Research around AI for Science has seen significant success since the rise of deep learning models over the past decade, even with longstanding challenges such as protein structure prediction. However, this fast development inevitably made their flaws apparent -- especially in domains of reasoning where understanding the cause-effect relationship is important. One such domain is drug discovery, in which such understanding is required to make sense of data otherwise plagued by spurious correlations. Said spuriousness only becomes worse with the ongoing trend of ever-increasing amounts of data in the life sciences and thereby restricts researchers in their ability to understand disease biology and create better therapeutics. Therefore, to advance the science of drug discovery with AI it is becoming necessary to formulate the key problems in the language of causality, which allows the explication of modelling assumptions needed for identifying true cause-effect relationships. In this attention paper, we present causal drug discovery as the craft of creating models that ground the process of drug discovery in causal reasoning.

Recent work has reported that AI classifiers trained on audio recordings can accurately predict severe acute respiratory syndrome coronavirus 2 (SARSCoV2) infection status. Here, we undertake a large scale study of audio-based deep learning classifiers, as part of the UK governments pandemic response. We collect and analyse a dataset of audio recordings from 67,842 individuals with linked metadata, including reverse transcription polymerase chain reaction (PCR) test outcomes, of whom 23,514 tested positive for SARS CoV 2. Subjects were recruited via the UK governments National Health Service Test-and-Trace programme and the REal-time Assessment of Community Transmission (REACT) randomised surveillance survey. In an unadjusted analysis of our dataset AI classifiers predict SARS-CoV-2 infection status with high accuracy (Receiver Operating Characteristic Area Under the Curve (ROCAUC) 0.846 [0.838, 0.854]) consistent with the findings of previous studies. However, after matching on measured confounders, such as age, gender, and self reported symptoms, our classifiers performance is much weaker (ROC-AUC 0.619 [0.594, 0.644]). Upon quantifying the utility of audio based classifiers in practical settings, we find them to be outperformed by simple predictive scores based on user reported symptoms.

Since early in the coronavirus disease 2019 (COVID-19) pandemic, there has been interest in using artificial intelligence methods to predict COVID-19 infection status based on vocal audio signals, for example cough recordings. However, existing studies have limitations in terms of data collection and of the assessment of the performances of the proposed predictive models. This paper rigorously assesses state-of-the-art machine learning techniques used to predict COVID-19 infection status based on vocal audio signals, using a dataset collected by the UK Health Security Agency. This dataset includes acoustic recordings and extensive study participant meta-data. We provide guidelines on testing the performance of methods to classify COVID-19 infection status based on acoustic features and we discuss how these can be extended more generally to the development and assessment of predictive methods based on public health datasets.

The UK COVID-19 Vocal Audio Dataset is designed for the training and evaluation of machine learning models that classify SARS-CoV-2 infection status or associated respiratory symptoms using vocal audio. The UK Health Security Agency recruited voluntary participants through the national Test and Trace programme and the REACT-1 survey in England from March 2021 to March 2022, during dominant transmission of the Alpha and Delta SARS-CoV-2 variants and some Omicron variant sublineages. Audio recordings of volitional coughs, exhalations, and speech were collected in the 'Speak up to help beat coronavirus' digital survey alongside demographic, self-reported symptom and respiratory condition data, and linked to SARS-CoV-2 test results. The UK COVID-19 Vocal Audio Dataset represents the largest collection of SARS-CoV-2 PCR-referenced audio recordings to date. PCR results were linked to 70,794 of 72,999 participants and 24,155 of 25,776 positive cases. Respiratory symptoms were reported by 45.62% of participants. This dataset has additional potential uses for bioacoustics research, with 11.30% participants reporting asthma, and 27.20% with linked influenza PCR test results.

Interpretability is a pressing issue for machine learning. Common approaches to interpretable machine learning constrain interactions between features of the input, rendering the effects of those features on a model's output comprehensible but at the expense of model complexity. We approach interpretability from a new angle: constrain the information about the features without restricting the complexity of the model. Borrowing from information theory, we use the Distributed Information Bottleneck to find optimal compressions of each feature that maximally preserve information about the output. The learned information allocation, by feature and by feature value, provides rich opportunities for interpretation, particularly in problems with many features and complex feature interactions. The central object of analysis is not a single trained model, but rather a spectrum of models serving as approximations that leverage variable amounts of information about the inputs. Information is allocated to features by their relevance to the output, thereby solving the problem of feature selection by constructing a learned continuum of feature inclusion-to-exclusion. The optimal compression of each feature -- at every stage of approximation -- allows fine-grained inspection of the distinctions among feature values that are most impactful for prediction. We develop a framework for extracting insight from the spectrum of approximate models and demonstrate its utility on a range of tabular datasets.

