许多操作数值天气预报系统中使用的数据同化程序基于4D-VAR算法的变体。解决4D-VAR问题的成本是由物理模型的前进和伴随评估的成本为主。这通过快速，近似代理模型来激励他们的替代。神经网络为代理模型的数据驱动创建提供了一个有希望的方法。已显示代理4D-VAR问题解决方案的准确性，明确地依赖于对其他代理建模方法和一般非线性设置的准确建模和伴随的准确建模。我们制定和分析若干方法，将衍生信息纳入神经网络替代品的构建。通过训练集数据和Lorenz-63系统上的顺序数据同化设置来测试生成的网络。与没有伴随信息的替代网络培训的代理网络相比，两种方法表现出卓越的性能，显示将伴随信息纳入训练过程的益处。

A machine learning model, under the influence of observed or unobserved confounders in the training data, can learn spurious correlations and fail to generalize when deployed. For image classifiers, augmenting a training dataset using counterfactual examples has been empirically shown to break spurious correlations. However, the counterfactual generation task itself becomes more difficult as the level of confounding increases. Existing methods for counterfactual generation under confounding consider a fixed set of interventions (e.g., texture, rotation) and are not flexible enough to capture diverse data-generating processes. Given a causal generative process, we formally characterize the adverse effects of confounding on any downstream tasks and show that the correlation between generative factors (attributes) can be used to quantitatively measure confounding between generative factors. To minimize such correlation, we propose a counterfactual generation method that learns to modify the value of any attribute in an image and generate new images given a set of observed attributes, even when the dataset is highly confounded. These counterfactual images are then used to regularize the downstream classifier such that the learned representations are the same across various generative factors conditioned on the class label. Our method is computationally efficient, simple to implement, and works well for any number of generative factors and confounding variables. Our experimental results on both synthetic (MNIST variants) and real-world (CelebA) datasets show the usefulness of our approach.

基于中心的聚类（例如，$ k $ -means，$ k $ -Medians）和使用线性子空间的聚类是两种最受欢迎的技术，可以将真实数据分配到较小的群集中。但是，当数据由敏感人群组组成时，不同敏感组的每点的聚集成本显着不同，可能会导致与公平相关的危害（例如，服务质量不同）。社会公平聚类的目的是最大程度地降低所有组中每点聚类的最大成本。在这项工作中，我们提出了一个统一的框架，以解决社会公平的基于中心的聚类和线性子空间聚类，并为这些问题提供实用，高效的近似算法。我们进行了广泛的实验，以表明在多个基准数据集上，我们的算法要么紧密匹配或超越最先进的基线。

神经网络利用数据中的因果关系和相关的关系，以学习优化给定性能标准的模型，例如分类准确性。这导致学习模型可能不一定反映输入和输出之间的真实因果关系。当在培训时可获得因果关系的域中，即使在学习优化性能标准时，神经网络模型也将这些关系保持为因果关系。我们提出了一种因果规则化方法，可以将这种因果域前瞻纳入网络，并支持直接和完全因果效应。我们表明这种方法可以推广到各种因果前导者的规范，包括给定输入特征的因果效果的单调性或针对公平的目的去除一定的影响。我们在11个基准数据集上的实验显示了这种方法在规则中规范学习的神经网络模型以保持所需的因果效果。在大多数数据集上，可以在不损害精度的情况下获得域名一致模型。

改变特定特征但不是其他特性的输入扰动的反事实示例 - 已经显示用于评估机器学习模型的偏差，例如，对特定的人口组。然而，由于图像的各种特征上的底层的因果结构，生成用于图像的反事实示例是非琐碎的。为了有意义，生成的扰动需要满足因果模型所暗示的约束。我们通过在前瞻性学习推断（ALI）的改进变型中结合结构因果模型（SCM）来提出一种方法，该方法是根据图像的属性之间的因果关系生成反事实。基于所生成的反事实，我们展示了如何解释预先训练的机器学习分类器，评估其偏置，并使用反事实程序缓解偏差。在Morpho-Mnist DataSet上，我们的方法会在质量上产生与基于SCM的Factficuls（DeepScm）的质量相当的反功能，而在更复杂的Celeba DataSet上，我们的方法优于DeepScm在产生高质量的有效反应性时。此外，生成的反事件难以从人类评估实验中的重建图像中无法区分，并且随后使用它们来评估在Celeba数据上培训的标准分类器的公平性。我们表明分类器是偏见的w.r.t.皮肤和头发颜色，以及反事实规则化如何消除这些偏差。

