从有限的资源中获得最大收益可以进步自然语言处理(NLP)研究和实践,同时保守资源。这些资源可能是数据,时间,存储或能源。NLP的最新工作从缩放率产生了有趣的结果。但是,仅使用比例来改善结果意味着资源消耗也会扩展。这种关系激发了对有效方法的研究,这些方法需要更少的资源才能获得相似的结果。这项调查涉及NLP效率的方法和发现,旨在指导该领域的新研究人员并激发新方法的发展。
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Reinforcement learning (RL) has shown great promise with algorithms learning in environments with large state and action spaces purely from scalar reward signals. A crucial challenge for current deep RL algorithms is that they require a tremendous amount of environment interactions for learning. This can be infeasible in situations where such interactions are expensive; such as in robotics. Offline RL algorithms try to address this issue by bootstrapping the learning process from existing logged data without needing to interact with the environment from the very beginning. While online RL algorithms are typically evaluated as a function of the number of environment interactions, there exists no single established protocol for evaluating offline RL methods.In this paper, we propose a sequential approach to evaluate offline RL algorithms as a function of the training set size and thus by their data efficiency. Sequential evaluation provides valuable insights into the data efficiency of the learning process and the robustness of algorithms to distribution changes in the dataset while also harmonizing the visualization of the offline and online learning phases. Our approach is generally applicable and easy to implement. We compare several existing offline RL algorithms using this approach and present insights from a variety of tasks and offline datasets.
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Despite the impact of psychiatric disorders on clinical health, early-stage diagnosis remains a challenge. Machine learning studies have shown that classifiers tend to be overly narrow in the diagnosis prediction task. The overlap between conditions leads to high heterogeneity among participants that is not adequately captured by classification models. To address this issue, normative approaches have surged as an alternative method. By using a generative model to learn the distribution of healthy brain data patterns, we can identify the presence of pathologies as deviations or outliers from the distribution learned by the model. In particular, deep generative models showed great results as normative models to identify neurological lesions in the brain. However, unlike most neurological lesions, psychiatric disorders present subtle changes widespread in several brain regions, making these alterations challenging to identify. In this work, we evaluate the performance of transformer-based normative models to detect subtle brain changes expressed in adolescents and young adults. We trained our model on 3D MRI scans of neurotypical individuals (N=1,765). Then, we obtained the likelihood of neurotypical controls and psychiatric patients with early-stage schizophrenia from an independent dataset (N=93) from the Human Connectome Project. Using the predicted likelihood of the scans as a proxy for a normative score, we obtained an AUROC of 0.82 when assessing the difference between controls and individuals with early-stage schizophrenia. Our approach surpassed recent normative methods based on brain age and Gaussian Process, showing the promising use of deep generative models to help in individualised analyses.
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In this paper, a hyperparameter tuning based Bayesian optimization of digital twins is carried out to diagnose various faults in grid connected inverters. As fault detection and diagnosis require very high precision, we channelize our efforts towards an online optimization of the digital twins, which, in turn, allows a flexible implementation with limited amount of data. As a result, the proposed framework not only becomes a practical solution for model versioning and deployment of digital twins design with limited data, but also allows integration of deep learning tools to improve the hyperparameter tuning capabilities. For classification performance assessment, we consider different fault cases in virtual synchronous generator (VSG) controlled grid-forming converters and demonstrate the efficacy of our approach. Our research outcomes reveal the increased accuracy and fidelity levels achieved by our digital twin design, overcoming the shortcomings of traditional hyperparameter tuning methods.
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Automated machine learning (AutoML) algorithms have grown in popularity due to their high performance and flexibility to adapt to different problems and data sets. With the increasing number of AutoML algorithms, deciding which would best suit a given problem becomes increasingly more work. Therefore, it is essential to use complex and challenging benchmarks which would be able to differentiate the AutoML algorithms from each other. This paper compares the performance of four different AutoML algorithms: Tree-based Pipeline Optimization Tool (TPOT), Auto-Sklearn, Auto-Sklearn 2, and H2O AutoML. We use the Diverse and Generative ML benchmark (DIGEN), a diverse set of synthetic datasets derived from generative functions designed to highlight the strengths and weaknesses of the performance of common machine learning algorithms. We confirm that AutoML can identify pipelines that perform well on all included datasets. Most AutoML algorithms performed similarly without much room for improvement; however, some were more consistent than others at finding high-performing solutions for some datasets.