近年来，由于它在机器人技术和自主驾驶中的应用，因此自我监督的单眼深度估计已成为一项激烈研究的主题。最近的许多工作都集中在通过提高体系结构复杂性来改善深度估计。本文表明，也可以通过改善学习过程而不是提高模型复杂性来实现最先进的绩效。更具体地说，我们建议（i）仅在训练期间对前几个时期使用不变姿势损失，（ii）训练时忽略小的潜在动态物体，（iii）采用基于外观的方法分别估算物体姿势，以实现真正动态的姿势对象。我们证明这些简化将GPU的内存使用量减少了29％，并导致定性和定量改进的深度图

已经发现，已经发现深度学习架构，特别是深度动量网络（DMNS）[1904.04912]是一种有效的势头和平均逆转交易的方法。然而，近年来一些关键挑战涉及学习长期依赖，在考虑返回交易成本净净额并适应新的市场制度时，绩效的退化，特别是在SARS-COV-2危机期间。注意机制或基于变换器的架构是对这些挑战的解决方案，因为它们允许网络专注于过去和长期模式的重要时间步骤。我们介绍了势头变压器，一种基于关注的架构，胜过基准，并且本质上是可解释的，为我们提供更大的深入学习交易策略。我们的模型是基于LSTM的DMN的扩展，它通过在风险调整的性能度量上优化网络，直接输出位置尺寸，例如锐利比率。我们发现注意力LSTM混合解码器仅时间融合变压器（TFT）样式架构是最佳的执行模型。在可解释性方面，我们观察注意力模式的显着结构，在动量转点时具有重要的重要性。因此，时间序列被分段为制度，并且该模型倾向于关注以前的制度中的先前时间步骤。我们发现ChangePoint检测（CPD）[2105.13727]，另一个用于响应政权变化的技术可以补充多抬头的注意力，特别是当我们在多个时间尺度运行CPD时。通过添加可解释的变量选择网络，我们观察CPD如何帮助我们的模型在日常返回数据上主要远离交易。我们注意到该模型可以智能地切换和混合古典策略 - 基于数据的决定。

单图像姿势估计是许多视觉和机器人任务中的一个基本问题，并且现有的深度学习方法不会完全建模和处理来遭受：i）关于预测的不确定性，ii）具有多个（有时是无限）正确姿势的对称对象。为此，我们引入了一种在SO（3）上估算任意非参数分布的方法。我们的关键思想是通过神经网络隐含地表示分布，该神经网络估计给定输入图像和候选姿势的概率。网格采样或梯度上升可用于找到最有可能的姿势，但也可以评估任何姿势的概率，从而实现关于对称性和不确定性的推理。这是代表流形分布的最通用方法，为了展示丰富的表现力，我们介绍了一个具有挑战性的对称和几乎对称对象的数据集。我们不需要对姿势不确定性的监督 - 模型仅以一个示例训练单个姿势。但是，我们的隐式模型具有高度表达能力在3D姿势上处理复杂的分布，同时仍然在标准的非歧义环境上获得准确的姿势估计，从而在Pascal3d+和ModelNet10-SO-SO（3）基准方面实现了最先进的性能。

动量策略是替代投资的重要组成部分，是商品交易顾问（CTA）的核心。然而，这些策略已被发现难以调整市场条件的快速变化，例如在2020年市场崩溃期间。特别是，在动量转向点之后，在趋势从上升趋势（下降趋势）逆转到下降趋势（上升趋势），时间序列动量（TSMOM）策略容易发生不良赌注。为了提高对政权变更的响应，我们介绍了一种新颖的方法，在那里我们将在线切换点检测（CPD）模块插入深势网络（DMN）[1904.04912]管道，它使用LSTM深度学习架构同时学习趋势估算与定位尺寸。此外，我们的模型能够优化它的平衡1）延迟延期的速度策略，它利用持续趋势，但没有过度反应到本地化价格移动，而且2）通过快速翻转其位置，这是一种快速平均转换策略制度，然后再次将其交换为利用本地化的价格。我们的CPD模块输出ChangePoint位置和严重性分数，允许我们的模型以数据驱动的方式学习响应变化的不平衡或更小，更局部化的变换点。在1995 - 2020年期间，在1995 - 2020年期间，添加CPD模块的添加导致夏普率的提高三分之一。该模块在显着的非间抗性期间特别有益，特别是在最近几年（2015-2020）中，性能提升大约三分之二。随着传统的动量策略在此期间的表现不佳，这很有趣。

代表性学习形成最深入的学习应用的骨干，并且学习表示的值与其关于不同变异因素的信息内容密切相关。找到良好的表现取决于监督和学习算法的性质。我们提出了一种新颖的算法，该算法利用弱形的监督形式，其中数据根据各种变体的某些无效（常见）因子来划分到集合中，这些因素在每个集合的元素中不变。我们的主要识别是，通过寻求不同集合之间的对应，我们学习强烈的表示，排除了变异的非活动因素，并隔离了所有集合中变化的活动（不同）因素。由于重点关注有源因素，我们的方法可以利用设定监督和全文无监督数据的混合，甚至可以属于不同的域。我们通过将概括到类别水平和综合/真实域间隙的图像姿势信息隔离，解决综合对象姿势转移的具有挑战性问题，即使没有对任何内容的姿势注释也没有姿势注释。通过加强中间表示，该方法还可以提高监督设置的性能。