基于全注意力的变压器体系结构的强大建模能力通常会导致过度拟合，并且 - 对于自然语言处理任务，导致自动回归变压器解码器中隐式学习的内部语言模型，使外部语言模型的集成变得复杂。在本文中，我们探索了放松的注意力，对注意力的重量进行了简单易于实现的平滑平滑，从编码器。其次，我们表明它自然支持外部语言模型的整合，因为它通过放松解码器中的交叉注意来抑制隐式学习的内部语言模型。我们证明了在几项任务中放松注意力的好处，并与最近的基准方法相结合，并明显改善。具体而言，我们超过了最大的最大公共唇部阅读LRS3基准的26.90％单词错误率的先前最新性能，单词错误率为26.31％，并且我们达到了最佳表现的BLEU分数37.67在IWSLT14（de $ \ rightarrow $ en）的机器翻译任务没有外部语言模型，几乎没有其他模型参数。代码和模型将公开可用。

尽管深度神经网络（DNNS）在环境感知任务上取得了令人印象深刻的表现，但它们对对抗性扰动的敏感性限制了它们在实际应用中的使用。在本文中，我们（i）提出了一种基于对复杂视觉任务的多任务感知（即深度估计和语义分割）的新型对抗扰动检测方案。具体而言，通过在输入图像的提取边缘，深度输出和分割输出之间的不一致之处检测到对抗性扰动。为了进一步提高这一技术，我们（ii）在所有三种方式之间发展了新颖的边缘一致性损失，从而提高了它们的初始一致性，从而支持我们的检测方案。我们通过采用各种已知攻击和图像噪声来验证检测方案的有效性。此外，我们（iii）开发了多任务对抗攻击，旨在欺骗这两个任务以及我们的检测方案。对城市景观和KITTI数据集的实验评估表明，在假设5％的假阳性率的假设下，最高100％的图像被正确检测为对抗性扰动，具体取决于扰动的强度。代码可在https://github.com/ifnspaml/advattackdet上找到。 https://youtu.be/kka6goywmh4的简短视频可提供定性结果。

自动驾驶车辆中的环境感知通常严重依赖于深度神经网络（DNN），这些神经网络受到域的转移，导致DNN部署期间的性能大大降低。通常，通过无监督的域适应（UDA）方法解决了此问题，同时在源和目标域数据集上训练了训练，甚至仅以离线方式对目标数据进行训练。在这项工作中，我们进一步将无源的UDA方法扩展到了连续的，因此可以在单一图像的基础上进行语义细分。因此，我们的方法仅需要供应商（在源域中训练）和电流（未标记的目标域）相机图像的预训练模型。我们的方法持续batchNorm适应（CBNA）使用目标域图像以无监督的方式修改了批准层中的源域统计信息，从而在推理过程中可以提高稳定的性能。因此，与现有作品相反，我们的方法可以应用于在部署期间不断地以单位图像改进DNN，而无需访问源数据，而无需算法延迟，并且几乎没有计算开销。我们在各种源/目标域设置中显示了我们方法在语义分割中的一致有效性。代码可在https://github.com/ifnspaml/cbna上找到。

可重新配置的智能表面（RIS）是未来无线通信系统的新兴技术。在这项工作中，我们考虑由RIS启用的下行链路空间多路复用，以获得加权和速率（WSR）最大化。在文献中，大多数解决方案使用交替的基于梯度的优化，具有中等性能，高复杂性和有限的可扩展性。我们建议应用完全卷积的网络（FCN）来解决这个问题，最初是为图像的语义分割而设计的。 RIS的矩形形状和具有相邻RIS天线的通道的空间相关性由于它们之间的短距离而鼓励我们将其应用于RIS配置。我们设计一组通道功能，包括通过RIS和Direct通道的级联通道。在基站（BS）中，可分离的最小均方平方误差（MMSE）预编码器用于预测，然后应用加权最小均方误差（WMMSE）预编码器以进行微调，这是不增强的，更复杂的，但实现更好的表现。评价结果表明，该解决方案具有更高的性能，允许比基线更快的评估。因此，它可以更好地缩放到大量的天线，推进RIS更接近实际部署的步骤。

最近，基于注意的编码器 - 解码器（AED）模型对多个任务的端到端自动语音识别（ASR）显示了高性能。在此类模型中解决了过度控制，本文介绍了轻松关注的概念，这是一种简单地逐渐注入对训练期间对编码器 - 解码器注意重量的统一分配，其易于用两行代码实现。我们调查轻松关注跨不同AED模型架构和两个突出的ASR任务，华尔街日志（WSJ）和LibRisPeech的影响。我们发现，在用外部语言模型解码时，随着宽松的注意力训练的变压器始终如一地始终如一地遵循标准基线模型。在WSJ中，我们为基于变压器的端到端语音识别设置了一个新的基准，以3.65％的单词错误率，最优于13.1％的相对状态，同时仅引入单个HyperParameter。