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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.
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Simulation-based inference (SBI) solves statistical inverse problems by repeatedly running a stochastic simulator and inferring posterior distributions from model-simulations. To improve simulation efficiency, several inference methods take a sequential approach and iteratively adapt the proposal distributions from which model simulations are generated. However, many of these sequential methods are difficult to use in practice, both because the resulting optimisation problems can be challenging and efficient diagnostic tools are lacking. To overcome these issues, we present Truncated Sequential Neural Posterior Estimation (TSNPE). TSNPE performs sequential inference with truncated proposals, sidestepping the optimisation issues of alternative approaches. In addition, TSNPE allows to efficiently perform coverage tests that can scale to complex models with many parameters. We demonstrate that TSNPE performs on par with previous methods on established benchmark tasks. We then apply TSNPE to two challenging problems from neuroscience and show that TSNPE can successfully obtain the posterior distributions, whereas previous methods fail. Overall, our results demonstrate that TSNPE is an efficient, accurate, and robust inference method that can scale to challenging scientific models.
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人类机器人相互作用(HRI)对于在日常生活中广泛使用机器人至关重要。机器人最终将能够通过有效的社会互动来履行人类文明的各种职责。创建直接且易于理解的界面,以与机器人开始在个人工作区中扩散时与机器人互动至关重要。通常,与模拟机器人的交互显示在屏幕上。虚拟现实(VR)是一个更具吸引力的替代方法,它为视觉提示提供了更像现实世界中看到的线索。在这项研究中,我们介绍了Jubileo,这是一种机器人的动画面孔,并使用人类机器人社会互动领域的各种研究和应用开发工具。Jubileo Project不仅提供功能齐全的开源物理机器人。它还提供了一个全面的框架,可以通过VR接口进行操作,从而为HRI应用程序测试带来沉浸式环境,并明显更好地部署速度。
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及时,准确地检测功率电子中的异常,对于维持复杂的生产系统而变得越来越重要。强大而可解释的策略有助于减少系统的停机时间,并抢占或减轻基础设施网络攻击。这项工作从解释当前数据集和机器学习算法输出中存在的不确定性类型开始。然后引入和分析三种打击这些不确定性的技术。我们进一步介绍了两种异常检测和分类方法,即矩阵曲线算法和异常变压器,它们是在电源电子转换器数据集的背景下应用的。具体而言,矩阵配置文件算法被证明非常适合作为检测流时间序列数据中实时异常的概括方法。迭代矩阵配置文件的结构python库实现用于创建检测器。创建了一系列自定义过滤器并将其添加到检测器中,以调整其灵敏度,回忆和检测精度。我们的数值结果表明,通过简单的参数调整,检测器在各种故障场景中提供了高精度和性能。
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深度神经网络在医学图像分析中带来了显着突破。但是,由于其渴望数据的性质,医学成像项目中适度的数据集大小可能会阻碍其全部潜力。生成合成数据提供了一种有希望的替代方案,可以补充培训数据集并进行更大范围的医学图像研究。最近,扩散模型通过产生逼真的合成图像引起了计算机视觉社区的注意。在这项研究中,我们使用潜在扩散模型探索从高分辨率3D脑图像中生成合成图像。我们使用来自英国生物银行数据集的T1W MRI图像(n = 31,740)来训练我们的模型,以了解脑图像的概率分布,该脑图像以协变量为基础,例如年龄,性别和大脑结构量。我们发现我们的模型创建了现实的数据,并且可以使用条件变量有效地控制数据生成。除此之外,我们创建了一个带有100,000次脑图像的合成数据集,并使科学界公开使用。
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