大多数怀孕和出生会导致良好的结果，但是并不常见，当发生时，它们可能会与母亲和婴儿的严重影响相关。预测建模有可能通过更好地理解风险因素，增强监视以及更及时，更适当的干预措施来改善结果，从而帮助产科医生提供更好的护理。对于三种类型的并发症，我们使用可解释的提升机（EBM）（玻璃箱模型）来识别和研究最重要的风险因素，以获得清晰度：（i）严重的孕妇发病率（SMM），（ii）（iii）早产启示性。在使用EBM的解释性来揭示出对风险促成的特征的惊人见解时，我们的实验表明EBM与其他黑盒ML方法（例如深神经网和随机森林）的准确性相匹配。

机器学习（ML）可解释性技术可以揭示数据中的不良模式，这些模型模型开发以做出预测 - 一旦部署就会​​造成危害。但是，如何采取行动解决这些模式并不总是很清楚。在ML与人类计算机互动研究人员，医师和数据科学家之间的合作中，我们开发了GAM Changer，这是第一个互动系统，可帮助域专家和数据科学家轻松，负责任地编辑通用的添加剂模型（GAM）和修复有问题的模式。借助新颖的交互技术，我们的工具将可解释性置于行动中 - 使用户能够分析，验证和使模型行为与知识和价值相结合。医师已经开始使用我们的工具来调查和修复肺炎和败血症的风险预测模型，以及在不同领域工作的7位数据科学家的评估突出显示我们的工具易于使用，满足他们的模型编辑需求，并适合他们当前的工作流程。我们的工具以现代网络技术为基础，在用户的网络浏览器或计算笔记本电脑中本地运行，从而降低了使用的障碍。 GAM Changer可在以下公共演示链接中获得：https：//interpret.ml/gam-changer。

最近在可解释的机器学习中的进展（ML）研究表明，模型利用数据中的不良模式来进行预测，这可能导致部署危害。但是，尚不清楚我们如何解决这些模型。我们介绍了我们正在进行的工作，游戏改变者，一个开源交互式系统，以帮助数据科学家和领域专家轻松且负责任地编辑其广义添加剂模型（Gams）。通过新颖的可视化技术，我们的工具将可解释性投入到行动 - 使人类用户能够分析，验证和对齐模型行为与他们的知识和价值。使用现代Web技术建造，我们的工具在用户的计算笔记本或Web浏览器中在本地运行，而无需额外计算资源，降低屏障以创建更负责的ML模型。Gam更换器可在https://interpret.ml/gam-changer中获得。

Generalizability of time series forecasting models depends on the quality of model selection. Temporal cross validation (TCV) is a standard technique to perform model selection in forecasting tasks. TCV sequentially partitions the training time series into train and validation windows, and performs hyperparameter optmization (HPO) of the forecast model to select the model with the best validation performance. Model selection with TCV often leads to poor test performance when the test data distribution differs from that of the validation data. We propose a novel model selection method, H-Pro that exploits the data hierarchy often associated with a time series dataset. Generally, the aggregated data at the higher levels of the hierarchy show better predictability and more consistency compared to the bottom-level data which is more sparse and (sometimes) intermittent. H-Pro performs the HPO of the lowest-level student model based on the test proxy forecasts obtained from a set of teacher models at higher levels in the hierarchy. The consistency of the teachers' proxy forecasts help select better student models at the lowest-level. We perform extensive empirical studies on multiple datasets to validate the efficacy of the proposed method. H-Pro along with off-the-shelf forecasting models outperform existing state-of-the-art forecasting methods including the winning models of the M5 point-forecasting competition.

In this current technological world, the application of machine learning is becoming ubiquitous. Incorporating machine learning algorithms on extremely low-power and inexpensive embedded devices at the edge level is now possible due to the combination of the Internet of Things (IoT) and edge computing. To estimate an outcome, traditional machine learning demands vast amounts of resources. The TinyML concept for embedded machine learning attempts to push such diversity from usual high-end approaches to low-end applications. TinyML is a rapidly expanding interdisciplinary topic at the convergence of machine learning, software, and hardware centered on deploying deep neural network models on embedded (micro-controller-driven) systems. TinyML will pave the way for novel edge-level services and applications that survive on distributed edge inferring and independent decision-making rather than server computation. In this paper, we explore TinyML's methodology, how TinyML can benefit a few specific industrial fields, its obstacles, and its future scope.

