传统的推荐系统面临两个长期存在的障碍，即数据稀疏性和冷启动问题，这些问题促进了跨域建议（CDR）的出现和发展。 CDR的核心思想是利用从其他领域收集的信息来减轻一个域中的两个问题。在过去的十年中，许多努力进行了跨域建议。最近，随着深度学习和神经网络的发展，出现了许多方法。但是，关于CDR的系统调查数量有限，尤其是关于最新提出的方法以及他们解决的建议方案和建议任务。在本调查文件中，我们首先提出了跨域建议的两级分类法，该分类法对不同的建议方案和建议任务进行了分类。然后，我们以结构化的方式介绍并总结了不同建议方案下的现有跨域推荐方法。我们还组织了常用的数据集。我们通过提供有关该领域的几个潜在研究方向来结束这项调查。

Several self-supervised representation learning methods have been proposed for reinforcement learning (RL) with rich observations. For real-world applications of RL, recovering underlying latent states is crucial, particularly when sensory inputs contain irrelevant and exogenous information. In this work, we study how information bottlenecks can be used to construct latent states efficiently in the presence of task-irrelevant information. We propose architectures that utilize variational and discrete information bottlenecks, coined as RepDIB, to learn structured factorized representations. Exploiting the expressiveness bought by factorized representations, we introduce a simple, yet effective, bottleneck that can be integrated with any existing self-supervised objective for RL. We demonstrate this across several online and offline RL benchmarks, along with a real robot arm task, where we find that compressed representations with RepDIB can lead to strong performance improvements, as the learned bottlenecks help predict only the relevant state while ignoring irrelevant information.

Deep learning classifiers provide the most accurate means of automatically diagnosing diabetic retinopathy (DR) based on optical coherence tomography (OCT) and its angiography (OCTA). The power of these models is attributable in part to the inclusion of hidden layers that provide the complexity required to achieve a desired task. However, hidden layers also render algorithm outputs difficult to interpret. Here we introduce a novel biomarker activation map (BAM) framework based on generative adversarial learning that allows clinicians to verify and understand classifiers decision-making. A data set including 456 macular scans were graded as non-referable or referable DR based on current clinical standards. A DR classifier that was used to evaluate our BAM was first trained based on this data set. The BAM generation framework was designed by combing two U-shaped generators to provide meaningful interpretability to this classifier. The main generator was trained to take referable scans as input and produce an output that would be classified by the classifier as non-referable. The BAM is then constructed as the difference image between the output and input of the main generator. To ensure that the BAM only highlights classifier-utilized biomarkers an assistant generator was trained to do the opposite, producing scans that would be classified as referable by the classifier from non-referable scans. The generated BAMs highlighted known pathologic features including nonperfusion area and retinal fluid. A fully interpretable classifier based on these highlights could help clinicians better utilize and verify automated DR diagnosis.

Safety-critical Autonomous Systems require trustworthy and transparent decision-making process to be deployable in the real world. The advancement of Machine Learning introduces high performance but largely through black-box algorithms. We focus the discussion of explainability specifically with Autonomous Vehicles (AVs). As a safety-critical system, AVs provide the unique opportunity to utilize cutting-edge Machine Learning techniques while requiring transparency in decision making. Interpretability in every action the AV takes becomes crucial in post-hoc analysis where blame assignment might be necessary. In this paper, we provide positioning on how researchers could consider incorporating explainability and interpretability into design and optimization of separate Autonomous Vehicle modules including Perception, Planning, and Control.

