我们在最常用的计算机视觉，自然语言和音频数据集中的10个测试集中识别标签错误，随后研究这些标签错误的可能性影响基准结果。测试集中的错误是众多和广泛的：我们估计10个数据集的至少3.3％的误差，例如标签错误包括至少6％的想象验证集。使用自信的学习算法识别推定的标签错误，然后通过众包（51％的算法上标记的候选者的51％确实错误地标记了数据集）。传统上，机器学习从业者选择基于测试准确性部署哪种模型 - 我们的调查结果在此提出谨慎行事，提出在正确标记的测试集上判断模型可能更有用，特别是对于嘈杂的现实世界数据集。令人惊讶的是，我们发现较低的容量模型可能与现实世界数据集中的更高容量模型几乎更有用，具有高比例的错误标记数据。例如，在具有校正标签的ImageNet上：Reset-18优于Reset-50，如果最初错误标记的测试示例的普及仅增加6％。在具有校正标签的CiFar-10上：VGG-11优于VGG-19，如果最初错误标记的测试示例的患病率达到5％。在HTTPS://labelerrors.com上查看10个数据集中的测试集错误，HTTPS：//github.com/cleanlab/labelors可以再现所有标签错误。

学习存在于数据的背景下，但信心的概念通常集中在模型预测上，而不是标签质量上。自信学习（CL）是一种替代方法，它通过根据修剪嘈杂数据的原理来表征和识别数据集中的标签错误来重点关注标签质量，并使用概率阈值来估算噪声，并将示例排名以自信。尽管许多研究已经独立开发了这些原理，但在这里，我们将它们结合起来，建立在类似的噪声过程的基础上，以直接估计嘈杂（给定的）标签和未腐败（未知）标签之间的关节分布。这导致了广义的CL，该CL证明是一致且具有实验性能的。我们提供了足够的条件，CL准确地发现标签错误，并且CL性能超过了CIFAR数据集上使用嘈杂标签的七种近期学习方法。独特的是，CL框架不与特定的数据模式或模型耦合（例如，我们使用CL在假定的无错误MNIST数据集中查找几个标签错误，并在亚马逊评论中对文本数据进行改善的情感分类）。我们还使用Imagenet上的CL来量化本体论类重叠（例如，估计645个“导弹”图像被错误标记为其母体类“弹丸”），并通过清洁训练前清洁数据来提高模型准确性（例如，用于RESNET）。使用开源清洁行释放可以复制这些结果。

Transfer learning increasingly becomes an important tool in handling data scarcity often encountered in machine learning. In the application of high-throughput thickness as a downstream process of the high-throughput optimization of optoelectronic thin films with autonomous workflows, data scarcity occurs especially for new materials. To achieve high-throughput thickness characterization, we propose a machine learning model called thicknessML that predicts thickness from UV-Vis spectrophotometry input and an overarching transfer learning workflow. We demonstrate the transfer learning workflow from generic source domain of generic band-gapped materials to specific target domain of perovskite materials, where the target domain data only come from limited number (18) of refractive indices from literature. The target domain can be easily extended to other material classes with a few literature data. Defining thickness prediction accuracy to be within-10% deviation, thicknessML achieves 92.2% (with a deviation of 3.6%) accuracy with transfer learning compared to 81.8% (with a deviation of 3.6%) 11.7% without (lower mean and larger standard deviation). Experimental validation on six deposited perovskite films also corroborates the efficacy of the proposed workflow by yielding a 10.5% mean absolute percentage error (MAPE).

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.

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.

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.

Artificial neural networks can learn complex, salient data features to achieve a given task. On the opposite end of the spectrum, mathematically grounded methods such as topological data analysis allow users to design analysis pipelines fully aware of data constraints and symmetries. We introduce a class of persistence-based neural network layers. Persistence-based layers allow the users to easily inject knowledge about symmetries (equivariance) respected by the data, are equipped with learnable weights, and can be composed with state-of-the-art neural architectures.

KL-regularized reinforcement learning from expert demonstrations has proved successful in improving the sample efficiency of deep reinforcement learning algorithms, allowing them to be applied to challenging physical real-world tasks. However, we show that KL-regularized reinforcement learning with behavioral reference policies derived from expert demonstrations can suffer from pathological training dynamics that can lead to slow, unstable, and suboptimal online learning. We show empirically that the pathology occurs for commonly chosen behavioral policy classes and demonstrate its impact on sample efficiency and online policy performance. Finally, we show that the pathology can be remedied by non-parametric behavioral reference policies and that this allows KL-regularized reinforcement learning to significantly outperform state-of-the-art approaches on a variety of challenging locomotion and dexterous hand manipulation tasks.

Three main points: 1. Data Science (DS) will be increasingly important to heliophysics; 2. Methods of heliophysics science discovery will continually evolve, requiring the use of learning technologies [e.g., machine learning (ML)] that are applied rigorously and that are capable of supporting discovery; and 3. To grow with the pace of data, technology, and workforce changes, heliophysics requires a new approach to the representation of knowledge.

In the Earth's magnetosphere, there are fewer than a dozen dedicated probes beyond low-Earth orbit making in-situ observations at any given time. As a result, we poorly understand its global structure and evolution, the mechanisms of its main activity processes, magnetic storms, and substorms. New Artificial Intelligence (AI) methods, including machine learning, data mining, and data assimilation, as well as new AI-enabled missions will need to be developed to meet this Sparse Data challenge.