捕获和归因于代码变更引起的生产中的性能回归很难；事先预测它们，甚至更努力。关于自动学习预测软件中性能回归的入门，本文介绍了我们在Meta研究和部署基于ML的回归预测管道时获得的经验。在本文中，我们报告了一项比较研究，其复杂性增加了四个ML模型，从（1）代码 - opaque，（2）单词袋，（3）基于转换的变压器到（4）基于定制变压器的模型，创造的超大通信器。我们的调查表明，性能预测问题的固有难度，其特征是良性对回归变化的不平衡。我们的结果还质疑了基于变压器的架构在性能预测中的一般适用性：基于基础的代码伯特方法的性能令人惊讶。我们高度定制的超大号架构最初实现了预测性能，这与简单的单词模型相当，并且仅在下游用例中优于它们。超级人员将其转移到应用程序的这种能力很少有学习示例提供了在Meta实践中部署它的机会：它可以作为预滤波器来解决不太可能引入回归的更改，从而缩小更改空间的变化空间搜索回归高达43％，比随机基线提高45倍。为了进一步洞悉超大号公园，我们通过一系列计算反事实解释进行了探索。这些突出显示了代码的哪些部分更改模型认为重要的，从而验证了学习的黑框模型。

机器学习（ML）模型在许多软件工程任务中起着越来越普遍的作用。然而，由于大多数模型现在由不透明的深度神经网络供电，因此开发人员可能很难理解为什么该模型的结论以及如何对模型的预测作用。这一问题的激励，本文探讨了源代码模型的反事实解释。这种反事实解释构成了模型“改变其思想”的源代码的最小变化。我们将反事实解释生成整合到真实世界中的源代码的模型。我们描述了影响能够找到现实和合理的反事工艺解释的能力，以及对模型用户的这种解释的有用性。在一系列实验中，我们研究了我们对三种不同模型的方法的功效，每个模型都是基于在源代码上运行的伯特式架构。

With the advent of Neural Style Transfer (NST), stylizing an image has become quite popular. A convenient way for extending stylization techniques to videos is by applying them on a per-frame basis. However, such per-frame application usually lacks temporal-consistency expressed by undesirable flickering artifacts. Most of the existing approaches for enforcing temporal-consistency suffers from one or more of the following drawbacks. They (1) are only suitable for a limited range of stylization techniques, (2) can only be applied in an offline fashion requiring the complete video as input, (3) cannot provide consistency for the task of stylization, or (4) do not provide interactive consistency-control. Note that existing consistent video-filtering approaches aim to completely remove flickering artifacts and thus do not respect any specific consistency-control aspect. For stylization tasks, however, consistency-control is an essential requirement where a certain amount of flickering can add to the artistic look and feel. Moreover, making this control interactive is paramount from a usability perspective. To achieve the above requirements, we propose an approach that can stylize video streams while providing interactive consistency-control. Apart from stylization, our approach also supports various other image processing filters. For achieving interactive performance, we develop a lite optical-flow network that operates at 80 Frames per second (FPS) on desktop systems with sufficient accuracy. We show that the final consistent video-output using our flow network is comparable to that being obtained using state-of-the-art optical-flow network. Further, we employ an adaptive combination of local and global consistent features and enable interactive selection between the two. By objective and subjective evaluation, we show that our method is superior to state-of-the-art approaches.

Meta Learning automates the search for learning algorithms. At the same time, it creates a dependency on human engineering on the meta-level, where meta learning algorithms need to be designed. In this paper, we investigate self-referential meta learning systems that modify themselves without the need for explicit meta optimization. We discuss the relationship of such systems to in-context and memory-based meta learning and show that self-referential neural networks require functionality to be reused in the form of parameter sharing. Finally, we propose fitness monotonic execution (FME), a simple approach to avoid explicit meta optimization. A neural network self-modifies to solve bandit and classic control tasks, improves its self-modifications, and learns how to learn, purely by assigning more computational resources to better performing solutions.

There are two important things in science: (A) Finding answers to given questions, and (B) Coming up with good questions. Our artificial scientists not only learn to answer given questions, but also continually invent new questions, by proposing hypotheses to be verified or falsified through potentially complex and time-consuming experiments, including thought experiments akin to those of mathematicians. While an artificial scientist expands its knowledge, it remains biased towards the simplest, least costly experiments that still have surprising outcomes, until they become boring. We present an empirical analysis of the automatic generation of interesting experiments. In the first setting, we investigate self-invented experiments in a reinforcement-providing environment and show that they lead to effective exploration. In the second setting, pure thought experiments are implemented as the weights of recurrent neural networks generated by a neural experiment generator. Initially interesting thought experiments may become boring over time.

