在过去几年中，自动驾驶一直是最受欢迎，最具挑战性的主题之一。在实现完全自治的道路上，研究人员使用了各种传感器，例如LIDAR，相机，惯性测量单元（IMU）和GPS，并开发了用于自动驾驶应用程序的智能算法，例如对象检测，对象段，障碍，避免障碍物，避免障碍物和障碍物，以及路径计划。近年来，高清（HD）地图引起了很多关注。由于本地化中高清图的精度和信息水平很高，因此它立即成为自动驾驶的关键组成部分之一。从Baidu Apollo，Nvidia和TomTom等大型组织到个别研究人员，研究人员创建了用于自主驾驶的不同场景和用途的高清地图。有必要查看高清图生成的最新方法。本文回顾了最新的高清图生成技术，这些技术利用了2D和3D地图生成。这篇评论介绍了高清图的概念及其在自主驾驶中的有用性，并详细概述了高清地图生成技术。我们还将讨论当前高清图生成技术的局限性，以激发未来的研究。

本文介绍了使用变压器解决关键点检测和实例关联的新方法。对于自下而上的多人姿势估计模型，他们需要检测关键点并在关键点之间学习关联信息。我们认为这些问题可以完全由变压器解决。具体而言，变压器中的自我关注测量任何一对位置之间的依赖性，这可以为关键点分组提供关联信息。但是，天真的注意力模式仍然没有主观控制，因此无法保证关键点始终会参加它们所属的实例。为了解决它，我们提出了一种监督多人关键点检测和实例关联的自我关注的新方法。通过使用实例掩码来监督自我关注的实例感知，我们可以基于成对引人注定分数为其对应的实例分配检测到的关键字，而无需使用预定义的偏移量字段或嵌入像基于CNN的自下而上模型。我们方法的另一个好处是可以从监督的注意矩阵直接获得任何数量的人的实例分段结果，从而简化了像素分配管道。对Coco多人关键点检测挑战和人实例分割任务的实验证明了所提出的方法的有效性和简单性，并显示出于针对特定目的控制自我关注行为的有希望的方法。

Optimal transport (OT) has become a widely used tool in the machine learning field to measure the discrepancy between probability distributions. For instance, OT is a popular loss function that quantifies the discrepancy between an empirical distribution and a parametric model. Recently, an entropic penalty term and the celebrated Sinkhorn algorithm have been commonly used to approximate the original OT in a computationally efficient way. However, since the Sinkhorn algorithm runs a projection associated with the Kullback-Leibler divergence, it is often vulnerable to outliers. To overcome this problem, we propose regularizing OT with the \beta-potential term associated with the so-called $\beta$-divergence, which was developed in robust statistics. Our theoretical analysis reveals that the $\beta$-potential can prevent the mass from being transported to outliers. We experimentally demonstrate that the transport matrix computed with our algorithm helps estimate a probability distribution robustly even in the presence of outliers. In addition, our proposed method can successfully detect outliers from a contaminated dataset

In the era of Internet of Things (IoT), network-wide anomaly detection is a crucial part of monitoring IoT networks due to the inherent security vulnerabilities of most IoT devices. Principal Components Analysis (PCA) has been proposed to separate network traffics into two disjoint subspaces corresponding to normal and malicious behaviors for anomaly detection. However, the privacy concerns and limitations of devices' computing resources compromise the practical effectiveness of PCA. We propose a federated PCA-based Grassmannian optimization framework that coordinates IoT devices to aggregate a joint profile of normal network behaviors for anomaly detection. First, we introduce a privacy-preserving federated PCA framework to simultaneously capture the profile of various IoT devices' traffic. Then, we investigate the alternating direction method of multipliers gradient-based learning on the Grassmann manifold to guarantee fast training and the absence of detecting latency using limited computational resources. Empirical results on the NSL-KDD dataset demonstrate that our method outperforms baseline approaches. Finally, we show that the Grassmann manifold algorithm is highly adapted for IoT anomaly detection, which permits drastically reducing the analysis time of the system. To the best of our knowledge, this is the first federated PCA algorithm for anomaly detection meeting the requirements of IoT networks.

