我们介绍了一种新的体系结构，用于无监督对象以中心的表示学习和多对象检测和分割，该架构使用翻译等级的注意机制来预测场景中存在的对象的坐标并将功能向量关联到每个对象。变压器编码器处理闭塞和冗余检测，卷积自动编码器负责背景重建。我们表明，这种体系结构在复杂的合成基准上大大优于最新技术。

即使在几十年的研究之后，动态场景背景重建和前景对象分割仍然被认为是由于诸如由空气湍流或移动树引起的照明变化，相机运动或背景噪声等各种挑战而被视为公开问题。我们在本文中提出了使用AutoEncoder将视频序列的背景模拟为低维歧管，并将由该AutoEncoder提供的重建背景与原始图像进行比较以计算前景/背景分割掩码。所提出的模型的主要新颖性是，AutoEncoder也接受了预测背景噪声，其允许为每个帧计算以执行背景/前景分割的像素相关阈值。虽然所提出的模型不使用任何时间或运动信息，但它超过了CDNET 2014和Lasiesta数据集的无监督背景减法的最先进的背景，并且对相机正在移动的视频有重大改进。

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.

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

语言模型既展示了定量的改进，又展示了新的定性功能，随着规模的增加。尽管它们具有潜在的变革性影响，但这些新能力的特征却很差。为了为未来的研究提供信息，为破坏性的新模型能力做准备，并改善社会有害的效果，至关重要的是，我们必须了解目前和近乎未来的能力和语言模型的局限性。为了应对这一挑战，我们介绍了超越模仿游戏基准（Big Bench）。 Big Bench目前由204个任务组成，由132家机构的442位作者贡献。任务主题是多样的，从语言学，儿童发展，数学，常识性推理，生物学，物理学，社会偏见，软件开发等等。 Big-Bench专注于被认为超出当前语言模型的功能的任务。我们评估了OpenAI的GPT型号，Google内部密集变压器体系结构和大型基础上的开关稀疏变压器的行为，跨越了数百万到数十亿个参数。此外，一个人类专家评估者团队执行了所有任务，以提供强大的基准。研究结果包括：模型性能和校准都随规模改善，但绝对的术语（以及与评估者的性能相比）；在模型类中的性能非常相似，尽管带有稀疏性。逐渐和预测的任务通常涉及大量知识或记忆成分，而在临界规模上表现出“突破性”行为的任务通常涉及多个步骤或组成部分或脆性指标；社交偏见通常会随着含糊不清的环境而随着规模而增加，但这可以通过提示来改善。

Denoising diffusions are state-of-the-art generative models which exhibit remarkable empirical performance and come with theoretical guarantees. The core idea of these models is to progressively transform the empirical data distribution into a simple Gaussian distribution by adding noise using a diffusion. We obtain new samples whose distribution is close to the data distribution by simulating a "denoising" diffusion approximating the time reversal of this "noising" diffusion. This denoising diffusion relies on approximations of the logarithmic derivatives of the noised data densities, known as scores, obtained using score matching. Such models can be easily extended to perform approximate posterior simulation in high-dimensional scenarios where one can only sample from the prior and simulate synthetic observations from the likelihood. These methods have been primarily developed for data on $\mathbb{R}^d$ while extensions to more general spaces have been developed on a case-by-case basis. We propose here a general framework which not only unifies and generalizes this approach to a wide class of spaces but also leads to an original extension of score matching. We illustrate the resulting class of denoising Markov models on various applications.

To apply federated learning to drug discovery we developed a novel platform in the context of European Innovative Medicines Initiative (IMI) project MELLODDY (grant n{\deg}831472), which was comprised of 10 pharmaceutical companies, academic research labs, large industrial companies and startups. The MELLODDY platform was the first industry-scale platform to enable the creation of a global federated model for drug discovery without sharing the confidential data sets of the individual partners. The federated model was trained on the platform by aggregating the gradients of all contributing partners in a cryptographic, secure way following each training iteration. The platform was deployed on an Amazon Web Services (AWS) multi-account architecture running Kubernetes clusters in private subnets. Organisationally, the roles of the different partners were codified as different rights and permissions on the platform and administrated in a decentralized way. The MELLODDY platform generated new scientific discoveries which are described in a companion paper.

事实证明，基于得分的生成建模（SGM）是对有限维空间建模密度的非常有效的方法。在这项工作中，我们建议将这种方法扩展到在功能空间上学习生成模型。为此，我们代表光谱空间中的功能数据，以将过程的随机部分与其时空部分解离。然后，我们使用有限尺寸SGM从其随机组件中采样了尺寸降低技术。我们证明了我们的方法对各种多模式数据集进行建模的有效性。

基于得分的生成模型在密度估计和生成建模任务上表现出最新的性能。这些模型通常假设数据几何形状是平坦的，但已开发出最近的扩展来合成生活在Riemannian歧管上的数据。现有的加速扩散模型采样方法通常不适用于Riemannian设置，基于Riemannian得分的方法尚未适应数据集插值的重要任务。为了克服这些问题，我们介绍了\ emph {riemannian扩散schr \“ odinger桥}。我们提出的方法概括了扩散的schr \“ \ cite {debortoli2021neurips}中引入的odinger桥，向非欧国性分数设置超出了Riemannian Score的模型，并扩展第一次逆转。我们验证我们提出的关于合成数据以及真实地球和气候数据的方法。

去核扩散模型最近已成为强大的生成模型类别。它们提供最新的结果，不仅用于无条件模拟，而且还提供了解决在各种反问题中产生的条件模拟问题时。这些模型的一个局限性在于它们在生成时间上是计算密集型的，因为它们需要长期模拟扩散过程。进行无条件的模拟时，Schr \“生成建模的Odinger桥式公式会导致理论上接地的算法缩短生成时间，这与其他提出的加速技术互补。我们将Schr \'Edinger桥式桥式扩展到条件模拟。我们在各种应用程序上演示了这种新颖的方法，包括图像超分辨率，状态空间模型的最佳过滤以及预训练的网络的完善。我们的代码可以在https://github.com/vdeborto/cdsb上找到。