深度强化学习（DRL）是一种有前途的方法，可以通过与环境的互动来学习政策来解决复杂的控制任务。但是，对DRL政策的培训需要大量的培训经验，这使得直接了解物理系统的政策是不切实际的。 SIM到运行的方法可以利用模拟来验证DRL政策，然后将其部署在现实世界中。不幸的是，经过验证的政策的直接现实部署通常由于不同的动态（称为现实差距）而遭受性能恶化。最近的SIM到现实方法，例如域随机化和域的适应性，重点是改善预审预告剂的鲁棒性。然而，经过模拟训练的策略通常需要使用现实世界中的数据来调整以达到最佳性能，这是由于现实世界样本的高成本而具有挑战性的。这项工作提出了一个分布式的云边缘建筑，以实时培训现实世界中的DRL代理。在体系结构中，推理和训练被分配到边缘和云，将实时控制循环与计算昂贵的训练回路分开。为了克服现实差距，我们的体系结构利用了SIM到现实的转移策略，以继续在物理系统上训练模拟预言的代理。我们证明了其在物理倒置螺旋控制系统上的适用性，分析了关键参数。现实世界实验表明，我们的体系结构可以使验证的DRL代理能够始终如一，有效地看不见动态。
translated by 谷歌翻译
Evaluating and comparing text-to-image models is a challenging problem. Significant advances in the field have recently been made, piquing interest of various industrial sectors. As a consequence, a gold standard in the field should cover a variety of tasks and application contexts. In this paper a novel evaluation approach is experimented, on the basis of: (i) a curated data set, made by high-quality royalty-free image-text pairs, divided into ten categories; (ii) a quantitative metric, the CLIP-score, (iii) a human evaluation task to distinguish, for a given text, the real and the generated images. The proposed method has been applied to the most recent models, i.e., DALLE2, Latent Diffusion, Stable Diffusion, GLIDE and Craiyon. Early experimental results show that the accuracy of the human judgement is fully coherent with the CLIP-score. The dataset has been made available to the public.
translated by 谷歌翻译
大量的时间序列数据通常被组织成具有不同聚集水平的横截面结构。示例包括产品和地理组。与此类数据集相干决策和计划的必要条件是针对分散的系列的预测，可以准确地添加到汇总的系列预测中，这激发了新型层次结构预测算法的创建。机器学习社区对横截面层次预测系统的兴趣日益增长，我们正处于一个有利的时刻，以确保科学的努力基于声音基线。因此，我们提出了层次Forecast库，该库包含预处理的公开可用数据集，评估指标和一组编译的统计基线模型。我们基于Python的框架旨在弥合统计，计量经济学建模和机器学习预测研究之间的差距。代码和文档可在https://github.com/nixtla/hierarchicalforecast中找到。
translated by 谷歌翻译
神经预测的最新进展加速了大规模预测系统的性能。然而，长途预测仍然是一项非常艰巨的任务。困扰任务的两个常见挑战是预测的波动及其计算复杂性。我们介绍了N-HITS，该模型通过结合新的分层插值和多率数据采样技术来解决挑战。这些技术使提出的方法能够顺序组装其预测，并在分解输入信号并合成预测的同时强调不同频率和尺度的组件。我们证明，在平稳性的情况下，层次结构插值技术可以有效地近似于任意长的视野。此外，我们从长远的预测文献中进行了广泛的大规模数据集实验，证明了我们方法比最新方法的优势，在该方法中，N-HITS可提供比最新的16％的平均准确性提高。变压器体系结构在减少计算时间的同时（50次）。我们的代码可在https://bit.ly/3jlibp8上找到。
translated by 谷歌翻译
预测Twitter等社交媒体用户的地理位置已经发现了几种在健康监测，紧急监测，内容个性化和社会研究中的应用。在这项工作中，我们通过设计和评估基于加权多层的文献的新方法对该领域的研究有助于与最先进的深度学习技术相结合。探索的方法从类似的底层结构（扩展提及和/或跟随网络）出发，而是使用不同的信息处理策略，例如，通过转换和归纳算法 - RGCNS和GraphSage的信息扩散，以及节点嵌入node2vec +。然后，这些图形与注意机制结合到将用户的文本视图结合到模型中。我们评估每个方法的性能，并将它们与基线模型进行比较，在公开的推特 - 美国数据集中;我们还根据拉丁美洲的大型Twitter捕获，提供新的数据集。最后，我们的工作讨论了在不同标签定义和指标的背景下的方法中的比较的局限性和有效性。
translated by 谷歌翻译
放射线学使用定量医学成像特征来预测临床结果。目前，在新的临床应用中，必须通过启发式试验和纠正过程手动完成各种可用选项的最佳放射组方法。在这项研究中，我们提出了一个框架，以自动优化每个应用程序的放射线工作流程的构建。为此，我们将放射线学作为模块化工作流程，并为每个组件包含大量的常见算法。为了优化每个应用程序的工作流程，我们使用随机搜索和结合使用自动化机器学习。我们在十二个不同的临床应用中评估我们的方法，从而在曲线下导致以下区域：1）脂肪肉瘤（0.83）； 2）脱粘型纤维瘤病（0.82）; 3）原发性肝肿瘤（0.80）; 4）胃肠道肿瘤（0.77）； 5）结直肠肝转移（0.61）; 6）黑色素瘤转移（0.45）; 7）肝细胞癌（0.75）; 8）肠系膜纤维化（0.80）; 9）前列腺癌（0.72）； 10）神经胶质瘤（0.71）; 11）阿尔茨海默氏病（0.87）;和12）头颈癌（0.84）。我们表明，我们的框架具有比较人类专家的竞争性能，优于放射线基线，并且表现相似或优于贝叶斯优化和更高级的合奏方法。最后，我们的方法完全自动优化了放射线工作流的构建，从而简化了在新应用程序中对放射线生物标志物的搜索。为了促进可重复性和未来的研究，我们公开发布了六个数据集，框架的软件实施以及重现这项研究的代码。
translated by 谷歌翻译
While the capabilities of autonomous systems have been steadily improving in recent years, these systems still struggle to rapidly explore previously unknown environments without the aid of GPS-assisted navigation. The DARPA Subterranean (SubT) Challenge aimed to fast track the development of autonomous exploration systems by evaluating their performance in real-world underground search-and-rescue scenarios. Subterranean environments present a plethora of challenges for robotic systems, such as limited communications, complex topology, visually-degraded sensing, and harsh terrain. The presented solution enables long-term autonomy with minimal human supervision by combining a powerful and independent single-agent autonomy stack, with higher level mission management operating over a flexible mesh network. The autonomy suite deployed on quadruped and wheeled robots was fully independent, freeing the human supervision to loosely supervise the mission and make high-impact strategic decisions. We also discuss lessons learned from fielding our system at the SubT Final Event, relating to vehicle versatility, system adaptability, and re-configurable communications.