PROteolysis TArgeting Chimeras (PROTACs) are an emerging therapeutic modality for degrading a protein of interest (POI) by marking it for degradation by the proteasome. Recent developments in artificial intelligence (AI) suggest that deep generative models can assist with the de novo design of molecules with desired properties, and their application to PROTAC design remains largely unexplored. We show that a graph-based generative model can be used to propose novel PROTAC-like structures from empty graphs. Our model can be guided towards the generation of large molecules (30--140 heavy atoms) predicted to degrade a POI through policy-gradient reinforcement learning (RL). Rewards during RL are applied using a boosted tree surrogate model that predicts a molecule's degradation potential for each POI. Using this approach, we steer the generative model towards compounds with higher likelihoods of predicted degradation activity. Despite being trained on sparse public data, the generative model proposes molecules with substructures found in known degraders. After fine-tuning, predicted activity against a challenging POI increases from 50% to >80% with near-perfect chemical validity for sampled compounds, suggesting this is a promising approach for the optimization of large, PROTAC-like molecules for targeted protein degradation.

Many studies have examined the shortcomings of word error rate (WER) as an evaluation metric for automatic speech recognition (ASR) systems, particularly when used for spoken language understanding tasks such as intent recognition and dialogue systems. In this paper, we propose Hybrid-SD (H_SD), a new hybrid evaluation metric for ASR systems that takes into account both semantic correctness and error rate. To generate sentence dissimilarity scores (SD), we built a fast and lightweight SNanoBERT model using distillation techniques. Our experiments show that the SNanoBERT model is 25.9x smaller and 38.8x faster than SRoBERTa while achieving comparable results on well-known benchmarks. Hence, making it suitable for deploying with ASR models on edge devices. We also show that H_SD correlates more strongly with downstream tasks such as intent recognition and named-entity recognition (NER).

State-of-the-art algorithms for Approximate Nearest Neighbor Search (ANNS) such as DiskANN, FAISS-IVF, and HNSW build data dependent indices that offer substantially better accuracy and search efficiency over data-agnostic indices by overfitting to the index data distribution. When the query data is drawn from a different distribution - e.g., when index represents image embeddings and query represents textual embeddings - such algorithms lose much of this performance advantage. On a variety of datasets, for a fixed recall target, latency is worse by an order of magnitude or more for Out-Of-Distribution (OOD) queries as compared to In-Distribution (ID) queries. The question we address in this work is whether ANNS algorithms can be made efficient for OOD queries if the index construction is given access to a small sample set of these queries. We answer positively by presenting OOD-DiskANN, which uses a sparing sample (1% of index set size) of OOD queries, and provides up to 40% improvement in mean query latency over SoTA algorithms of a similar memory footprint. OOD-DiskANN is scalable and has the efficiency of graph-based ANNS indices. Some of our contributions can improve query efficiency for ID queries as well.

我们将人机协作问题解决的问题视为一项计划任务，再加上自然语言交流。我们的框架由三个组成部分组成 - 一种自然语言引擎，将语言话语解析为正式代表，反之亦然，这是一个概念学习者，该概念学习者基于与用户的有限互动来诱导计划的广义概念，以及解决方案的HTN规划师，以解决该计划。基于人类互动的任务。我们说明了该框架通过在基于Minecraft的Blocksworld域中的协作构建任务中证明协作问题解决的关键挑战的能力。随附的演示视频可在https://youtu.be/q1pwe4aahf0上获得。

机器学习模型的预测失败通常来自训练数据中的缺陷，例如不正确的标签，离群值和选择偏见。但是，这些负责给定失败模式的数据点通常不知道先验，更不用说修复故障的机制了。这项工作借鉴了贝叶斯对持续学习的看法，并为两者开发了一个通用框架，确定了导致目标失败的培训示例，并通过删除有关它们的信息来修复模型。该框架自然允许将最近学习的最新进展解决这一新的模型维修问题，同时将现有的作品集成了影响功能和数据删除作为特定实例。在实验上，提出的方法优于基准，既可以识别有害训练数据，又要以可普遍的方式固定模型失败。