可以与其他代理人互动以完成给定任务的自主代理的发展是人工智能和机器学习研究的核心领域。为了实现这一目标，自主代理研究小组开发了用于自主系统控制的新型机器学习算法，特别关注深度强化学习和多代理强化学习。研究问题包括可扩展的协调代理政策和代理间沟通；从有限观察的情况下对其他代理的行为，目标和组成的推理；以及基于内在动机，课程学习，因果推断和代表性学习的样品学习。本文概述了该小组正在进行的研究组合，并讨论了未来方向的开放问题。

变形金刚已成为主要的机器学习工作负载，它们不仅是自然语言处理任务的事实上的标准，而且还将部署在其他领域，例如视觉和语音识别。许多基于变压器的应用程序都是实时系统，例如机器翻译和Web搜索。这些实时系统通常具有严格的端到端推理潜伏期需求。不幸的是，尽管大多数变压器计算都来自基质乘法，但变压器还包括几种非线性组件，它们在推理过程中倾向于成为瓶颈。在这项工作中，我们加快了张量流处理器上BERT模型的推断。通过小心地将所有非线性组件与矩阵乘法组件融合在一起，我们能够有效地利用芯片矩阵乘法单元，从而通过BERT-1通过BERT-1通过BERT-BASE，确定性的尾巴延迟为130 $ \ MU $ s，比当前的最新时间快6倍。

光电子因子（PEF）是区分不同类型的储层岩石的重要良好记录工具，因为PEF测量对高原子数的元素敏感。此外，可以通过将PEF日志与其他井对数结合来确定岩石矿物的比率。但是，在某些情况下，PEF日志可能会缺少，例如在旧的井木和井中钻孔的井。因此，在这种情况下，开发用于估计缺失PEF日志的模型至关重要。在这项工作中，我们开发了各种机器学习模型，以使用以下井日志作为输入来预测PEF值：散装密度（RHOB），中子孔隙率（NPHI），伽马射线（GR），压缩和剪切速度。使用自适应网络模糊推理系统（ANFI）和人工神经网络（ANN）模型的PEF值的预测分别在测试数据集中的误差分别为16％和14％的平均绝对百分比误差（AAPE）。因此，提出了一种基于自动化机器学习概念的不同方法。它通过自动搜索最佳模型类型并优化了正在研究的数据集的超参数来工作。该方法选择了高斯过程回归（GPR）模型以准确估计PEF值。开发的GPR模型将测试数据集中预测的PEF值的AAPE降低到约10％AAPE。通过使用GPR模型对测量中的潜在噪声进行建模，可以进一步降低到约2％。

深度神经网络（DNN）在近年来，包括自动驾驶感知任务，包括自主驾驶感知任务的令人印象深刻。另一方面，目前的深神经网络很容易被对抗性攻击所欺骗。此漏洞提高了重要的问题，特别是在安全关键型应用中。因此，攻击和捍卫DNN的研究已经获得了很多覆盖范围。在这项工作中，横跨距离估计，语义分割，运动检测和对象检测，对详细的对抗攻击应用于各种多任务视觉感知深网络。实验考虑了针对目标和未定位案件的白色和黑色盒子攻击，同时攻击任务并检查所有其他效果，除了检查应用简单防御方法的效果。我们通过比较和讨论实验结果，提出见解和未来工作来结束本文。攻击的可视化可在https://youtu.be/6aixn90Budy上获得。

The performance of the Deep Learning (DL) models depends on the quality of labels. In some areas, the involvement of human annotators may lead to noise in the data. When these corrupted labels are blindly regarded as the ground truth (GT), DL models suffer from performance deficiency. This paper presents a method that aims to learn a confident model in the presence of noisy labels. This is done in conjunction with estimating the uncertainty of multiple annotators. We robustly estimate the predictions given only the noisy labels by adding entropy or information-based regularizer to the classifier network. We conduct our experiments on a noisy version of MNIST, CIFAR-10, and FMNIST datasets. Our empirical results demonstrate the robustness of our method as it outperforms or performs comparably to other state-of-the-art (SOTA) methods. In addition, we evaluated the proposed method on the curated dataset, where the noise type and level of various annotators depend on the input image style. We show that our approach performs well and is adept at learning annotators' confusion. Moreover, we demonstrate how our model is more confident in predicting GT than other baselines. Finally, we assess our approach for segmentation problem and showcase its effectiveness with experiments.

