The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

为了解决复杂环境中的任务，机器人需要从经验中学习。深度强化学习是一种常见的机器人学习方法，但需要大量的反复试验才能学习，从而限制了其在物理世界中的部署。结果，机器人学习的许多进步都取决于模拟器。另一方面，模拟器内部的学习无法捕获现实世界的复杂性，很容易模拟器不准确，并且由此产生的行为并不适应世界上的变化。 Dreamer算法最近通过在学习的世界模型中进行计划，表现出巨大的希望，可以从少量互动中学习，从而超过了视频游戏中的纯强化学习。学习一个世界模型来预测潜在行动的结果，使计划可以在想象中进行计划，从而减少了真实环境中所需的反复试验量。但是，尚不清楚梦想家是否可以促进更快地学习物理机器人。在本文中，我们将Dreamer应用于4个机器人，以直接在网上学习，直接在现实世界中，而无需模拟器。 Dreamer训练一个四倍的机器人，从头开始，站起来，站起来，仅在1小时内就没有重置。然后，我们推动机器人，发现Dreamer在10分钟内适应以承受扰动或迅速翻身并站起来。在两个不同的机器人臂上，Dreamer学会了直接从相机图像和稀疏的奖励中挑选和放置多个物体，从而接近人类的性能。在轮式机器人上，Dreamer学会了纯粹从相机图像导航到目标位置，从而自动解决有关机器人方向的歧义。在所有实验中使用相同的超参数，我们发现Dreamer能够在现实世界中在线学习，建立强大的基线。我们释放我们的基础架构，用于世界模型在机器人学习中的未来应用。

Compared to point estimates calculated by standard neural networks, Bayesian neural networks (BNN) provide probability distributions over the output predictions and model parameters, i.e., the weights. Training the weight distribution of a BNN, however, is more involved due to the intractability of the underlying Bayesian inference problem and thus, requires efficient approximations. In this paper, we propose a novel approach for BNN learning via closed-form Bayesian inference. For this purpose, the calculation of the predictive distribution of the output and the update of the weight distribution are treated as Bayesian filtering and smoothing problems, where the weights are modeled as Gaussian random variables. This allows closed-form expressions for training the network's parameters in a sequential/online fashion without gradient descent. We demonstrate our method on several UCI datasets and compare it to the state of the art.

对抗性实例的有趣现象引起了机器学习中的显着关注，对社区可能更令人惊讶的是存在普遍对抗扰动（UAPS），即欺骗目标DNN的单一扰动。随着对深层分类器的关注，本调查总结了最近普遍对抗攻击的进展，讨论了攻击和防御方的挑战，以及uap存在的原因。我们的目标是将此工作扩展为动态调查，该调查将定期更新其内容，以遵循关于在广泛的域中的UAP或通用攻击的新作品，例如图像，音频，视频，文本等。将讨论相关更新：https://bit.ly/2sbqlgg。我们欢迎未来的作者在该领域的作品，联系我们，包括您的新发现。

由于它可能对粮食安全，可持续性，资源利用效率，化学处理的降低以及人类努力和产量的优化，因此，自主机器人在农业中的应用正在越来越受欢迎。有了这一愿景，蓬勃发展的研究项目旨在开发一种适应性的机器人解决方案，用于精确耕作，该解决方案结合了小型自动无人驾驶飞机（UAV）（UAV）的空中调查能力以及由多功能无人驾驶的无人接地车（UGV）执行的针对性干预措施。本文概述了该项目中获得的科学和技术进步和结果。我们引入了多光谱感知算法以及空中和地面系统，用于监测农作物密度，杂草压力，作物氮营养状况，并准确地对杂草进行分类和定位。然后，我们介绍了针对我们在农业环境中机器人身份量身定制的导航和映射系统，以及用于协作映射的模块。我们最终介绍了我们在不同的现场条件和不同农作物中实施和测试的地面干预硬件，软件解决方案以及接口。我们描述了一个真正的用例，在该案例中，无人机与UGV合作以监视该领域并进行选择性喷涂而无需人工干预。

