在实践中，只要可以设计教学代理以提供专家监督，仿制学习就是纯粹的加强学习。但是，我们表明，当教学代理商决定与学生无法访问的特权信息时，在模仿学习期间，此信息被边缘化，导致“模仿差距”，导致潜在，差距。先前的工作通过仿制学习的仿制学习来弥合这一差距。虽然经常成功，但逐步的进展失败，需要频繁切换勘探和记忆之间的频繁交换。为了更好地解决这些任务并减轻模仿缺口，我们提出“适应性不管”（顾问）。顾问在培训期间动态重量仿制和奖励的加固学习损失，在模仿和探索之间启用了在线切换。在Gridworlds中设置的一套充满挑战的任务，多代理粒子环境和高保真3D模拟器，我们展示了与顾问的在线交换，优于纯粹的模仿，纯粹的加固学习以及它们的顺序和并行组合。

This paper presents a method for adding multiple tasks to a single deep neural network while avoiding catastrophic forgetting. Inspired by network pruning techniques, we exploit redundancies in large deep networks to free up parameters that can then be employed to learn new tasks. By performing iterative pruning and network re-training, we are able to sequentially "pack" multiple tasks into a single network while ensuring minimal drop in performance and minimal storage overhead. Unlike prior work that uses proxy losses to maintain accuracy on older tasks, we always optimize for the task at hand. We perform extensive experiments on a variety of network architectures and largescale datasets, and observe much better robustness against catastrophic forgetting than prior work. In particular, we are able to add three fine-grained classification tasks to a single ImageNet-trained VGG-16 network and achieve accuracies close to those of separately trained networks for each task. Code available at https://github.com/ arunmallya/packnet

The Flickr30k dataset has become a standard benchmark for sentence-based image description. This paper presents Flickr30k Entities, which augments the 158k captions from Flickr30k with 244k coreference chains, linking mentions of the same entities across different captions for the same image, and associating them with 276k manually annotated bounding boxes. Such annotations are essential for continued progress in automatic image description and grounded language understanding. They enable us to define a new benchmark for localization of textual entity mentions in an image. We present a strong baseline for this task that combines an image-text embedding, detectors for common objects, a color classifier, and a bias towards selecting larger objects. While our baseline rivals in accuracy more complex state-of-the-art models, we show that its gains cannot be easily parlayed into improvements on such tasks as image-sentence retrieval, thus underlining the limitations of current methods and the need for further research.

We introduce a linguistically enhanced combination of pre-training methods for transformers. The pre-training objectives include POS-tagging, synset prediction based on semantic knowledge graphs, and parent prediction based on dependency parse trees. Our approach achieves competitive results on the Natural Language Inference task, compared to the state of the art. Specifically for smaller models, the method results in a significant performance boost, emphasizing the fact that intelligent pre-training can make up for fewer parameters and help building more efficient models. Combining POS-tagging and synset prediction yields the overall best results.

Designing efficient and labor-saving prosthetic hands requires powerful hand gesture recognition algorithms that can achieve high accuracy with limited complexity and latency. In this context, the paper proposes a compact deep learning framework referred to as the CT-HGR, which employs a vision transformer network to conduct hand gesture recognition using highdensity sEMG (HD-sEMG) signals. The attention mechanism in the proposed model identifies similarities among different data segments with a greater capacity for parallel computations and addresses the memory limitation problems while dealing with inputs of large sequence lengths. CT-HGR can be trained from scratch without any need for transfer learning and can simultaneously extract both temporal and spatial features of HD-sEMG data. Additionally, the CT-HGR framework can perform instantaneous recognition using sEMG image spatially composed from HD-sEMG signals. A variant of the CT-HGR is also designed to incorporate microscopic neural drive information in the form of Motor Unit Spike Trains (MUSTs) extracted from HD-sEMG signals using Blind Source Separation (BSS). This variant is combined with its baseline version via a hybrid architecture to evaluate potentials of fusing macroscopic and microscopic neural drive information. The utilized HD-sEMG dataset involves 128 electrodes that collect the signals related to 65 isometric hand gestures of 20 subjects. The proposed CT-HGR framework is applied to 31.25, 62.5, 125, 250 ms window sizes of the above-mentioned dataset utilizing 32, 64, 128 electrode channels. The average accuracy over all the participants using 32 electrodes and a window size of 31.25 ms is 86.23%, which gradually increases till reaching 91.98% for 128 electrodes and a window size of 250 ms. The CT-HGR achieves accuracy of 89.13% for instantaneous recognition based on a single frame of HD-sEMG image.