Capturing large fields of view with only one camera is an important aspect in surveillance and automotive applications, but the wide-angle fisheye imagery thus obtained exhibits very special characteristics that may not be very well suited for typical image and video processing methods such as motion estimation. This paper introduces a motion estimation method that adapts to the typical radial characteristics of fisheye video sequences by making use of an equisolid re-projection after moving part of the motion vector search into the perspective domain via a corresponding back-projection. By combining this approach with conventional translational motion estimation and compensation, average gains in luminance PSNR of up to 1.14 dB are achieved for synthetic fish-eye sequences and up to 0.96 dB for real-world data. Maximum gains for selected frame pairs amount to 2.40 dB and 1.39 dB for synthetic and real-world data, respectively.

The extensive surviving corpus of the ancient scholar Plutarch of Chaeronea (ca. 45-120 CE) also contains several texts which, according to current scholarly opinion, did not originate with him and are therefore attributed to an anonymous author Pseudo-Plutarch. These include, in particular, the work Placita Philosophorum (Quotations and Opinions of the Ancient Philosophers), which is extremely important for the history of ancient philosophy. Little is known about the identity of that anonymous author and its relation to other authors from the same period. This paper presents a BERT language model for Ancient Greek. The model discovers previously unknown statistical properties relevant to these literary, philosophical, and historical problems and can shed new light on this authorship question. In particular, the Placita Philosophorum, together with one of the other Pseudo-Plutarch texts, shows similarities with the texts written by authors from an Alexandrian context (2nd/3rd century CE).

In this work we present a fast occupancy map building approach based on the VDB datastructure. Existing log-odds based occupancy mapping systems are often not able to keep up with the high point densities and framerates of modern sensors. Therefore, we suggest a highly optimized approach based on a modern datastructure coming from a computer graphic background. A multithreaded insertion scheme allows occupancy map building at unprecedented speed. Multiple optimizations allow for a customizable tradeoff between runtime and map quality. We first demonstrate the effectiveness of the approach quantitatively on a set of ablation studies and typical benchmark sets, before we practically demonstrate the system using a legged robot and a UAV.

Model-Based Reinforcement Learning (RL) is widely believed to have the potential to improve sample efficiency by allowing an agent to synthesize large amounts of imagined experience. Experience Replay (ER) can be considered a simple kind of model, which has proved extremely effective at improving the stability and efficiency of deep RL. In principle, a learned parametric model could improve on ER by generalizing from real experience to augment the dataset with additional plausible experience. However, owing to the many design choices involved in empirically successful algorithms, it can be very hard to establish where the benefits are actually coming from. Here, we provide theoretical and empirical insight into when, and how, we can expect data generated by a learned model to be useful. First, we provide a general theorem motivating how learning a model as an intermediate step can narrow down the set of possible value functions more than learning a value function directly from data using the Bellman equation. Second, we provide an illustrative example showing empirically how a similar effect occurs in a more concrete setting with neural network function approximation. Finally, we provide extensive experiments showing the benefit of model-based learning for online RL in environments with combinatorial complexity, but factored structure that allows a learned model to generalize. In these experiments, we take care to control for other factors in order to isolate, insofar as possible, the benefit of using experience generated by a learned model relative to ER alone.

在神经网络应用中，不足的培训样本是一个常见的问题。尽管数据增强方法至少需要最少数量的样本，但我们提出了一种基于新颖的，基于渲染的管道来合成带注释的数据集。我们的方法不会修改现有样本，而是合成全新样本。提出的基于渲染的管道能够在全自动过程中生成和注释合成和部分真实的图像和视频数据。此外，管道可以帮助获取真实数据。拟议的管道基于渲染过程。此过程生成综合数据。部分实现的数据使合成序列通过在采集过程中合并真实摄像机使综合序列更接近现实。在自动车牌识别的背景下，广泛的实验验证证明了拟议的数据生成管道的好处，尤其是对于具有有限的可用培训数据的机器学习方案。与仅在实际数据集中训练的OCR算法相比，该实验表明，角色错误率和错过率分别从73.74％和100％和14.11％和41.27％降低。这些改进是通过仅对合成数据训练算法来实现的。当另外合并真实数据时，错误率可以进一步降低。因此，角色错误率和遗漏率可以分别降低至11.90％和39.88％。在实验过程中使用的所有数据以及针对自动数据生成的拟议基于渲染的管道公开可用（URL将在出版时揭示）。