最近的方法（例如材料gan）已使用无条件的gan来生成每像素材料图，或作为从输入照片重建材料之前的材料。这些模型可以生成各种随机材料外观，但没有任何将生成材料限制为特定类别或控制生成材料的粗体结构的机制，例如砖墙上的精确砖布局。此外，从单个输入照片中重建的材料通常具有伪像，并且通常不可易换，这限制了它们在实际内容创建管道中的使用。我们提出了Tilegen，这是一种针对SVBRDFS的生成模型，该模型特定于材料类别，始终可易换，并且在提供的输入结构模式上有条件。 Tilegen是Stylegan的变体，其架构经过修改以始终生成可易于的（周期性）材料图。除了标准的“样式”潜在代码外，Tilegen还可以选择拍摄条件图像，从而使用户直接控制材料的主要空间（和可选的颜色）功能。例如，在砖块中，用户可以指定砖布局和砖块，或者在皮革材料中，皱纹和褶皱的位置。我们的反渲染方法可以通过优化找到一种材料，从而感知到单个目标照片。这种重建也可以以用户提供的模式为条件。所得的材料是可拆卸的，可以大于目标图像，并且可以通过改变条件来编辑。

我们为通用纹理综合提供了一种新型的U-Inction Vision Transformer。我们利用注意力机制可以利用自然的远程依赖性，以使我们的方法合成各种纹理，同时在单个推论中保留其结构。我们提出了一个分层的沙漏骨架，该骨干骨架可参与全球结构，并在粗到粉的流中以不同的尺度进行补丁映射。通过跳过连接和卷积设计，以不同的尺度传播和融合信息，我们的分层U型体系结构将注意力从宏结构到微细节的特征统一，并在连续阶段逐步完善合成结果。我们的方法比以前在随机纹理和结构化纹理上的工作更强大2 $ \ times $综合，同时概括了不看到纹理而不会进行微调。消融研究证明了我们体系结构的每个组成部分的有效性。

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.

We propose a fully unsupervised method to detect bias in contextualized embeddings. The method leverages the assortative information latently encoded by social networks and combines orthogonality regularization, structured sparsity learning, and graph neural networks to find the embedding subspace capturing this information. As a concrete example, we focus on the phenomenon of ideological bias: we introduce the concept of an ideological subspace, show how it can be found by applying our method to online discussion forums, and present techniques to probe it. Our experiments suggest that the ideological subspace encodes abstract evaluative semantics and reflects changes in the political left-right spectrum during the presidency of Donald Trump.

Classifying forecasting methods as being either of a "machine learning" or "statistical" nature has become commonplace in parts of the forecasting literature and community, as exemplified by the M4 competition and the conclusion drawn by the organizers. We argue that this distinction does not stem from fundamental differences in the methods assigned to either class. Instead, this distinction is probably of a tribal nature, which limits the insights into the appropriateness and effectiveness of different forecasting methods. We provide alternative characteristics of forecasting methods which, in our view, allow to draw meaningful conclusions. Further, we discuss areas of forecasting which could benefit most from cross-pollination between the ML and the statistics communities.

Incorporating prior knowledge of physics laws and structural properties of dynamical systems into the design of deep learning architectures has proven to be a powerful technique for improving their computational efficiency and generalization capacity. Learning accurate models of robot dynamics is critical for safe and stable control. Autonomous mobile robots, including wheeled, aerial, and underwater vehicles, can be modeled as controlled Lagrangian or Hamiltonian rigid-body systems evolving on matrix Lie groups. In this paper, we introduce a new structure-preserving deep learning architecture, the Lie group Forced Variational Integrator Network (LieFVIN), capable of learning controlled Lagrangian or Hamiltonian dynamics on Lie groups, either from position-velocity or position-only data. By design, LieFVINs preserve both the Lie group structure on which the dynamics evolve and the symplectic structure underlying the Hamiltonian or Lagrangian systems of interest. The proposed architecture learns surrogate discrete-time flow maps instead of surrogate vector fields, which allows better and faster prediction without requiring the use of a numerical integrator, neural ODE, or adjoint techniques. Furthermore, the learnt discrete-time dynamics can be combined seamlessly with computationally scalable discrete-time (optimal) control strategies.

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.

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.

The heterogeneity of hardware and data is a well-known and studied problem in the community of Federated Learning (FL) as running under heterogeneous settings. Recently, custom-size client models trained with Knowledge Distillation (KD) has emerged as a viable strategy for tackling the heterogeneity challenge. However, previous efforts in this direction are aimed at client model tuning rather than their impact onto the knowledge aggregation of the global model. Despite performance of global models being the primary objective of FL systems, under heterogeneous settings client models have received more attention. Here, we provide more insights into how the chosen approach for training custom client models has an impact on the global model, which is essential for any FL application. We show the global model can fully leverage the strength of KD with heterogeneous data. Driven by empirical observations, we further propose a new approach that combines KD and Learning without Forgetting (LwoF) to produce improved personalised models. We bring heterogeneous FL on pair with the mighty FedAvg of homogeneous FL, in realistic deployment scenarios with dropping clients.

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。