预测到优化的框架在许多实际设置中都是基础：预测优化问题的未知参数，然后使用参数的预测值解决该问题。与参数的预测误差相反，在这种环境中的自然损失函数是考虑预测参数引起的决策成本。最近在Elmachtoub和Grigas（2022）中引入了此损失函数，并被称为智能预测 - 优化（SPO）损失。在这项工作中，我们试图提供有关在SPO损失的背景下，预测模型在训练数据中概括的预测模型的性能如何。由于SPO损失是非凸面和非lipschitz，因此不适用推导概括范围的标准结果。我们首先根据natarajan维度得出界限，在多面体可行区域中，在极端点数中最大程度地比对数扩展，但是，在一般凸的可行区域中，对决策维度具有线性依赖性。通过利用SPO损耗函数的结构和可行区域的关键特性，我们将其表示为强度属性，我们可以显着提高对决策和特征维度的依赖。我们的方法和分析依赖于围绕有问题的预测的利润，这些预测不会产生独特的最佳解决方案，然后在修改后的利润率SPO损失函数的背景下提供了概括界限，而SPO损失函数是Lipschitz的连续。最后，我们表征了强度特性，并表明可以有效地计算出具有显式极端表示的强凸体和多面体的修饰的SPO损耗。

在启用语音的应用程序中，一个预定的热词在同时用来激活设备以便进行查询。 toavoid重复一个热词，我们提出了一个端到端的流（E2E）打算查询检测器，该查询检测器识别向设备指向的发音，并滤除针对设备的其他发出内容。提出的方法将预期的查询检测器置于E2E模型中，该模型将语音识别的不同组件折叠成一个神经网络。E2E对台面解码和预期的查询检测进行建模，也使我们可以基于早期的部分偏置检测结果， ，这对于减少潜伏期和使系统响应很重要。我们证明，与独立的预期检测器相比，检测准确性和600个MSLATENCE的相对相对改善的相对提高一级误差率（EER）的相对提高了22％。在我们的实验中，提出的模型检测用户正在用用户开始讲话后，用8.7％的Eerwithin与设备进行对话。

我们扩展了共形预测以控制任何单调损耗函数的预期值。该算法将共形预测与其覆盖范围保证一起概括。像共构预测一样，共形风险控制程序紧密到$ \ Mathcal {o}（1/n）$ factor。来自计算机视觉和自然语言处理的工作示例表明，我们的算法使用了虚假的负率，图形距离和令牌级别的F1得分。

我们提出了Adios，这是一个用于自我监督学习的遮罩图像模型（MIM）框架，同时使用对抗性目标学习掩盖功能和图像编码器。对图像编码器进行了训练，以最大程度地减少原始图像的表示形式与蒙版图像的表示之间的距离。相反，掩蔽函数旨在最大化此距离。阿迪奥斯（Adios）始终改进有关各种任务和数据集的最先进的自我监督学习（SSL）方法 - 包括Imagenet100和STL10上的分类，CIFAR10/100上的转移学习，Flowers102和Inaturalist，以及鲁棒性在背景挑战中进行了评估（Xiao等，2021） - 同时产生语义意义的面具。与MAE，BEIT和IBOT等现代MIM模型不同，Adios不依赖视觉变压器的图像斑点令牌构造，并且可以用卷积的骨架来实现。我们进一步证明，与对流行MIM模型中使用的掩盖方案相比，阿迪奥斯学到的面具在改善SSL方法的表示方面更有效。

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.

Diabetic Retinopathy (DR) is a leading cause of vision loss in the world, and early DR detection is necessary to prevent vision loss and support an appropriate treatment. In this work, we leverage interactive machine learning and introduce a joint learning framework, termed DRG-Net, to effectively learn both disease grading and multi-lesion segmentation. Our DRG-Net consists of two modules: (i) DRG-AI-System to classify DR Grading, localize lesion areas, and provide visual explanations; (ii) DRG-Expert-Interaction to receive feedback from user-expert and improve the DRG-AI-System. To deal with sparse data, we utilize transfer learning mechanisms to extract invariant feature representations by using Wasserstein distance and adversarial learning-based entropy minimization. Besides, we propose a novel attention strategy at both low- and high-level features to automatically select the most significant lesion information and provide explainable properties. In terms of human interaction, we further develop DRG-Net as a tool that enables expert users to correct the system's predictions, which may then be used to update the system as a whole. Moreover, thanks to the attention mechanism and loss functions constraint between lesion features and classification features, our approach can be robust given a certain level of noise in the feedback of users. We have benchmarked DRG-Net on the two largest DR datasets, i.e., IDRID and FGADR, and compared it to various state-of-the-art deep learning networks. In addition to outperforming other SOTA approaches, DRG-Net is effectively updated using user feedback, even in a weakly-supervised manner.

This paper deals with the problem of statistical and system heterogeneity in a cross-silo Federated Learning (FL) framework where there exist a limited number of Consumer Internet of Things (CIoT) devices in a smart building. We propose a novel Graph Signal Processing (GSP)-inspired aggregation rule based on graph filtering dubbed ``G-Fedfilt''. The proposed aggregator enables a structured flow of information based on the graph's topology. This behavior allows capturing the interconnection of CIoT devices and training domain-specific models. The embedded graph filter is equipped with a tunable parameter which enables a continuous trade-off between domain-agnostic and domain-specific FL. In the case of domain-agnostic, it forces G-Fedfilt to act similar to the conventional Federated Averaging (FedAvg) aggregation rule. The proposed G-Fedfilt also enables an intrinsic smooth clustering based on the graph connectivity without explicitly specified which further boosts the personalization of the models in the framework. In addition, the proposed scheme enjoys a communication-efficient time-scheduling to alleviate the system heterogeneity. This is accomplished by adaptively adjusting the amount of training data samples and sparsity of the models' gradients to reduce communication desynchronization and latency. Simulation results show that the proposed G-Fedfilt achieves up to $3.99\% $ better classification accuracy than the conventional FedAvg when concerning model personalization on the statistically heterogeneous local datasets, while it is capable of yielding up to $2.41\%$ higher accuracy than FedAvg in the case of testing the generalization of the models.

Previous work has shown the potential of deep learning to predict renal obstruction using kidney ultrasound images. However, these image-based classifiers have been trained with the goal of single-visit inference in mind. We compare methods from video action recognition (i.e. convolutional pooling, LSTM, TSM) to adapt single-visit convolutional models to handle multiple visit inference. We demonstrate that incorporating images from a patient's past hospital visits provides only a small benefit for the prediction of obstructive hydronephrosis. Therefore, inclusion of prior ultrasounds is beneficial, but prediction based on the latest ultrasound is sufficient for patient risk stratification.