我们建议使用以光源方向为条件的神经辐射场（NERF）的扩展来解决多视光度立体声问题。我们神经表示的几何部分预测表面正常方向，使我们能够理解局部表面反射率。我们的神经表示的外观部分被分解为神经双向反射率函数（BRDF），作为拟合过程的一部分学习，阴影预测网络（以光源方向为条件），使我们能够对明显的BRDF进行建模。基于物理图像形成模型的诱导偏差的学到的组件平衡使我们能够远离训练期间观察到的光源和查看器方向。我们证明了我们在多视光学立体基准基准上的方法，并表明可以通过NERF的神经密度表示可以获得竞争性能。

Industry 4.0 aims to optimize the manufacturing environment by leveraging new technological advances, such as new sensing capabilities and artificial intelligence. The DRAEM technique has shown state-of-the-art performance for unsupervised classification. The ability to create anomaly maps highlighting areas where defects probably lie can be leveraged to provide cues to supervised classification models and enhance their performance. Our research shows that the best performance is achieved when training a defect detection model by providing an image and the corresponding anomaly map as input. Furthermore, such a setting provides consistent performance when framing the defect detection as a binary or multiclass classification problem and is not affected by class balancing policies. We performed the experiments on three datasets with real-world data provided by Philips Consumer Lifestyle BV.

Quality control is a crucial activity performed by manufacturing companies to ensure their products conform to the requirements and specifications. The introduction of artificial intelligence models enables to automate the visual quality inspection, speeding up the inspection process and ensuring all products are evaluated under the same criteria. In this research, we compare supervised and unsupervised defect detection techniques and explore data augmentation techniques to mitigate the data imbalance in the context of automated visual inspection. Furthermore, we use Generative Adversarial Networks for data augmentation to enhance the classifiers' discriminative performance. Our results show that state-of-the-art unsupervised defect detection does not match the performance of supervised models but can be used to reduce the labeling workload by more than 50%. Furthermore, the best classification performance was achieved considering GAN-based data generation with AUC ROC scores equal to or higher than 0,9898, even when increasing the dataset imbalance by leaving only 25\% of the images denoting defective products. We performed the research with real-world data provided by Philips Consumer Lifestyle BV.

We address the problem of integrating data from multiple observational and interventional studies to eventually compute counterfactuals in structural causal models. We derive a likelihood characterisation for the overall data that leads us to extend a previous EM-based algorithm from the case of a single study to that of multiple ones. The new algorithm learns to approximate the (unidentifiability) region of model parameters from such mixed data sources. On this basis, it delivers interval approximations to counterfactual results, which collapse to points in the identifiable case. The algorithm is very general, it works on semi-Markovian models with discrete variables and can compute any counterfactual. Moreover, it automatically determines if a problem is feasible (the parameter region being nonempty), which is a necessary step not to yield incorrect results. Systematic numerical experiments show the effectiveness and accuracy of the algorithm, while hinting at the benefits of integrating heterogeneous data to get informative bounds in case of unidentifiability.

Self-trained autonomous agents developed using machine learning are showing great promise in a variety of control settings, perhaps most remarkably in applications involving autonomous vehicles. The main challenge associated with self-learned agents in the form of deep neural networks, is their black-box nature: it is impossible for humans to interpret deep neural networks. Therefore, humans cannot directly interpret the actions of deep neural network based agents, or foresee their robustness in different scenarios. In this work, we demonstrate a method for probing which concepts self-learning agents internalise in the course of their training. For demonstration, we use a chess playing agent in a fast and light environment developed specifically to be suitable for research groups without access to enormous computational resources or machine learning models.

We develop Bayesian neural networks (BNNs) that permit to model generic nonlinearities and time variation for (possibly large sets of) macroeconomic and financial variables. From a methodological point of view, we allow for a general specification of networks that can be applied to either dense or sparse datasets, and combines various activation functions, a possibly very large number of neurons, and stochastic volatility (SV) for the error term. From a computational point of view, we develop fast and efficient estimation algorithms for the general BNNs we introduce. From an empirical point of view, we show both with simulated data and with a set of common macro and financial applications that our BNNs can be of practical use, particularly so for observations in the tails of the cross-sectional or time series distributions of the target variables.

We propose a) a Language Agnostic end-to-end Speech Translation model (LAST), and b) a data augmentation strategy to increase code-switching (CS) performance. With increasing globalization, multiple languages are increasingly used interchangeably during fluent speech. Such CS complicates traditional speech recognition and translation, as we must recognize which language was spoken first and then apply a language-dependent recognizer and subsequent translation component to generate the desired target language output. Such a pipeline introduces latency and errors. In this paper, we eliminate the need for that, by treating speech recognition and translation as one unified end-to-end speech translation problem. By training LAST with both input languages, we decode speech into one target language, regardless of the input language. LAST delivers comparable recognition and speech translation accuracy in monolingual usage, while reducing latency and error rate considerably when CS is observed.

检测障碍对于安全有效的自动驾驶至关重要。为此，我们提出了NVRadarnet，这是一种深神经网络（DNN），它使用汽车雷达传感器检测动态障碍物和可驱动的自由空间。该网络利用从多个雷达传感器的时间积累的数据来检测动态障碍，并在自上而下的鸟类视图（BEV）中计算其方向。该网络还可以回归可驱动的自由空间，以检测未分类的障碍。我们的DNN是第一个使用稀疏雷达信号的同类DNN，以实时从雷达数据实时执行障碍物和自由空间检测。在实际的自动驾驶场景中，该网络已成功地用于我们的自动驾驶汽车。该网络在嵌入式GPU上的运行速度快于实时时间，并且在地理区域显示出良好的概括。

最近的神经监督主题细分模型具有优于无监督方法的杰出有效性，并从Wikipedia采样了大规模培训语料库。但是，这些模型可能会因利用简单的语言线索进行预测而引起的鲁棒性和可传递性有限，但忽略了更重要的索引间局部一致性。为了解决这个问题，我们提出了一种语言意识到的神经主题细分模型，并注入了句子上的话语依赖性结构，以鼓励模型使主题边界预测更多地基于句子之间的局部一致性。我们对英语评估数据集的实证研究表明，通过我们提出的策略将上述句子话语结构注入神经主题分段者可以实质上改善其在域内和外域数据上的性能，而模型的复杂性很小。

基于模型的递归分区（MOB）是一种半参数统计方法，允许鉴定可以与广泛的结果度量结合的亚组，包括连续的时间赛车结果。当以离散量表测量时间时，方法和模型需要考虑这种差异，因为其他亚组可能是虚假的，并且效果偏见。 M-Fluctuation检验的BOB分裂标准的基础测试假定独立观察。但是，对于拟合离散的事件模型，必须对数据矩阵进行修改，从而导致增强数据矩阵违反独立性假设。我们提出了用于离散生存数据（MOB-DS）的MOB，该数据控制用于数据拆分的测试的I型错误率，因此，尽管存在不存在。 MOB-DS使用置换方法来说明增强的事件时间数据中的依赖项，以获取存在无子组的零假设下的分布。通过模拟，我们研究了新的MOB-DS的I型错误率以及不同生存曲线和事件速率的不同模式的标准BOB。我们发现，测试的I型错误率对MOB-DS得到了很好的控制，但是观察到BOB的错误率有了相当大的膨胀。为了说明所提出的方法，将MOB-DS应用于失业时间的数据。