In this paper, we propose a novel architecture, the Enhanced Interactive Transformer (EIT), to address the issue of head degradation in self-attention mechanisms. Our approach replaces the traditional multi-head self-attention mechanism with the Enhanced Multi-Head Attention (EMHA) mechanism, which relaxes the one-to-one mapping constraint among queries and keys, allowing each query to attend to multiple keys. Furthermore, we introduce two interaction models, Inner-Subspace Interaction and Cross-Subspace Interaction, to fully utilize the many-to-many mapping capabilities of EMHA. Extensive experiments on a wide range of tasks (e.g. machine translation, abstractive summarization, grammar correction, language modelling and brain disease automatic diagnosis) show its superiority with a very modest increase in model size.

Task transfer learning is a popular technique in image processing applications that uses pre-trained models to reduce the supervision cost of related tasks. An important question is to determine task transferability, i.e. given a common input domain, estimating to what extent representations learned from a source task can help in learning a target task. Typically, transferability is either measured experimentally or inferred through task relatedness, which is often defined without a clear operational meaning. In this paper, we present a novel metric, H-score, an easily-computable evaluation function that estimates the performance of transferred representations from one task to another in classification problems using statistical and information theoretic principles. Experiments on real image data show that our metric is not only consistent with the empirical transferability measurement, but also useful to practitioners in applications such as source model selection and task transfer curriculum learning.

Summary quality assessment metrics have two categories: reference-based and reference-free. Reference-based metrics are theoretically more accurate but are limited by the availability and quality of the human-written references, which are both difficulty to ensure. This inspires the development of reference-free metrics, which are independent from human-written references, in the past few years. However, existing reference-free metrics cannot be both zero-shot and accurate. In this paper, we propose a zero-shot but accurate reference-free approach in a sneaky way: feeding documents, based upon which summaries generated, as references into reference-based metrics. Experimental results show that this zero-shot approach can give us the best-performing reference-free metrics on nearly all aspects on several recently-released datasets, even beating reference-free metrics specifically trained for this task sometimes. We further investigate what reference-based metrics can benefit from such repurposing and whether our additional tweaks help.

The quality of knowledge retrieval is crucial in knowledge-intensive conversations. Two common strategies to improve the retrieval quality are finetuning the retriever or generating a self-contained query, while they encounter heavy burdens on expensive computation and elaborate annotations. In this paper, we propose an unsupervised query enhanced approach for knowledge-intensive conversations, namely QKConv. There are three modules in QKConv: a query generator, an off-the-shelf knowledge selector, and a response generator. Without extra supervision, the end-to-end joint training of QKConv explores multiple candidate queries and utilizes corresponding selected knowledge to yield the target response. To evaluate the effectiveness of the proposed method, we conducted comprehensive experiments on conversational question-answering, task-oriented dialogue, and knowledge-grounded conversation. Experimental results demonstrate that QKConv achieves state-of-the-art performance compared to unsupervised methods and competitive performance compared to supervised methods.

In this paper, we carry out numerical analysis to prove convergence of a novel sample-wise back-propagation method for training a class of stochastic neural networks (SNNs). The structure of the SNN is formulated as discretization of a stochastic differential equation (SDE). A stochastic optimal control framework is introduced to model the training procedure, and a sample-wise approximation scheme for the adjoint backward SDE is applied to improve the efficiency of the stochastic optimal control solver, which is equivalent to the back-propagation for training the SNN. The convergence analysis is derived with and without convexity assumption for optimization of the SNN parameters. Especially, our analysis indicates that the number of SNN training steps should be proportional to the square of the number of layers in the convex optimization case. Numerical experiments are carried out to validate the analysis results, and the performance of the sample-wise back-propagation method for training SNNs is examined by benchmark machine learning examples.

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.