Recent advances in upper limb prostheses have led to significant improvements in the number of movements provided by the robotic limb. However, the method for controlling multiple degrees of freedom via user-generated signals remains challenging. To address this issue, various machine learning controllers have been developed to better predict movement intent. As these controllers become more intelligent and take on more autonomy in the system, the traditional approach of representing the human-machine interface as a human controlling a tool becomes limiting. One possible approach to improve the understanding of these interfaces is to model them as collaborative, multi-agent systems through the lens of joint action. The field of joint action has been commonly applied to two human partners who are trying to work jointly together to achieve a task, such as singing or moving a table together, by effecting coordinated change in their shared environment. In this work, we compare different prosthesis controllers (proportional electromyography with sequential switching, pattern recognition, and adaptive switching) in terms of how they present the hallmarks of joint action. The results of the comparison lead to a new perspective for understanding how existing myoelectric systems relate to each other, along with recommendations for how to improve these systems by increasing the collaborative communication between each partner.

Nowadays, the current neural network models of dialogue generation(chatbots) show great promise for generating answers for chatty agents. But they are short-sighted in that they predict utterances one at a time while disregarding their impact on future outcomes. Modelling a dialogue's future direction is critical for generating coherent, interesting dialogues, a need that has led traditional NLP dialogue models that rely on reinforcement learning. In this article, we explain how to combine these objectives by using deep reinforcement learning to predict future rewards in chatbot dialogue. The model simulates conversations between two virtual agents, with policy gradient methods used to reward sequences that exhibit three useful conversational characteristics: the flow of informality, coherence, and simplicity of response (related to forward-looking function). We assess our model based on its diversity, length, and complexity with regard to humans. In dialogue simulation, evaluations demonstrated that the proposed model generates more interactive responses and encourages a more sustained successful conversation. This work commemorates a preliminary step toward developing a neural conversational model based on the long-term success of dialogues.

In this work, we introduce a hypergraph representation learning framework called Hypergraph Neural Networks (HNN) that jointly learns hyperedge embeddings along with a set of hyperedge-dependent embeddings for each node in the hypergraph. HNN derives multiple embeddings per node in the hypergraph where each embedding for a node is dependent on a specific hyperedge of that node. Notably, HNN is accurate, data-efficient, flexible with many interchangeable components, and useful for a wide range of hypergraph learning tasks. We evaluate the effectiveness of the HNN framework for hyperedge prediction and hypergraph node classification. We find that HNN achieves an overall mean gain of 7.72% and 11.37% across all baseline models and graphs for hyperedge prediction and hypergraph node classification, respectively.

A "heart attack" or myocardial infarction (MI), occurs when an artery supplying blood to the heart is abruptly occluded. The "gold standard" method for imaging MI is Cardiovascular Magnetic Resonance Imaging (MRI), with intravenously administered gadolinium-based contrast (late gadolinium enhancement). However, no "gold standard" fully automated method for the quantification of MI exists. In this work, we propose an end-to-end fully automatic system (MyI-Net) for the detection and quantification of MI in MRI images. This has the potential to reduce the uncertainty due to the technical variability across labs and inherent problems of the data and labels. Our system consists of four processing stages designed to maintain the flow of information across scales. First, features from raw MRI images are generated using feature extractors built on ResNet and MoblieNet architectures. This is followed by the Atrous Spatial Pyramid Pooling (ASPP) to produce spatial information at different scales to preserve more image context. High-level features from ASPP and initial low-level features are concatenated at the third stage and then passed to the fourth stage where spatial information is recovered via up-sampling to produce final image segmentation output into: i) background, ii) heart muscle, iii) blood and iv) scar areas. New models were compared with state-of-art models and manual quantification. Our models showed favorable performance in global segmentation and scar tissue detection relative to state-of-the-art work, including a four-fold better performance in matching scar pixels to contours produced by clinicians.

Increasing popularity of deep-learning-powered applications raises the issue of vulnerability of neural networks to adversarial attacks. In other words, hardly perceptible changes in input data lead to the output error in neural network hindering their utilization in applications that involve decisions with security risks. A number of previous works have already thoroughly evaluated the most commonly used configuration - Convolutional Neural Networks (CNNs) against different types of adversarial attacks. Moreover, recent works demonstrated transferability of the some adversarial examples across different neural network models. This paper studied robustness of the new emerging models such as SpinalNet-based neural networks and Compact Convolutional Transformers (CCT) on image classification problem of CIFAR-10 dataset. Each architecture was tested against four White-box attacks and three Black-box attacks. Unlike VGG and SpinalNet models, attention-based CCT configuration demonstrated large span between strong robustness and vulnerability to adversarial examples. Eventually, the study of transferability between VGG, VGG-inspired SpinalNet and pretrained CCT 7/3x1 models was conducted. It was shown that despite high effectiveness of the attack on the certain individual model, this does not guarantee the transferability to other models.