现代目光跟踪系统中的相机具有基本的带宽和功率限制，实际上将数据采集速度限制为300 Hz。这会阻碍使用移动眼镜手术器的使用，例如低潜伏期预测性渲染，或者在野外使用头部安装的设备来快速而微妙的眼动运动，例如微扫视。在这里，我们提出了一个基于混合框架的近眼凝视跟踪系统，可提供超过10,000 Hz的更新速率，其准确性与在相同条件下评估时相匹配的高端台式机商业跟踪器。我们的系统建立在新兴事件摄像机的基础上，该摄像头同时获得定期采样框架和自适应采样事件。我们开发了一种在线2D学生拟合方法，该方法每一个或几个事件都会更新参数模型。此外，我们提出了一个多项式回归器，用于实时估算参数学生模型的凝视点。使用第一个基于事件的凝视数据集，可在https://github.com/aangelopoulos/event_based_gaze_tracking上获得，我们证明我们的系统可实现0.45度 - 1.75度的准确度，用于从45度到98度的视野。借助这项技术，我们希望能够为虚拟和增强现实提供新一代的超低延迟凝视呈现和展示技术。

（非）神经网络到小，对抗像素明智的扰动的鲁棒性，并且最近示出了甚至是随机空间转换（例如，翻译，旋转）恳求理论和经验理解。通过等级模型（例如，STDCNNS，GCNN）和训练增强，通常实现了随机翻译和旋转的空间鲁棒性，而普遍鲁棒性通常通过对抗性训练来实现。在本文中，我们在简单的统计环境中证明了空间和对抗性鲁棒性之间的定量折衷。我们通过展示：（a）随着等效模型的空间稳健性通过逐步培训更大的转化来改善，它们的对抗鲁棒性逐渐恶化，并且（b）随着最先进的强大模型是对抗的具有较大的像素明智的扰动训练，它们的空间鲁棒性逐渐下降。在此权衡中实现帕累托 - 最优性，我们提出了一种基于课程学习的方法，该方法逐步列举更加困难的扰动（空间和对抗性），以同时改善空间和对抗鲁棒性。

While the brain connectivity network can inform the understanding and diagnosis of developmental dyslexia, its cause-effect relationships have not yet enough been examined. Employing electroencephalography signals and band-limited white noise stimulus at 4.8 Hz (prosodic-syllabic frequency), we measure the phase Granger causalities among channels to identify differences between dyslexic learners and controls, thereby proposing a method to calculate directional connectivity. As causal relationships run in both directions, we explore three scenarios, namely channels' activity as sources, as sinks, and in total. Our proposed method can be used for both classification and exploratory analysis. In all scenarios, we find confirmation of the established right-lateralized Theta sampling network anomaly, in line with the temporal sampling framework's assumption of oscillatory differences in the Theta and Gamma bands. Further, we show that this anomaly primarily occurs in the causal relationships of channels acting as sinks, where it is significantly more pronounced than when only total activity is observed. In the sink scenario, our classifier obtains 0.84 and 0.88 accuracy and 0.87 and 0.93 AUC for the Theta and Gamma bands, respectively.

Differentiable Architecture Search (DARTS) has attracted considerable attention as a gradient-based Neural Architecture Search (NAS) method. Since the introduction of DARTS, there has been little work done on adapting the action space based on state-of-art architecture design principles for CNNs. In this work, we aim to address this gap by incrementally augmenting the DARTS search space with micro-design changes inspired by ConvNeXt and studying the trade-off between accuracy, evaluation layer count, and computational cost. To this end, we introduce the Pseudo-Inverted Bottleneck conv block intending to reduce the computational footprint of the inverted bottleneck block proposed in ConvNeXt. Our proposed architecture is much less sensitive to evaluation layer count and outperforms a DARTS network with similar size significantly, at layer counts as small as 2. Furthermore, with less layers, not only does it achieve higher accuracy with lower GMACs and parameter count, GradCAM comparisons show that our network is able to better detect distinctive features of target objects compared to DARTS.

We propose an ensemble approach to predict the labels in linear programming word problems. The entity identification and the meaning representation are two types of tasks to be solved in the NL4Opt competition. We propose the ensembleCRF method to identify the named entities for the first task. We found that single models didn't improve for the given task in our analysis. A set of prediction models predict the entities. The generated results are combined to form a consensus result in the ensembleCRF method. We present an ensemble text generator to produce the representation sentences for the second task. We thought of dividing the problem into multiple small tasks due to the overflow in the output. A single model generates different representations based on the prompt. All the generated text is combined to form an ensemble and produce a mathematical meaning of a linear programming problem.