几乎没有射击的细分是一项具有挑战性的密集预测任务，它需要分割新的查询图像，仅给出一个小注释的支持集。因此，关键问题是设计一种方法，该方法可以从支持集中汇总详细信息，同时对外观和上下文的巨大变化进行稳健。为此，我们提出了基于密集的高斯过程（GP）回归的几种分割方法。鉴于支持集，我们密集的GP了解了从局部深层图像特征到掩模值的映射，能够捕获复杂的外观分布。此外，它提供了一种捕获不确定性的原则方法，这是CNN解码器获得的最终分割的另一种强大提示。我们进一步利用了我们方法的端到端学习能力，以学习GP的高维输出空间。我们的方法在Pascal-5 $^i $和Coco-20 $^i $ Benchmarks上设定了新的最新技术，在Coco-20 $^i $中获得了绝对的$+8.4 $ MIOU的绝对增益5杆设置。此外，在增加支撑设置大小时，我们的方法的分割质量会优雅地缩放，同时实现强大的跨数据库转移。代码和训练有素的模型可在\ url {https://github.com/joakimjohnander/dgpnet}上获得。

在视频中自动识别有害内容是一项重要的任务，具有广泛的应用程序。但是，缺乏可用的专业标签开放数据集。在这项工作中，介绍了由专业人士注释的电影预告片的3589个视频片段的开放数据集。对数据集进行了分析，从而揭示了剪辑和拖车级别注释之间的关系。视听模型在数据集上进行了培训，并对进行的建模选择进行了深入研究。结果表明，通过结合视觉和音频方式，大规模视频识别数据集的预训练以及类平衡采样来大大提高性能。最后，使用歧视探测研究了受过训练的模型的偏差。Vidharm公开可用，并提供更多详细信息，请访问：https：//vidharm.github.io。

Data-driven models such as neural networks are being applied more and more to safety-critical applications, such as the modeling and control of cyber-physical systems. Despite the flexibility of the approach, there are still concerns about the safety of these models in this context, as well as the need for large amounts of potentially expensive data. In particular, when long-term predictions are needed or frequent measurements are not available, the open-loop stability of the model becomes important. However, it is difficult to make such guarantees for complex black-box models such as neural networks, and prior work has shown that model stability is indeed an issue. In this work, we consider an aluminum extraction process where measurements of the internal state of the reactor are time-consuming and expensive. We model the process using neural networks and investigate the role of including skip connections in the network architecture as well as using l1 regularization to induce sparse connection weights. We demonstrate that these measures can greatly improve both the accuracy and the stability of the models for datasets of varying sizes.

We test the performance of GAN models for lip-synchronization. For this, we reimplement LipGAN in Pytorch, train it on the dataset GRID and compare it to our own variation, L1WGAN-GP, adapted to the LipGAN architecture and also trained on GRID.

High content imaging assays can capture rich phenotypic response data for large sets of compound treatments, aiding in the characterization and discovery of novel drugs. However, extracting representative features from high content images that can capture subtle nuances in phenotypes remains challenging. The lack of high-quality labels makes it difficult to achieve satisfactory results with supervised deep learning. Self-Supervised learning methods, which learn from automatically generated labels has shown great success on natural images, offer an attractive alternative also to microscopy images. However, we find that self-supervised learning techniques underperform on high content imaging assays. One challenge is the undesirable domain shifts present in the data known as batch effects, which may be caused by biological noise or uncontrolled experimental conditions. To this end, we introduce Cross-Domain Consistency Learning (CDCL), a novel approach that is able to learn in the presence of batch effects. CDCL enforces the learning of biological similarities while disregarding undesirable batch-specific signals, which leads to more useful and versatile representations. These features are organised according to their morphological changes and are more useful for downstream tasks - such as distinguishing treatments and mode of action.

Objective: Imbalances of the electrolyte concentration levels in the body can lead to catastrophic consequences, but accurate and accessible measurements could improve patient outcomes. While blood tests provide accurate measurements, they are invasive and the laboratory analysis can be slow or inaccessible. In contrast, an electrocardiogram (ECG) is a widely adopted tool which is quick and simple to acquire. However, the problem of estimating continuous electrolyte concentrations directly from ECGs is not well-studied. We therefore investigate if regression methods can be used for accurate ECG-based prediction of electrolyte concentrations. Methods: We explore the use of deep neural networks (DNNs) for this task. We analyze the regression performance across four electrolytes, utilizing a novel dataset containing over 290000 ECGs. For improved understanding, we also study the full spectrum from continuous predictions to binary classification of extreme concentration levels. To enhance clinical usefulness, we finally extend to a probabilistic regression approach and evaluate different uncertainty estimates. Results: We find that the performance varies significantly between different electrolytes, which is clinically justified in the interplay of electrolytes and their manifestation in the ECG. We also compare the regression accuracy with that of traditional machine learning models, demonstrating superior performance of DNNs. Conclusion: Discretization can lead to good classification performance, but does not help solve the original problem of predicting continuous concentration levels. While probabilistic regression demonstrates potential practical usefulness, the uncertainty estimates are not particularly well-calibrated. Significance: Our study is a first step towards accurate and reliable ECG-based prediction of electrolyte concentration levels.

As spatial audio is enjoying a surge in popularity, data-driven machine learning techniques that have been proven successful in other domains are increasingly used to process head-related transfer function measurements. However, these techniques require much data, whereas the existing datasets are ranging from tens to the low hundreds of datapoints. It therefore becomes attractive to combine multiple of these datasets, although they are measured under different conditions. In this paper, we first establish the common ground between a number of datasets, then we investigate potential pitfalls of mixing datasets. We perform a simple experiment to test the relevance of the remaining differences between datasets when applying machine learning techniques. Finally, we pinpoint the most relevant differences.

This paper presents an evaluation of the quality of automatically generated reading comprehension questions from Swedish text, using the Quinductor method. This method is a light-weight, data-driven but non-neural method for automatic question generation (QG). The evaluation shows that Quinductor is a viable QG method that can provide a strong baseline for neural-network-based QG methods.

Using 3D CNNs on high resolution medical volumes is very computationally demanding, especially for large datasets like the UK Biobank which aims to scan 100,000 subjects. Here we demonstrate that using 2D CNNs on a few 2D projections (representing mean and standard deviation across axial, sagittal and coronal slices) of the 3D volumes leads to reasonable test accuracy when predicting the age from brain volumes. Using our approach, one training epoch with 20,324 subjects takes 40 - 70 seconds using a single GPU, which is almost 100 times faster compared to a small 3D CNN. These results are important for researchers who do not have access to expensive GPU hardware for 3D CNNs.

未知的非线性动力学通常会限制前馈控制的跟踪性能。本文的目的是开发一个可以使用通用函数近似器来补偿这些未知非线性动力学的前馈控制框架。前馈控制器被参数化为基于物理模型和神经网络的平行组合，在该组合中，两者都共享相同的线性自回旋（AR）动力学。该参数化允许通过Sanathanan-Koerner（SK）迭代进行有效的输出误差优化。在每个Sk-itteration中，神经网络的输出在基于物理模型的子空间中通过基于正交投影的正则化受到惩罚，从而使神经网络仅捕获未建模的动力学，从而产生可解释的模型。