在本文中，我们在不依赖于任何源域表示的情况下向“无监督域适应（UDA）的任务”的任务提供了一个解决方案。以前的UDA用于语义细分的方法使用在源域和目标域中的模型的同时训练，或者它们依赖于附加网络，在适应期间将源域知识重放到模型。相比之下，我们介绍了我们的小说无监督的批量适应（UBNA）方法，它将给定的预先训练模型适应未经使用的策略域而不使用 - 超出现有模型参数 - 任何源域表示（既不是数据或者，也可以在在线设置或仅以几滴方式使用从目标域中的几个未标记的图像中应用的。具体地，我们使用指数衰减的动量因子部分地将归一化层统计数据调整到目标域，从而将统计数据与两个域混合。通过评估语义分割的标准UDA基准测试，我们认为这优于一个没有适应的模型以及仅使用目标域中的统计数据的基线方法。与标准UDA方法相比，我们在源域表示的性能和使用之间报告权衡。

Modeling lies at the core of both the financial and the insurance industry for a wide variety of tasks. The rise and development of machine learning and deep learning models have created many opportunities to improve our modeling toolbox. Breakthroughs in these fields often come with the requirement of large amounts of data. Such large datasets are often not publicly available in finance and insurance, mainly due to privacy and ethics concerns. This lack of data is currently one of the main hurdles in developing better models. One possible option to alleviating this issue is generative modeling. Generative models are capable of simulating fake but realistic-looking data, also referred to as synthetic data, that can be shared more freely. Generative Adversarial Networks (GANs) is such a model that increases our capacity to fit very high-dimensional distributions of data. While research on GANs is an active topic in fields like computer vision, they have found limited adoption within the human sciences, like economics and insurance. Reason for this is that in these fields, most questions are inherently about identification of causal effects, while to this day neural networks, which are at the center of the GAN framework, focus mostly on high-dimensional correlations. In this paper we study the causal preservation capabilities of GANs and whether the produced synthetic data can reliably be used to answer causal questions. This is done by performing causal analyses on the synthetic data, produced by a GAN, with increasingly more lenient assumptions. We consider the cross-sectional case, the time series case and the case with a complete structural model. It is shown that in the simple cross-sectional scenario where correlation equals causation the GAN preserves causality, but that challenges arise for more advanced analyses.

KL-regularized reinforcement learning from expert demonstrations has proved successful in improving the sample efficiency of deep reinforcement learning algorithms, allowing them to be applied to challenging physical real-world tasks. However, we show that KL-regularized reinforcement learning with behavioral reference policies derived from expert demonstrations can suffer from pathological training dynamics that can lead to slow, unstable, and suboptimal online learning. We show empirically that the pathology occurs for commonly chosen behavioral policy classes and demonstrate its impact on sample efficiency and online policy performance. Finally, we show that the pathology can be remedied by non-parametric behavioral reference policies and that this allows KL-regularized reinforcement learning to significantly outperform state-of-the-art approaches on a variety of challenging locomotion and dexterous hand manipulation tasks.

Scientists and philosophers have debated whether humans can trust advanced artificial intelligence (AI) agents to respect humanity's best interests. Yet what about the reverse? Will advanced AI agents trust humans? Gauging an AI agent's trust in humans is challenging because--absent costs for dishonesty--such agents might respond falsely about their trust in humans. Here we present a method for incentivizing machine decisions without altering an AI agent's underlying algorithms or goal orientation. In two separate experiments, we then employ this method in hundreds of trust games between an AI agent (a Large Language Model (LLM) from OpenAI) and a human experimenter (author TJ). In our first experiment, we find that the AI agent decides to trust humans at higher rates when facing actual incentives than when making hypothetical decisions. Our second experiment replicates and extends these findings by automating game play and by homogenizing question wording. We again observe higher rates of trust when the AI agent faces real incentives. Across both experiments, the AI agent's trust decisions appear unrelated to the magnitude of stakes. Furthermore, to address the possibility that the AI agent's trust decisions reflect a preference for uncertainty, the experiments include two conditions that present the AI agent with a non-social decision task that provides the opportunity to choose a certain or uncertain option; in those conditions, the AI agent consistently chooses the certain option. Our experiments suggest that one of the most advanced AI language models to date alters its social behavior in response to incentives and displays behavior consistent with trust toward a human interlocutor when incentivized.

The cooperation of a human pilot with an autonomous agent during flight control realizes parallel autonomy. A parallel-autonomous system acts as a guardian that significantly enhances the robustness and safety of flight operations in challenging circumstances. Here, we propose an air-guardian concept that facilitates cooperation between an artificial pilot agent and a parallel end-to-end neural control system. Our vision-based air-guardian system combines a causal continuous-depth neural network model with a cooperation layer to enable parallel autonomy between a pilot agent and a control system based on perceived differences in their attention profile. The attention profiles are obtained by computing the networks' saliency maps (feature importance) through the VisualBackProp algorithm. The guardian agent is trained via reinforcement learning in a fixed-wing aircraft simulated environment. When the attention profile of the pilot and guardian agents align, the pilot makes control decisions. If the attention map of the pilot and the guardian do not align, the air-guardian makes interventions and takes over the control of the aircraft. We show that our attention-based air-guardian system can balance the trade-off between its level of involvement in the flight and the pilot's expertise and attention. We demonstrate the effectivness of our methods in simulated flight scenarios with a fixed-wing aircraft and on a real drone platform.