To understand the world, we humans constantly need to relate the present to the past, and put events in context. In this paper, we enable existing video models to do the same. We propose a long-term feature bank-supportive information extracted over the entire span of a video-to augment state-of-the-art video models that otherwise would only view short clips of 2-5 seconds. Our experiments demonstrate that augmenting 3D convolutional networks with a long-term feature bank yields state-of-the-art results on three challenging video datasets: AVA, EPIC-Kitchens, and Charades. Code is available online. 1 1 https://github.com/facebookresearch/ video-long-term-feature-banks Input clip (4 seconds) Target frame

Deep embeddings answer one simple question: How similar are two images? Learning these embeddings is the bedrock of verification, zero-shot learning, and visual search. The most prominent approaches optimize a deep convolutional network with a suitable loss function, such as contrastive loss or triplet loss. While a rich line of work focuses solely on the loss functions, we show in this paper that selecting training examples plays an equally important role. We propose distance weighted sampling, which selects more informative and stable examples than traditional approaches. In addition, we show that a simple margin based loss is sufficient to outperform all other loss functions. We evaluate our approach on the Stanford Online Products, CAR196, and the CUB200-2011 datasets for image retrieval and clustering, and on the LFW dataset for face verification. Our method achieves state-of-the-art performance on all of them.

The release of ChatGPT, a language model capable of generating text that appears human-like and authentic, has gained significant attention beyond the research community. We expect that the convincing performance of ChatGPT incentivizes users to apply it to a variety of downstream tasks, including prompting the model to simplify their own medical reports. To investigate this phenomenon, we conducted an exploratory case study. In a questionnaire, we asked 15 radiologists to assess the quality of radiology reports simplified by ChatGPT. Most radiologists agreed that the simplified reports were factually correct, complete, and not potentially harmful to the patient. Nevertheless, instances of incorrect statements, missed key medical findings, and potentially harmful passages were reported. While further studies are needed, the initial insights of this study indicate a great potential in using large language models like ChatGPT to improve patient-centered care in radiology and other medical domains.

We consider a semi-supervised $k$-clustering problem where information is available on whether pairs of objects are in the same or in different clusters. This information is either available with certainty or with a limited level of confidence. We introduce the PCCC algorithm, which iteratively assigns objects to clusters while accounting for the information provided on the pairs of objects. Our algorithm can include relationships as hard constraints that are guaranteed to be satisfied or as soft constraints that can be violated subject to a penalty. This flexibility distinguishes our algorithm from the state-of-the-art in which all pairwise constraints are either considered hard, or all are considered soft. Unlike existing algorithms, our algorithm scales to large-scale instances with up to 60,000 objects, 100 clusters, and millions of cannot-link constraints (which are the most challenging constraints to incorporate). We compare the PCCC algorithm with state-of-the-art approaches in an extensive computational study. Even though the PCCC algorithm is more general than the state-of-the-art approaches in its applicability, it outperforms the state-of-the-art approaches on instances with all hard constraints or all soft constraints both in terms of running time and various metrics of solution quality. The source code of the PCCC algorithm is publicly available on GitHub.

Linear partial differential equations (PDEs) are an important, widely applied class of mechanistic models, describing physical processes such as heat transfer, electromagnetism, and wave propagation. In practice, specialized numerical methods based on discretization are used to solve PDEs. They generally use an estimate of the unknown model parameters and, if available, physical measurements for initialization. Such solvers are often embedded into larger scientific models or analyses with a downstream application such that error quantification plays a key role. However, by entirely ignoring parameter and measurement uncertainty, classical PDE solvers may fail to produce consistent estimates of their inherent approximation error. In this work, we approach this problem in a principled fashion by interpreting solving linear PDEs as physics-informed Gaussian process (GP) regression. Our framework is based on a key generalization of a widely-applied theorem for conditioning GPs on a finite number of direct observations to observations made via an arbitrary bounded linear operator. Crucially, this probabilistic viewpoint allows to (1) quantify the inherent discretization error; (2) propagate uncertainty about the model parameters to the solution; and (3) condition on noisy measurements. Demonstrating the strength of this formulation, we prove that it strictly generalizes methods of weighted residuals, a central class of PDE solvers including collocation, finite volume, pseudospectral, and (generalized) Galerkin methods such as finite element and spectral methods. This class can thus be directly equipped with a structured error estimate and the capability to incorporate uncertain model parameters and observations. In summary, our results enable the seamless integration of mechanistic models as modular building blocks into probabilistic models.