最近有关于高斯神经网络（NNS）的大宽度特性的文献，即，其权重根据高斯分布分布。两个流行的问题是：i）研究NNS的大宽度行为，这些行为在高斯工艺方面提供了无限宽的限制的表征； ii）对NNS的大宽度训练动力学的研究，该动力在训练后NN和执行核回归之间具有等效性，并以确定性核为确定性内核，称为神经切线核（NTK）。在本文中，我们考虑了$ \ alpha $ stable NNS的这些问题，通过假设NN的权重分配为$ \ alpha $ - 稳定分布，它通过$ \ alpha \ in（0,2] $，概括了Gaussian nns。即带有沉重的尾巴的分布。对于带有relu激活功能的浅$ \ alpha $ stable nns，我们表明，如果NN的宽度转移到无穷大，那么重新缩放的NN弱收敛到$ \ alpha $ stable的过程，即随机的过程具有$ \ alpha $稳定的有限维分布的过程。作为高斯设置的新颖性，在$ \ alpha $稳定的设置中，激活功能的选择会影响NN的缩放，即：实现无限宽的$ \ alpha $稳定过程，relu功能需要相对于子线性函数进行附加的对数缩放。那么，我们的主要贡献是对浅的$ \ alpha $ stable relu-nns的NTK分析，这是领导的在训练恢复的NN和执行内核回归机智之间具有等效性h $（\ alpha/2）$ - 稳定的随机内核。这种内核的随机性是相对于高斯环境的进一步新颖性，即：在$ \ alpha $稳定性中，初始化时NN的随机性在NTK分析中不会消失，从而诱导了分布的分布基础内核回归的内核。

在现代深度学习中，最近又越来越多的文献，关于深高斯神经网络（NNS）的大宽度渐近性能之间的相互作用，即具有高斯分布重量的深NNS和高斯随机过程（SPS）。事实证明，这种相互作用在高斯SP先验下的贝叶斯推论中至关重要，对通过梯度下降训练的无限宽的深NN的内核回归以及无限宽的NN中的信息传播。通过经验分析的激励，该经验分析表明了用稳定的NN重量代替高斯分布的潜力，在本文中，我们对（完全连接的）进料深度稳定NN的大差异行为进行了严格的分析，即深NNS，即具有稳定的分布重量。我们表明，随着宽度共同在NN的层上共同进入无限，即``关节生长''的设置，重新缩放的深稳定nn弱收敛到稳定的SP，其分布通过NN的层递归地表征。 NN的三角结构，这是一个非标准的渐近问题，我们提出了一种独立利益的感应方法。然后，我们在````''''下建立了对稳定的SP的Sup-Norm收敛速率，``关节增长和``顺序增长''的宽度在NN的层上。这样的结果提供了'关节增长'和``顺序增长''的差异，表明前者的速率比速度慢。后者根据层的深度和NN的投入数量。我们的工作扩展了有关深gaussian nns无限宽限制的一些最新结果，以至于更通用的深稳定稳定性NNS，这是第一个结果，这是对融合率的第一个结果。``联合增长''环境。

计数示意图（CMS）是一个时间和内存有效的随机数据结构，可根据随机哈希的数据提供令牌数据流（即点查询）中代币频率的估计。 CAI，Mitzenmacher和Adams（\ textit {neurips} 2018）提出了CMS的学习增强版本，称为CMS-DP，它依赖于贝叶斯非参与式（BNP）模型通过dirichlet过程（DP），给定数据，估计点查询作为位置查询后验分布的合适平均功能的估计值给定数据。尽管CMS-DP已被证明可以改善CMS的某些方面，但它具有``建设性的''证明的主要缺点，该证明是基于针对DP先验的论点构建的，即对其他非参数priors不使用的论点。在本文中，我们提出了CMS-DP的``贝叶斯''证明，其主要优点是基于原则上可用的参数，在广泛的非参数先验中，这是由归一化的完全随机措施引起的。该结果导致在Power-Law数据流下开发了一种新颖的学习增强的CMS，称为CMS-PYP，该CMS-PYP依赖于Pitman-Yor流程（PYP）的BNP模型。在这个更一般的框架下，我们应用了CMS-DP的``贝叶斯人''证明的论点，适当地适合PYP先验，以计算鉴于Hashed Data。数据和真实文本数据显示，CMS-PYP在估计低频代币方面优于CMS和CMS-DP，这在文本数据中是至关重要的，并且相对于CMS的变化，它具有竞争力还讨论了为低频代币设计的。还讨论了我们BNP方法扩展到更通用的查询。

It is well known that conservative mechanical systems exhibit local oscillatory behaviours due to their elastic and gravitational potentials, which completely characterise these periodic motions together with the inertial properties of the system. The classification of these periodic behaviours and their geometric characterisation are in an on-going secular debate, which recently led to the so-called eigenmanifold theory. The eigenmanifold characterises nonlinear oscillations as a generalisation of linear eigenspaces. With the motivation of performing periodic tasks efficiently, we use tools coming from this theory to construct an optimization problem aimed at inducing desired closed-loop oscillations through a state feedback law. We solve the constructed optimization problem via gradient-descent methods involving neural networks. Extensive simulations show the validity of the approach.