translated by 谷歌翻译
Attention mechanisms form a core component of several successful deep learning architectures, and are based on one key idea: ''The output depends only on a small (but unknown) segment of the input.'' In several practical applications like image captioning and language translation, this is mostly true. In trained models with an attention mechanism, the outputs of an intermediate module that encodes the segment of input responsible for the output is often used as a way to peek into the `reasoning` of the network. We make such a notion more precise for a variant of the classification problem that we term selective dependence classification (SDC) when used with attention model architectures. Under such a setting, we demonstrate various error modes where an attention model can be accurate but fail to be interpretable, and show that such models do occur as a result of training. We illustrate various situations that can accentuate and mitigate this behaviour. Finally, we use our objective definition of interpretability for SDC tasks to evaluate a few attention model learning algorithms designed to encourage sparsity and demonstrate that these algorithms help improve interpretability.
translated by 谷歌翻译
It is well known that conservative mechanical systems exhibit local oscillatory behaviours due to their elastic and gravitational potentials, which completely characterise these periodic motions together with the inertial properties of the system. The classification of these periodic behaviours and their geometric characterisation are in an on-going secular debate, which recently led to the so-called eigenmanifold theory. The eigenmanifold characterises nonlinear oscillations as a generalisation of linear eigenspaces. With the motivation of performing periodic tasks efficiently, we use tools coming from this theory to construct an optimization problem aimed at inducing desired closed-loop oscillations through a state feedback law. We solve the constructed optimization problem via gradient-descent methods involving neural networks. Extensive simulations show the validity of the approach.
translated by 谷歌翻译
Recent advances in deep learning have enabled us to address the curse of dimensionality (COD) by solving problems in higher dimensions. A subset of such approaches of addressing the COD has led us to solving high-dimensional PDEs. This has resulted in opening doors to solving a variety of real-world problems ranging from mathematical finance to stochastic control for industrial applications. Although feasible, these deep learning methods are still constrained by training time and memory. Tackling these shortcomings, Tensor Neural Networks (TNN) demonstrate that they can provide significant parameter savings while attaining the same accuracy as compared to the classical Dense Neural Network (DNN). In addition, we also show how TNN can be trained faster than DNN for the same accuracy. Besides TNN, we also introduce Tensor Network Initializer (TNN Init), a weight initialization scheme that leads to faster convergence with smaller variance for an equivalent parameter count as compared to a DNN. We benchmark TNN and TNN Init by applying them to solve the parabolic PDE associated with the Heston model, which is widely used in financial pricing theory.
translated by 谷歌翻译