Text generation has made significant advances in the last few years. Yet, evaluation metrics have lagged behind, as the most popular choices (e.g., BLEU and ROUGE) may correlate poorly with human judgments. We propose BLEURT, a learned evaluation metric based on BERT that can model human judgments with a few thousand possibly biased training examples. A key aspect of our approach is a novel pre-training scheme that uses millions of synthetic examples to help the model generalize. BLEURT provides state-ofthe-art results on the last three years of the WMT Metrics shared task and the WebNLG Competition dataset. In contrast to a vanilla BERT-based approach, it yields superior results even when the training data is scarce and out-of-distribution.

Physics-Informed Neural Networks (PINNs) have gained much attention in various fields of engineering thanks to their capability of incorporating physical laws into the models. PINNs integrate the physical constraints by minimizing the partial differential equations (PDEs) residuals on a set of collocation points. The distribution of these collocation points appears to have a huge impact on the performance of PINNs and the assessment of the sampling methods for these points is still an active topic. In this paper, we propose a Fixed-Budget Online Adaptive Mesh Learning (FBOAML) method, which decomposes the domain into sub-domains, for training collocation points based on local maxima and local minima of the PDEs residuals. The stopping criterion is based on a data set of reference, which leads to an adaptive number of iterations for each specific problem. The effectiveness of FBOAML is demonstrated in the context of non-parameterized and parameterized problems. The impact of the hyper-parameters in FBOAML is investigated in this work. The comparison with other adaptive sampling methods is also illustrated. The numerical results demonstrate important gains in terms of accuracy of PINNs with FBOAML over the classical PINNs with non-adaptive collocation points. We also apply FBOAML in a complex industrial application involving coupling between mechanical and thermal fields. We show that FBOAML is able to identify the high-gradient location and even give better prediction for some physical fields than the classical PINNs with collocation points taken on a pre-adapted finite element mesh.

Most camera lens systems are designed in isolation, separately from downstream computer vision methods. Recently, joint optimization approaches that design lenses alongside other components of the image acquisition and processing pipeline -- notably, downstream neural networks -- have achieved improved imaging quality or better performance on vision tasks. However, these existing methods optimize only a subset of lens parameters and cannot optimize glass materials given their categorical nature. In this work, we develop a differentiable spherical lens simulation model that accurately captures geometrical aberrations. We propose an optimization strategy to address the challenges of lens design -- notorious for non-convex loss function landscapes and many manufacturing constraints -- that are exacerbated in joint optimization tasks. Specifically, we introduce quantized continuous glass variables to facilitate the optimization and selection of glass materials in an end-to-end design context, and couple this with carefully designed constraints to support manufacturability. In automotive object detection, we show improved detection performance over existing designs even when simplifying designs to two- or three-element lenses, despite significantly degrading the image quality. Code and optical designs will be made publicly available.

Reduced order modeling methods are often used as a mean to reduce simulation costs in industrial applications. Despite their computational advantages, reduced order models (ROMs) often fail to accurately reproduce complex dynamics encountered in real life applications. To address this challenge, we leverage NeuralODEs to propose a novel ROM correction approach based on a time-continuous memory formulation. Finally, experimental results show that our proposed method provides a high level of accuracy while retaining the low computational costs inherent to reduced models.

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.

自适应多机构系统（AMAS）将机器学习问题转变为代理之间的本地合作问题。我们提出了Smapy，这是一种基于合奏的AMA用于移动性预测的实施，除合作规则外，还为其代理提供了机器学习模型。通过详细的方法，我们表明，如果将线性模型集成到合作多代理结构中，则可以在基准传输模式检测数据集上使用线性模型进行非线性分类。获得的结果表明，由于多代理方法，在非线性环境中线性模型的性能有了显着改善。

培训视频中人类姿势估计的最先进模型需要具有很难获得的注释的数据集。尽管最近已将变压器用于身体姿势序列建模，但相关方法依靠伪地真相来增强目前有限的培训数据可用于学习此类模型。在本文中，我们介绍了Posebert，Posebert是一个通过掩盖建模对3D运动捕获（MOCAP）数据进行全面训练的变压器模块。它是简单，通用和通用的，因为它可以插入任何基于图像的模型的顶部，以在基于视频的模型中使用时间信息。我们展示了Posebert的变体，不同的输入从3D骨骼关键点到全身或仅仅是手（Mano）的3D参数模型的旋转。由于Posebert培训是任务不可知论的，因此该模型可以应用于姿势细化，未来的姿势预测或运动完成等几个任务。我们的实验结果验证了在各种最新姿势估计方法之上添加Posebert始终提高其性能，而其低计算成本使我们能够在实时演示中使用它，以通过A的机器人手使机器人手通过摄像头。可以在https://github.com/naver/posebert上获得测试代码和型号。

深神经网络的指纹识别的最新进展检测了放置在黑盒相互作用方案中的模型实例。指纹协议使用的输入专门为要检查的每个精确模型而设计。尽管在这种情况下有效，但这仍然导致仅在模型的修改（例如重新训练，量化）之后缺乏保证。本文通过推广到模型家族及其变体的概念来应对提出的i）指纹识别方案，这些方案有抵御模型的重大修改，ii）ii）延伸指纹任务，包括指纹任务，包括一个人，不仅需要指纹指纹指纹指纹指纹。一个精确的模型（以前称为检测任务），但还可以确定哪个模型族在Black-Box（标识任务）。我们通过证明良性输入（例如未修改的图像）是两个任务的足够材料来实现这两个目标。我们利用信息理论方案来进行识别任务。我们为检测任务设计了一种贪婪的歧视算法。两种方法均在空前的1000多个网络中进行实验验证。

本申请论文对拓扑数据分析（TDA）的适用性进行了全面的实验评估，以进行湍流的定量比较。具体而言，我们的研究记录了流动肠的最大值（已建立的涡度指标）的持续图，用于180个集合成员的拓扑表示，这是由五个数值求解器的参数空间的粗略采样而产生的。我们记录了域专家报告的五个主要假设，描述了他们对不同求解器配置产生的流量变异性的期望。我们贡献了三种评估方案，以通过两种比较度量评估上述假设的验证：（i）科学成像（L2规范）中使用的标准距离和（ii）持久图之间的已建立拓扑距离（L2-Wasserstein Metricric ）。在输入集合上进行的广泛实验表明，由于其涡旋的配置，拓扑距离（II）报告彼此相近的流量相似，预计将与域专家相似。总体而言，我们的研究报告的见解带来了TDA代表和比较湍流的适用性的实验证据，从而使流体动态社区对未来工作的使用量提供了信心。此外，我们的流数据和评估协议为TDA社区提供了一个由应用程序批准的基准测试，用于评估和设计进一步的拓扑距离。

流体（VOF）方法的体积被广泛用于多相流仿真中，以跟踪和定位两个不混溶的流体之间的界面。VOF方法的主要瓶颈是界面重建步骤，由于其高计算成本和非结构化网格的精度较低。我们建议基于图神经网络（GNN）的机器学习增强的VOF方法，以加速通用非结构化网格上的接口重建。我们首先开发一种方法来基于在非结构化网格上离散的抛物面表面生成合成数据集。然后，我们训练基于GNN的模型并执行概括测试。我们的结果表明，在工业背景下，基于GNN的界面重建方法的效率。