Recent work has shown that machine learning (ML) models can be trained to accurately forecast the dynamics of unknown chaotic dynamical systems. Such ML models can be used to produce both short-term predictions of the state evolution and long-term predictions of the statistical patterns of the dynamics (``climate''). Both of these tasks can be accomplished by employing a feedback loop, whereby the model is trained to predict forward one time step, then the trained model is iterated for multiple time steps with its output used as the input. In the absence of mitigating techniques, however, this technique can result in artificially rapid error growth, leading to inaccurate predictions and/or climate instability. In this article, we systematically examine the technique of adding noise to the ML model input during training as a means to promote stability and improve prediction accuracy. Furthermore, we introduce Linearized Multi-Noise Training (LMNT), a regularization technique that deterministically approximates the effect of many small, independent noise realizations added to the model input during training. Our case study uses reservoir computing, a machine-learning method using recurrent neural networks, to predict the spatiotemporal chaotic Kuramoto-Sivashinsky equation. We find that reservoir computers trained with noise or with LMNT produce climate predictions that appear to be indefinitely stable and have a climate very similar to the true system, while reservoir computers trained without regularization are unstable. Compared with other types of regularization that yield stability in some cases, we find that both short-term and climate predictions from reservoir computers trained with noise or with LMNT are substantially more accurate. Finally, we show that the deterministic aspect of our LMNT regularization facilitates fast hyperparameter tuning when compared to training with noise.

我们提出了GAAF（一种广义自动解剖器查找器），用于鉴定3D CT扫描中的通用解剖位置。GAAF是端到端管道，具有专用模块用于数据预处理，模型培训和推理。GAAF以核心使用自定义卷积神经网络（CNN）。CNN型号很小，轻巧，可以调整以适合特定应用。到目前为止，GAAF框架已经在头部和颈部进行了测试，并且能够找到解剖位置，例如脑干的质量中心。GAAF在开放式数据集中进行了评估，并且能够准确稳健地定位性能。我们所有的代码都是开源的，可在https://github.com/rrr-uom-projects/gaaf上找到。

腹部器官分割是一项艰巨且耗时的任务。为了减轻临床专家的负担，非常需要完全自动化的方法。当前的方法由卷积神经网络（CNN）主导，但是计算要求和对大数据集的需求限制了其在实践中的应用。通过实施小而高效的自定义3D CNN，编译训练的模型并优化计算图：我们的方法可产生高精度分割（骰子相似性系数（％）：肝脏：97.3 $ \ pm 1.3，肾脏：94.8 $ \ pm $ 3.6，$ 3.6，，$ 3.6，，$ 3.6，，，$ 3.6，，，$ 3.6，，，$ 3.6，，$ \ pm $ 3.6，，肝气脾脏：96.4 $ \ pm $ 3.0，pancreas：80.9 $ \ pm $ 10.1），每张图像1.6秒。至关重要的是，我们能够仅在CPU上执行细分推断（无需GPU），从而在没有专家硬件的情况下便利地促进模型的简单和广泛部署。

随着在高风险决策中引入机器学习，确保算法公平已成为越来越重要的问题。为此，已经提出了许多关于公平性的数学定义，并且已经开发了多种优化技术，所有这些都旨在最大化明确的公平概念。但是，公平解决方案取决于训练数据的质量，并且对噪声高度敏感。最近的研究表明，鲁棒性（模型在看不见的数据上表现良好的能力）在解决新问题时应使用的策略类型起着重要作用，因此，测量这些策略的鲁棒性已成为一种基本问题。因此，在这项工作中，我们提出了一个新标准，以衡量各种公平优化策略的鲁棒性 - \ textit {稳健性比率}。我们使用三种最受欢迎​​的公平策略在五个最受欢迎的公平定义方面，在五个基准标记公平数据集上进行了多次广泛的实验。我们的实验从经验上表明，依赖阈值优化的公平方法对所有评估的数据集中的噪声非常敏感，尽管大多数表现优于其他方法。这与其他两种方法相反，这对于低噪声方案而言不太公平，但对于高噪声方案而言更公平。据我们所知，我们是第一个定量评估公平优化策略的鲁棒性的人。这可以作为选择各种数据集的最合适的公平策略的指南。

由于技术困难以与原始数据一致的方式更改数据，因此在网络安全域中，数据扩展很少见。鉴于获得符合版权限制的良性和恶意培训数据的独特困难，这一缺陷尤其繁重，而银行和政府等机构会收到有针对性的恶意软件，这些恶意软件永远不会大量存在。我们介绍Marvolo是一种二进制突变器，该突变器以编程方式生产恶意软件（和良性）数据集，以提高ML驱动的恶意软件探测器的准确性。 Marvolo采用语义保护代码转换，模仿恶意软件作者和防御性良性开发人员通常在实践中进行的更改，从而使我们能够生成有意义的增强数据。至关重要的是，语义传播的转换也使Marvolo能够安全地将标签从原始生成的数据样本传播到，而无需规定昂贵的二进制文件的昂贵反向工程。此外，Marvolo通过最大化给定时间（或资源）预算中生成的各种数据样本的密度来最大化，使从业人员最大程度地嵌入了几种关键优化。使用广泛的商业恶意软件数据集和最近的ML驱动的恶意软件探测器进行的实验表明，Marvolo将准确性提高了5％，而仅在潜在的输入二进制文件的一小部分（15％）上运行。

