模型预测控制(MPC)是一种最先进的(SOTA)控制技术,需要迭代地解决硬约束优化问题。对于不确定的动态,基于分析模型的强大MPC施加了其他约束,从而增加了问题的硬度。当需要在较少的时间内需要更多计算时,问题会加剧性能至关重要的应用程序。过去已经提出了数据驱动的回归方法,例如神经网络,以近似系统动力学。但是,在没有符号分析先验的情况下,此类模型依赖于大量标记的数据。这会产生非平凡的培训间接开销。物理知识的神经网络(PINN)以合理的精度获得了近似的普通微分方程(ODE)的非线性系统的吸引力。在这项工作中,我们通过PINNS(RAMP-NET)提出了一个强大的自适应MPC框架,该框架使用了一种神经网络,部分从简单的ODE中训练,部分是由数据训练的。物理损失用于学习代表理想动态的简单odes。访问损失函数内部的分析功能是正常化的,为参数不确定性执行了可靠的行为。另一方面,定期数据丢失用于适应剩余的干扰(非参数不确定性),在数学建模过程中未被误解。实验是在模拟环境中进行的,以进行四轨的轨迹跟踪。与两种基于SOTA回归的MPC方法相比,我们报告了7.8%至43.2%和8.04%和8.04%至61.5%的跟踪误差的降低。
translated by 谷歌翻译
Recent advances in operator learning theory have improved our knowledge about learning maps between infinite dimensional spaces. However, for large-scale engineering problems such as concurrent multiscale simulation for mechanical properties, the training cost for the current operator learning methods is very high. The article presents a thorough analysis on the mathematical underpinnings of the operator learning paradigm and proposes a kernel learning method that maps between function spaces. We first provide a survey of modern kernel and operator learning theory, as well as discuss recent results and open problems. From there, the article presents an algorithm to how we can analytically approximate the piecewise constant functions on R for operator learning. This implies the potential feasibility of success of neural operators on clustered functions. Finally, a k-means clustered domain on the basis of a mechanistic response is considered and the Lippmann-Schwinger equation for micro-mechanical homogenization is solved. The article briefly discusses the mathematics of previous kernel learning methods and some preliminary results with those methods. The proposed kernel operator learning method uses graph kernel networks to come up with a mechanistic reduced order method for multiscale homogenization.
translated by 谷歌翻译
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.
translated by 谷歌翻译
This paper surveys some recent developments in measures of association related to a new coefficient of correlation introduced by the author. A straightforward extension of this coefficient to standard Borel spaces (which includes all Polish spaces), overlooked in the literature so far, is proposed at the end of the survey.
translated by 谷歌翻译
Aspect Based Sentiment Analysis is a dominant research area with potential applications in social media analytics, business, finance, and health. Prior works in this area are primarily based on supervised methods, with a few techniques using weak supervision limited to predicting a single aspect category per review sentence. In this paper, we present an extremely weakly supervised multi-label Aspect Category Sentiment Analysis framework which does not use any labelled data. We only rely on a single word per class as an initial indicative information. We further propose an automatic word selection technique to choose these seed categories and sentiment words. We explore unsupervised language model post-training to improve the overall performance, and propose a multi-label generator model to generate multiple aspect category-sentiment pairs per review sentence. Experiments conducted on four benchmark datasets showcase our method to outperform other weakly supervised baselines by a significant margin.
translated by 谷歌翻译
Graph neural networks (GNNs) find applications in various domains such as computational biology, natural language processing, and computer security. Owing to their popularity, there is an increasing need to explain GNN predictions since GNNs are black-box machine learning models. One way to address this is counterfactual reasoning where the objective is to change the GNN prediction by minimal changes in the input graph. Existing methods for counterfactual explanation of GNNs are limited to instance-specific local reasoning. This approach has two major limitations of not being able to offer global recourse policies and overloading human cognitive ability with too much information. In this work, we study the global explainability of GNNs through global counterfactual reasoning. Specifically, we want to find a small set of representative counterfactual graphs that explains all input graphs. Towards this goal, we propose GCFExplainer, a novel algorithm powered by vertex-reinforced random walks on an edit map of graphs with a greedy summary. Extensive experiments on real graph datasets show that the global explanation from GCFExplainer provides important high-level insights of the model behavior and achieves a 46.9% gain in recourse coverage and a 9.5% reduction in recourse cost compared to the state-of-the-art local counterfactual explainers.
translated by 谷歌翻译
这项研究旨在开发一种新型的路灯管理系统,该系统由电视电视(CCTV)摄像头安装的计算机视觉技术提供动力,该摄像头允许发光二极管(LED)路灯通过识别行人或车辆的存在,从而自动通过适当的亮度点亮。并在视频中通过语义图像细分在缺席的情况下对路灯进行了颠倒。
translated by 谷歌翻译
动态磁共振成像(MRI)是一种流行的医学成像技术,可生成组织和器官内部对比度材料流动的图像序列。但是,仅在少数可行性研究中证明了它在通过食道运动中的成像运动中的应用,并且相对尚未探索。在这项工作中,我们提出了一个称为力学的MRI(MRI-MEC)的计算框架,该计算框架增强了该能力,从而增加了动态MRI在诊断食管疾病中的适用性。菠萝汁用作动态MRI的吞咽对比材料,MRI图像序列被用作MRI-MECH的输入。 MRI-MECH将食道建模为柔性的一维管,弹性管壁遵循线性管定律。然后,通过一维质量和动量保护方程式,通过食道流动。这些方程是使用物理信息的神经网络(PINN)求解的。 PINN最大程度地减少了MRI测量和模型预测之间的差异,以确保始终遵循流体流量问题的物理。 MRI-Mech计算了食管转运期间的流体速度和压力,并通过计算壁刚度和主动弛豫来估计食道健康的机械健康。此外,MRI-Mech预测了在排空过程中有关下食管下括约肌的缺失信息,这证明了其适用于缺少数据或图像分辨率差的方案。除了基于食管机械健康的定量估计值来改善临床决策外,MRI-MECH还可以增强用于应用其他医学成像方式以增强其功能。
translated by 谷歌翻译
我们考虑了从相对较小的I.I.D.估算大因果多树的骨骼的问题。样本。这是由于确定因果结构的问题,当变量数量与样本量非常大,例如基因调节网络中的问题。我们给出了一种算法,该算法在此类设置中以高精度恢复了树。该算法在基本上没有分布或建模假设下起作用,而不是一些轻度的非分类条件。
translated by 谷歌翻译
联合学习(FL),其中多个机构在不共享数据的情况下协作训练机器学习模型正在变得流行。参与机构可能不会平等地做出贡献,有些贡献了更多的数据,一些更好的质量数据或一些更多样化的数据。为了公平地排名不同机构的贡献,沙普利价值(SV)已成为选择方法。精确的SV计算非常昂贵,尤其是在有数百个贡献者的情况下。现有的SV计算技术使用近似值。但是,在医疗保健中,贡献机构的数量可能不是巨大的规模,计算精确的SVS仍然很昂贵,但并非不可能。对于此类设置,我们提出了一种称为Safe的高效SV计算技术(用于使用Enembly的联合学习的Shapley值)。我们从经验上表明,安全计算接近精确SV的值,并且其性能优于当前SV近似值。这在医学成像环境中尤其重要,在医学成像环境中,整个机构之间的广泛异质性猖ramp,并且需要快速准确的数据评估来确定每个参与者在多机构协作学习中的贡献。
translated by 谷歌翻译