背景：12个引线ECG是心血管疾病的核心诊断工具。在这里，我们描述并分析了一个集成的深度神经网络架构，从12个引导eCG分类了24个心脏异常。方法：我们提出了挤压和激发reset，以自动学习来自12个引主ECG的深度特征，以识别24个心脏病。在最终完全连接的层中，随着年龄和性别特征增强了深度特征。使用约束网格搜索设置每个类的输出阈值。为了确定为什么该模型的预测不正确，两个专家诊所人员独立地解释了一组关于左轴偏差的一次无序的ECG。结果：采用定制加权精度度量，我们达到了0.684的5倍交叉验证得分，灵敏度和特异性分别为0.758和0.969。我们在完整的测试数据中得分0.520，并在官方挑战排名中排名第21中。在一系列被错误分类的心电图中，两个临床医生和训练标签之间的协议差（临床医生1：Kappa = -0.057，临床医生2：Kappa = -0.159）。相比之下，临床医生之间的协议非常高（Kappa = 0.92）。讨论：与在相同数据上培训的模型相比，所提出的预测模型很好地对验证和隐藏的测试数据进行了良好。我们还发现培训标签的相当不一致，这可能会阻碍更准确的模型的开发。

We present Muse, a text-to-image Transformer model that achieves state-of-the-art image generation performance while being significantly more efficient than diffusion or autoregressive models. Muse is trained on a masked modeling task in discrete token space: given the text embedding extracted from a pre-trained large language model (LLM), Muse is trained to predict randomly masked image tokens. Compared to pixel-space diffusion models, such as Imagen and DALL-E 2, Muse is significantly more efficient due to the use of discrete tokens and requiring fewer sampling iterations; compared to autoregressive models, such as Parti, Muse is more efficient due to the use of parallel decoding. The use of a pre-trained LLM enables fine-grained language understanding, translating to high-fidelity image generation and the understanding of visual concepts such as objects, their spatial relationships, pose, cardinality etc. Our 900M parameter model achieves a new SOTA on CC3M, with an FID score of 6.06. The Muse 3B parameter model achieves an FID of 7.88 on zero-shot COCO evaluation, along with a CLIP score of 0.32. Muse also directly enables a number of image editing applications without the need to fine-tune or invert the model: inpainting, outpainting, and mask-free editing. More results are available at https://muse-model.github.io

Autonomous robotic surgery has advanced significantly based on analysis of visual and temporal cues in surgical workflow, but relational cues from domain knowledge remain under investigation. Complex relations in surgical annotations can be divided into intra- and inter-relations, both valuable to autonomous systems to comprehend surgical workflows. Intra- and inter-relations describe the relevance of various categories within a particular annotation type and the relevance of different annotation types, respectively. This paper aims to systematically investigate the importance of relational cues in surgery. First, we contribute the RLLS12M dataset, a large-scale collection of robotic left lateral sectionectomy (RLLS), by curating 50 videos of 50 patients operated by 5 surgeons and annotating a hierarchical workflow, which consists of 3 inter- and 6 intra-relations, 6 steps, 15 tasks, and 38 activities represented as the triplet of 11 instruments, 8 actions, and 16 objects, totaling 2,113,510 video frames and 12,681,060 annotation entities. Correspondingly, we propose a multi-relation purification hybrid network (MURPHY), which aptly incorporates novel relation modules to augment the feature representation by purifying relational features using the intra- and inter-relations embodied in annotations. The intra-relation module leverages a R-GCN to implant visual features in different graph relations, which are aggregated using a targeted relation purification with affinity information measuring label consistency and feature similarity. The inter-relation module is motivated by attention mechanisms to regularize the influence of relational features based on the hierarchy of annotation types from the domain knowledge. Extensive experimental results on the curated RLLS dataset confirm the effectiveness of our approach, demonstrating that relations matter in surgical workflow analysis.

Mobile health (mHealth) technologies empower patients to adopt/maintain healthy behaviors in their daily lives, by providing interventions (e.g. push notifications) tailored to the user's needs. In these settings, without intervention, human decision making may be impaired (e.g. valuing near term pleasure over own long term goals). In this work, we formalize this relationship with a framework in which the user optimizes a (potentially impaired) Markov Decision Process (MDP) and the mHealth agent intervenes on the user's MDP parameters. We show that different types of impairments imply different types of optimal intervention. We also provide analytical and empirical explorations of these differences.

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.

