模型是不平凡的。在这项工作中，我们研究是否可以以有意义的方式量化此类表示形式的不确定性。换句话说，我们探索给定数据点上的下游性能是否可以直接从其预训练的嵌入中预测。我们表明，可以通过直接估算嵌入空间中训练数据的分布并考虑表示表示的局部一致性来实现此目标。我们的实验表明，嵌入向量的不确定性概念通常与其下游精度密切相关。

The field of autonomous mobile robots has undergone dramatic advancements over the past decades. Despite achieving important milestones, several challenges are yet to be addressed. Aggregating the achievements of the robotic community as survey papers is vital to keep the track of current state-of-the-art and the challenges that must be tackled in the future. This paper tries to provide a comprehensive review of autonomous mobile robots covering topics such as sensor types, mobile robot platforms, simulation tools, path planning and following, sensor fusion methods, obstacle avoidance, and SLAM. The urge to present a survey paper is twofold. First, autonomous navigation field evolves fast so writing survey papers regularly is crucial to keep the research community well-aware of the current status of this field. Second, deep learning methods have revolutionized many fields including autonomous navigation. Therefore, it is necessary to give an appropriate treatment of the role of deep learning in autonomous navigation as well which is covered in this paper. Future works and research gaps will also be discussed.

Regularising the parameter matrices of neural networks is ubiquitous in training deep models. Typical regularisation approaches suggest initialising weights using small random values, and to penalise weights to promote sparsity. However, these widely used techniques may be less effective in certain scenarios. Here, we study the Koopman autoencoder model which includes an encoder, a Koopman operator layer, and a decoder. These models have been designed and dedicated to tackle physics-related problems with interpretable dynamics and an ability to incorporate physics-related constraints. However, the majority of existing work employs standard regularisation practices. In our work, we take a step toward augmenting Koopman autoencoders with initialisation and penalty schemes tailored for physics-related settings. Specifically, we propose the "eigeninit" initialisation scheme that samples initial Koopman operators from specific eigenvalue distributions. In addition, we suggest the "eigenloss" penalty scheme that penalises the eigenvalues of the Koopman operator during training. We demonstrate the utility of these schemes on two synthetic data sets: a driven pendulum and flow past a cylinder; and two real-world problems: ocean surface temperatures and cyclone wind fields. We find on these datasets that eigenloss and eigeninit improves the convergence rate by up to a factor of 5, and that they reduce the cumulative long-term prediction error by up to a factor of 3. Such a finding points to the utility of incorporating similar schemes as an inductive bias in other physics-related deep learning approaches.

Backpropagation is widely used to train artificial neural networks, but its relationship to synaptic plasticity in the brain is unknown. Some biological models of backpropagation rely on feedback projections that are symmetric with feedforward connections, but experiments do not corroborate the existence of such symmetric backward connectivity. Random feedback alignment offers an alternative model in which errors are propagated backward through fixed, random backward connections. This approach successfully trains shallow models, but learns slowly and does not perform well with deeper models or online learning. In this study, we develop a novel meta-plasticity approach to discover interpretable, biologically plausible plasticity rules that improve online learning performance with fixed random feedback connections. The resulting plasticity rules show improved online training of deep models in the low data regime. Our results highlight the potential of meta-plasticity to discover effective, interpretable learning rules satisfying biological constraints.

We consider a radio resource management (RRM) problem in a multi-user wireless network, where the goal is to optimize a network-wide utility function subject to constraints on the ergodic average performance of users. We propose a state-augmented parameterization for the RRM policy, where alongside the instantaneous network states, the RRM policy takes as input the set of dual variables corresponding to the constraints. We provide theoretical justification for the feasibility and near-optimality of the RRM decisions generated by the proposed state-augmented algorithm. Focusing on the power allocation problem with RRM policies parameterized by a graph neural network (GNN) and dual variables sampled from the dual descent dynamics, we numerically demonstrate that the proposed approach achieves a superior trade-off between mean, minimum, and 5th percentile rates than baseline methods.