神经网络稳健性近年来已成为机器学习中的核心主题。大多数培训算法，提高模型对抗对抗和共同腐败的鲁棒性也引入了大的计算开销，需要向前和后向往的数量和后向往的多达十倍以便收敛。为了打击这种低效率，我们提出了Bullettrain $ - $界限示例挖掘技术，以大大降低强大培训的计算成本。我们的主要观察是，只有一小部分的例子是有利于改善稳健性的有益。Bullettrain动态预测了这些重要的例子，并优化了强大的培训算法，专注于重要例子。我们将技术应用于几个现有的强大培训算法，在CiFar-10和Cifar-10-C和CiFar上的Augmix上获得了2.1美元\ Times $ 10.7 $ \ times $ Scase-Up。100-C没有任何清洁和稳健的准确性。

深度强化学习（RL）的进展是通过用于培训代理商的具有挑战性的基准的可用性来驱动。但是，社区广泛采用的基准未明确设计用于评估RL方法的特定功能。虽然存在用于评估RL的特定打开问题的环境（例如探索，转移学习，无监督环境设计，甚至语言辅助RL），但一旦研究超出证明，通常难以将这些更富有，更复杂的环境 - 概念结果。我们展示了一个强大的沙箱框架，用于易于设计新颖的RL环境。 Minihack是一个停止商店，用于RL实验，环境包括从小房间到复杂的，程序生成的世界。通过利用来自Nethack的全套实体和环境动态，MiniHack是最富有的基网上的视频游戏之一，允许设计快速方便的定制RL测试台。使用这种沙箱框架，可以轻松设计新颖的环境，可以使用人类可读的描述语言或简单的Python接口来设计。除了各种RL任务和基线外，Minihack还可以包装现有的RL基准，并提供无缝添加额外复杂性的方法。

With an ever-growing number of parameters defining increasingly complex networks, Deep Learning has led to several breakthroughs surpassing human performance. As a result, data movement for these millions of model parameters causes a growing imbalance known as the memory wall. Neuromorphic computing is an emerging paradigm that confronts this imbalance by performing computations directly in analog memories. On the software side, the sequential Backpropagation algorithm prevents efficient parallelization and thus fast convergence. A novel method, Direct Feedback Alignment, resolves inherent layer dependencies by directly passing the error from the output to each layer. At the intersection of hardware/software co-design, there is a demand for developing algorithms that are tolerable to hardware nonidealities. Therefore, this work explores the interrelationship of implementing bio-plausible learning in-situ on neuromorphic hardware, emphasizing energy, area, and latency constraints. Using the benchmarking framework DNN+NeuroSim, we investigate the impact of hardware nonidealities and quantization on algorithm performance, as well as how network topologies and algorithm-level design choices can scale latency, energy and area consumption of a chip. To the best of our knowledge, this work is the first to compare the impact of different learning algorithms on Compute-In-Memory-based hardware and vice versa. The best results achieved for accuracy remain Backpropagation-based, notably when facing hardware imperfections. Direct Feedback Alignment, on the other hand, allows for significant speedup due to parallelization, reducing training time by a factor approaching N for N-layered networks.

The SINDy algorithm has been successfully used to identify the governing equations of dynamical systems from time series data. In this paper, we argue that this makes SINDy a potentially useful tool for causal discovery and that existing tools for causal discovery can be used to dramatically improve the performance of SINDy as tool for robust sparse modeling and system identification. We then demonstrate empirically that augmenting the SINDy algorithm with tools from causal discovery can provides engineers with a tool for learning causally robust governing equations.

When testing conditions differ from those represented in training data, so-called out-of-distribution (OOD) inputs can mar the reliability of black-box learned components in the modern robot autonomy stack. Therefore, coping with OOD data is an important challenge on the path towards trustworthy learning-enabled open-world autonomy. In this paper, we aim to demystify the topic of OOD data and its associated challenges in the context of data-driven robotic systems, drawing connections to emerging paradigms in the ML community that study the effect of OOD data on learned models in isolation. We argue that as roboticists, we should reason about the overall system-level competence of a robot as it performs tasks in OOD conditions. We highlight key research questions around this system-level view of OOD problems to guide future research toward safe and reliable learning-enabled autonomy.