Spurious correlations, or correlations that change across domains where a model can be deployed, present significant challenges to real-world applications of machine learning models. However, such correlations are not always "spurious"; often, they provide valuable prior information for a prediction beyond what can be extracted from the input alone. Here, we present a test-time adaptation method that exploits the spurious correlation phenomenon, in contrast to recent approaches that attempt to eliminate spurious correlations through invariance. We consider situations where the prior distribution $p(y, z)$, which models the marginal dependence between the class label $y$ and the nuisance factors $z$, may change across domains, but the generative model for features $p(\mathbf{x}|y, z)$ is constant. We note that this is an expanded version of the label shift assumption, where the labels now also include the nuisance factors $z$. Based on this observation, we train a classifier to predict $p(y, z|\mathbf{x})$ on the source distribution, and implement a test-time label shift correction that adapts to changes in the marginal distribution $p(y, z)$ using unlabeled samples from the target domain. We call our method "Test-Time Label-Shift Adaptation" or TTLSA. We apply our method to two different image datasets -- the CheXpert chest X-ray dataset and the colored MNIST dataset -- and show that it gives better downstream results than methods that try to train classifiers which are invariant to the changes in prior distribution. Code reproducing experiments is available at https://github.com/nalzok/test-time-label-shift .

In this technical note, we introduce an improved variant of nearest neighbors for counterfactual inference in panel data settings where multiple units are assigned multiple treatments over multiple time points, each sampled with constant probabilities. We call this estimator a doubly robust nearest neighbor estimator and provide a high probability non-asymptotic error bound for the mean parameter corresponding to each unit at each time. Our guarantee shows that the doubly robust estimator provides a (near-)quadratic improvement in the error compared to nearest neighbor estimators analyzed in prior work for these settings.

我们考虑了从一个示例轨迹中学习$ dx_t = f（x_t）dt+sigma（x_t）dw_t $的形式的随机微分方程的问题。这个问题比学习确定性动力学系统更具挑战性，因为一个示例轨迹仅提供有关未知功能$ f $，$ \ sigma $的间接信息，而随机过程$ dw_t $代表漂移，扩散和随机强迫术语，强迫术语，，分别。我们为此问题提出了一个简单的基于内核的解决方案，可以分解如下：（1）表示时间添加映射$ x_t \ rightarrow x_ {t+dt} $作为计算图，其中$ f $，$ \ \ Sigma $和$ DW_T $作为未知功能和随机变量出现。 （2）通过在未知函数上使用高斯过程（GP）先验的最大后验估计（给定数据）来完成图（近似未知的函数和随机变量）。 （3）从具有随机交叉验证的数据中学习GP先验的协方差函数（内核）。数值实验说明了我们方法的功效，鲁棒性和范围。

在纠缠和连贯性等计量学中利用量子效应使人们可以测量具有增强灵敏度的参数。但是，时间依赖性的噪声会破坏这种海森堡限制的扩增。我们提出了一种基于量子信号处理框架，以克服这些现实的噪声诱导的实践量子计量学限制。我们的算法将门参数$ \ varphi $〜（单量Z阶段）分开，该算法易受时间依赖性错误与目标门参数$ \ theta $〜（| 10>和| 01> state之间的交换 - 角）易受时间依赖时间的错误。这在很大程度上没有时间依赖性误差。我们的方法实现了$ 10^{ - 4} $径向的准确性，用于学习超导级实验的$ \ theta $，以优于两个数量级的现有替代方案。我们还通过快速的傅立叶变换和顺序相位差异证明了学习时间依赖性栅极参数的鲁棒性。我们从理论和数字上均显示出最佳计量方差缩放的有趣过渡，这是电路深度$ d $的函数，从预抗态度制度$ d \ ll 1/\ theta $ to to Heisenberg限制$ d \ to \ to \ $ $。值得注意的是，在临时策略中，我们的方法对时间敏感参数$ \ varphi $比例的估计差异比渐近的海森伯格限制快速限制为深度的函数，$ \ text {var}（\ hat {\ varphi}）\ aid 1/d^4 $。我们的工作是第一个证明在实验室量子计算机中实用应用的量子信号处理算法。

解决时间扩展的任务是大多数增强学习（RL）算法的挑战[ARXIV：1906.07343]。我们研究了RL代理商学会提出自然语言问题的能力，以了解其环境并在新颖，时间扩展的环境中实现更大的概括性能。我们通过赋予该代理商的能力向全知的甲骨文提出“是，不”问题来做到这一点。这使代理商可以获得有关手头任务的指导，同时限制了对新信息的访问。为了在时间扩展的任务的背景下研究这种自然语言问题的出现，我们首先在迷你网格环境中训练代理商。然后，我们将受过训练的代理转移到另一个更艰难的环境中。与无法提出问题的基线代理相比，我们观察到概括性能的显着提高。通过将其对自然语言在其环境中的理解，代理可以推理其环境的动态，以至于在新型环境中部署时可以提出新的，相关的问题。