Detecting anomalous data within time series is a very relevant task in pattern recognition and machine learning, with many possible applications that range from disease prevention in medicine, e.g., detecting early alterations of the health status before it can clearly be defined as "illness" up to monitoring industrial plants. Regarding this latter application, detecting anomalies in an industrial plant's status firstly prevents serious damages that would require a long interruption of the production process. Secondly, it permits optimal scheduling of maintenance interventions by limiting them to urgent situations. At the same time, they typically follow a fixed prudential schedule according to which components are substituted well before the end of their expected lifetime. This paper describes a case study regarding the monitoring of the status of Laser-guided Vehicles (LGVs) batteries, on which we worked as our contribution to project SUPER (Supercomputing Unified Platform, Emilia Romagna) aimed at establishing and demonstrating a regional High-Performance Computing platform that is going to represent the main Italian supercomputing environment for both computing power and data volume.

Methods based on ordinary differential equations (ODEs) are widely used to build generative models of time-series. In addition to high computational overhead due to explicitly computing hidden states recurrence, existing ODE-based models fall short in learning sequence data with sharp transitions - common in many real-world systems - due to numerical challenges during optimization. In this work, we propose LS4, a generative model for sequences with latent variables evolving according to a state space ODE to increase modeling capacity. Inspired by recent deep state space models (S4), we achieve speedups by leveraging a convolutional representation of LS4 which bypasses the explicit evaluation of hidden states. We show that LS4 significantly outperforms previous continuous-time generative models in terms of marginal distribution, classification, and prediction scores on real-world datasets in the Monash Forecasting Repository, and is capable of modeling highly stochastic data with sharp temporal transitions. LS4 sets state-of-the-art for continuous-time latent generative models, with significant improvement of mean squared error and tighter variational lower bounds on irregularly-sampled datasets, while also being x100 faster than other baselines on long sequences.

This project leverages advances in multi-agent reinforcement learning (MARL) to improve the efficiency and flexibility of order-picking systems for commercial warehouses. We envision a warehouse of the future in which dozens of mobile robots and human pickers work together to collect and deliver items within the warehouse. The fundamental problem we tackle, called the order-picking problem, is how these worker agents must coordinate their movement and actions in the warehouse to maximise performance (e.g. order throughput) under given resource constraints. Established industry methods using heuristic approaches require large engineering efforts to optimise for innately variable warehouse configurations. In contrast, the MARL framework can be flexibly applied to any warehouse configuration (e.g. size, layout, number/types of workers, item replenishment frequency) and the agents learn via a process of trial-and-error how to optimally cooperate with one another. This paper details the current status of the R&D effort initiated by Dematic and the University of Edinburgh towards a general-purpose and scalable MARL solution for the order-picking problem in realistic warehouses.

With the rise in high resolution remote sensing technologies there has been an explosion in the amount of data available for forest monitoring, and an accompanying growth in artificial intelligence applications to automatically derive forest properties of interest from these datasets. Many studies use their own data at small spatio-temporal scales, and demonstrate an application of an existing or adapted data science method for a particular task. This approach often involves intensive and time-consuming data collection and processing, but generates results restricted to specific ecosystems and sensor types. There is a lack of widespread acknowledgement of how the types and structures of data used affects performance and accuracy of analysis algorithms. To accelerate progress in the field more efficiently, benchmarking datasets upon which methods can be tested and compared are sorely needed. Here, we discuss how lack of standardisation impacts confidence in estimation of key forest properties, and how considerations of data collection need to be accounted for in assessing method performance. We present pragmatic requirements and considerations for the creation of rigorous, useful benchmarking datasets for forest monitoring applications, and discuss how tools from modern data science can improve use of existing data. We list a set of example large-scale datasets that could contribute to benchmarking, and present a vision for how community-driven, representative benchmarking initiatives could benefit the field.

In this work, we devise robust and efficient learning protocols for orchestrating a Federated Learning (FL) process for the Federated Tumor Segmentation Challenge (FeTS 2022). Enabling FL for FeTS setup is challenging mainly due to data heterogeneity among collaborators and communication cost of training. To tackle these challenges, we propose Robust Learning Protocol (RoLePRO) which is a combination of server-side adaptive optimisation (e.g., server-side Adam) and judicious parameter (weights) aggregation schemes (e.g., adaptive weighted aggregation). RoLePRO takes a two-phase approach, where the first phase consists of vanilla Federated Averaging, while the second phase consists of a judicious aggregation scheme that uses a sophisticated reweighting, all in the presence of an adaptive optimisation algorithm at the server. We draw insights from extensive experimentation to tune learning rates for the two phases.

The paper addresses the problem of time offset synchronization in the presence of temperature variations, which lead to a non-Gaussian environment. In this context, regular Kalman filtering reveals to be suboptimal. A functional optimization approach is developed in order to approximate optimal estimation of the clock offset between master and slave. A numerical approximation is provided to this aim, based on regular neural network training. Other heuristics are provided as well, based on spline regression. An extensive performance evaluation highlights the benefits of the proposed techniques, which can be easily generalized to several clock synchronization protocols and operating environments.