Timely and effective response to humanitarian crises requires quick and accurate analysis of large amounts of text data - a process that can highly benefit from expert-assisted NLP systems trained on validated and annotated data in the humanitarian response domain. To enable creation of such NLP systems, we introduce and release HumSet, a novel and rich multilingual dataset of humanitarian response documents annotated by experts in the humanitarian response community. The dataset provides documents in three languages (English, French, Spanish) and covers a variety of humanitarian crises from 2018 to 2021 across the globe. For each document, HUMSET provides selected snippets (entries) as well as assigned classes to each entry annotated using common humanitarian information analysis frameworks. HUMSET also provides novel and challenging entry extraction and multi-label entry classification tasks. In this paper, we take a first step towards approaching these tasks and conduct a set of experiments on Pre-trained Language Models (PLM) to establish strong baselines for future research in this domain. The dataset is available at https://blog.thedeep.io/humset/.

随着人工智能的最新进展，可以在人类日常生活的各个方面看到其应用。从语音助手到移动医疗保健和自动驾驶，我们依靠AI方法的性能来完成许多关键任务；因此，必须以适当的手段进行预防损坏的方式主张模型的性能。通常，AI模型的短缺，尤其是深度机器学习，当面对数据分布的变化时，性能下降。尽管如此，在现实世界应用中始终期望这些转变。因此，已经出现了一个研究领域，重点是检测分布外数据子集并实现更全面的概括。此外，由于许多基于深度学习的模型在基准数据集上取得了近乎完美的结果，因此需要评估这些模型的可靠性和可靠性以推向现实世界应用程序的需求，这比以往任何时候都更加强烈。这引起了越来越多的研究领域的研究和领域的概括，这引起了对从各个角度比较这些研究进行比较的调查的需求，并突出了它们的平直和弱点。本文提出了一项调查，除了审查该领域的70多篇论文外，还提出了未来作品的挑战和方向，并为各种类型的数据转移和解决方案提供了统一的外观，以更好地泛化。

包括无人驾驶汽车（UAV）在内的自动移动机器人因其在建筑中的应用而受到了极大的关注。这些平台具有极大的潜力，可以自动化和增强许多任务所需数据的质量和频率，例如施工时间表更新，检查和监视。强大的本地化是可靠部署自动机器人平台的关键推动力。自动化的机器人解决方案主要依靠全球定位系统（GPS）进行户外定位。但是，GPS信号在室内被拒绝，并且经常使用预建的环境图来室内定位。这需要通过对环境中的移动机器人进行远程操作来产生高质量的地图。这种方法不仅耗时且乏味，而且在室内建筑环境中也是不可靠的。布局随着施工的进度而变化，需要频繁的映射会话来支持自主任务。此外，依赖视觉特征的基于视觉解决方案的有效性在现场低质地和重复区域都受到高度影响。为了应对这些挑战，我们以前提出了使用Apriltags的低成本，轻巧的基于标签的视觉惯性定位方法。在这种方法中，标签是具有已知尺寸和位置的纸张可打印地标，代表环境的准图。由于标签放置/更换是一个手动过程，因此它会遭受人体错误。在这项工作中，我们研究了人体错误在手动标签安装过程中的影响，并提出了一种随机方法，以使用谎言组理论来解决这种不确定性。使用蒙特卡洛模拟，我们通过实验表明，在我们的Manifold配方中纳入的拟议随机模型可提高基于标签的定位对在现场手动标签安装中不可避免的瑕疵的鲁棒性和准确性。

神经网络是众多远期过程的强大代孕。这种代理人的反转在科学和工程中非常有价值。成功的神经反向方法的最重要属性是在现实世界中（即在本地远期过程（不仅是学识渊博的替代）中部署在现实世界中时的解决方案的性能。我们建议自动化，这是一种高度自动化的神经网络代理的方法。我们的主要见解是在可靠数据附近寻求反向解决方案，这些解决方案已被取样形式，并用于训练替代模型。自动信息通过考虑替代物的预测不确定性并在反转过程中最小化，从而找到了这种解决方案。除了高精度外，自动验证液可以实现溶液的可行性，并带有嵌入式正规化，并且不含初始化。我们通过解决控制，制造和设计中的一系列现实世界问题来验证我们的方法。