Mission teams are exposed to the emotional toll of life and death decisions. These are small groups of specially trained people supported by intelligent machines for dealing with stressful environments and scenarios. We developed a composite model for stress monitoring in such teams of human and autonomous machines. This modelling aims to identify the conditions that may contribute to mission failure. The proposed model is composed of three parts: 1) a computational logic part that statically describes the stress states of teammates; 2) a decision part that manifests the mission status at any time; 3) a stress propagation part based on standard Susceptible-Infected-Susceptible (SIS) paradigm. In contrast to the approaches such as agent-based, random-walk and game models, the proposed model combines various mechanisms to satisfy the conditions of stress propagation in small groups. Our core approach involves data structures such as decision tables and decision diagrams. These tools are adaptable to human-machine teaming as well.

尽管存在许多减少卷积神经网络（CNN）过度拟合的方法，但仍不清楚如何自信地衡量过度拟合的程度。但是，反映过度拟合水平的度量可能非常有用，可对不同体系结构的比较和评估各种技术来应对过度拟合。由于过度拟合的神经网络倾向于记住训练数据中的噪声而不是普遍看不见的数据，因此我们研究了训练精度在增加数据扰动的存在并研究与过度拟合的联系时如何变化。尽管以前的工作仅针对标签噪声，但我们还是研究了一系列技术，以将噪声注入训练数据，包括对抗性扰动和输入损坏。基于此，我们定义了两个新的指标，可以自信地区分正确的模型和过度拟合模型。为了进行评估，我们得出了事先已知过度拟合行为的模型池。为了测试各种因素的效果，我们基于VGG和Resnet引入了架构中的几种反拟合措施，并研究其影响，包括正则化技术，训练集大小和参数数量。最后，我们通过测量模型池外几个CNN体系结构的过度拟合度来评估所提出的指标的适用性。

基于对抗斑块的攻击旨在欺骗一个有意产生的噪声的神经网络，该网络集中在输入图像的特定区域中。在这项工作中，我们对不同的贴片生成参数进行了深入的分析，包括初始化，贴剂大小，尤其是在训练过程中将贴剂放置在图像中。我们专注于对象消失的攻击，并以Yolov3作为白色盒子设置中的攻击的模型运行实验，并使用COCO数据集中的图像。我们的实验表明，在训练期间，将斑块插入大小增加的窗口内，与固定位置相比，攻击强度显着提高。当斑块在训练过程中随机定位时，获得了最佳结果，而贴片位置则在批处理中也有所不同。

在本文中，我们研究了视觉和热图像的性能和公平性，并将评估扩展到掩盖的合成图像。使用SeadyFace和Thermal掩码数据集，我们提出了一个过程来评估真实图像的公平性，并显示如何将同一过程应用于合成图像。随机猜测的人口统计差异为1.59，当识别性能提高到99.99 \％时，人口统计学差异为1.59。我们表明，固有的偏见数据集可以深深影响任何生物识别系统的公平性。偏见数据集的主要原因是由于数据收集过程而导致的类不平衡。为了解决不平衡的数据集，可以使用合成图像来增强样品的较少类，以生成更平衡的数据集，从而在训练机器学习系统时产生较小的偏见。对于支持生物特征的系统，公平性至关重要，而相关的公平，多样性和包容性（EDI）的相关概念非常适合生物识别技术公平性的概括，我们专注于3个最常见的人口统计组年龄，性别和种族。

在许多科学领域中发现一个有意义的，尺寸同质的，象征性的表达是一个基本挑战。我们提出了一个新颖的开源计算框架，称为科学家机器方程探测器（Scimed），该框架将科学纪律智慧与科学家在循环的方法中融合在一起，并将其与最先进的符号回归（SR）方法相结合。Scimed将基于遗传算法的包装器选择方法与自动机器学习和两个SR方法结合在一起。我们对具有和没有非线性空气动力学阻力的球体沉降的四个配置进行了测试。我们表明，疲惫不堪的人足够坚固，可以从嘈杂的数据中发现正确的物理有意义的符号表达式。我们的结果表明，与最先进的SR软件包相比，这些任务的性能更好。