Graphic sketch representations are effective for representing sketches. Existing methods take the patches cropped from sketches as the graph nodes, and construct the edges based on sketch's drawing order or Euclidean distances on the canvas. However, the drawing order of a sketch may not be unique, while the patches from semantically related parts of a sketch may be far away from each other on the canvas. In this paper, we propose an order-invariant, semantics-aware method for graphic sketch representations. The cropped sketch patches are linked according to their global semantics or local geometric shapes, namely the synonymous proximity, by computing the cosine similarity between the captured patch embeddings. Such constructed edges are learnable to adapt to the variation of sketch drawings, which enable the message passing among synonymous patches. Aggregating the messages from synonymous patches by graph convolutional networks plays a role of denoising, which is beneficial to produce robust patch embeddings and accurate sketch representations. Furthermore, we enforce a clustering constraint over the embeddings jointly with the network learning. The synonymous patches are self-organized as compact clusters, and their embeddings are guided to move towards their assigned cluster centroids. It raises the accuracy of the computed synonymous proximity. Experimental results show that our method significantly improves the performance on both controllable sketch synthesis and sketch healing.

Unsupervised domain adaptation (UDA) has been highly successful in transferring knowledge acquired from a label-rich source domain to a label-scarce target domain. Open-set domain adaptation (ODA) and universal domain adaptation (UNDA) have been proposed as solutions to the problem concerning the presence of additional novel categories in the target domain. Existing ODA and UNDA approaches treat all novel categories as one unified unknown class and attempt to detect this unknown class during the training process. We find that domain variance leads to more significant view-noise in unsupervised data augmentation, affecting the further applications of contrastive learning~(CL), as well as the current closed-set classifier and open-set classifier causing the model to be overconfident in novel class discovery. To address the above two issues, we propose Soft-contrastive All-in-one Network~(SAN) for ODA and UNDA tasks. SAN includes a novel data-augmentation-based CL loss, which is used to improve the representational capability, and a more human-intuitive classifier, which is used to improve the new class discovery capability. The soft contrastive learning~(SCL) loss is used to weaken the adverse effects of the data-augmentation label noise problem, which is amplified in domain transfer. The All-in-One~(AIO) classifier overcomes the overconfidence problem of the current mainstream closed-set classifier and open-set classifier in a more human-intuitive way. The visualization results and ablation experiments demonstrate the importance of the two proposed innovations. Moreover, extensive experimental results on ODA and UNDA show that SAN has advantages over the existing state-of-the-art methods.

The miniaturization and mobility of computer vision systems are limited by the heavy computational burden and the size of optical lenses. Here, we propose to use a ultra-thin diffractive optical element to implement passive optical convolution. A division adjoint opto-electronic co-design method is also proposed. In our simulation experiments, the first few convolutional layers of the neural network can be replaced by optical convolution in a classification task on the CIFAR-10 dataset with no power consumption, while similar performance can be obtained.

机器人定位是使用地图和传感器测量结果找到机器人姿势的反问题。近年来，可逆神经网络（INNS）成功地解决了各个领域的模棱两可的反问题。本文提出了一个解决旅馆本地化问题的框架。我们设计了一个在逆路径中提供隐式映射表示形式的旅馆。通过对评估中的潜在空间进行采样，局部\ _inn输出机器人以协方差构成，可用于估计不确定性。我们表明，本地\ _inn的本地化性能与延迟较低的当前方法相当。我们使用训练集的外观显示了从本地\ _inn的详细的2D和3D地图重建。我们还使用本地\ _inn提供了全球本地化算法来解决绑架问题。

尽管机器人学课程在高等教育方面已建立，但这些课程通常专注于理论，有时缺乏对开发，部署和将软件应用于真实硬件的技术的系统覆盖。此外，大多数用于机器人教学的硬件平台是针对中学水平的年轻学生的低级玩具。为了解决这一差距，开发了一个自动驾驶汽车硬件平台，称为第1 f1 f1tth，用于教授自动驾驶系统。本文介绍了以“赛车”和替换考试的竞赛为主题的各种教育水平教学模块和软件堆栈。第1辆车提供了一个模块化硬件平台及其相关软件，用于教授自动驾驶算法的基础知识。从基本的反应方法到高级计划算法，教学模块通过使用第1辆车的自动驾驶来增强学生的计算思维。第1辆汽车填补了研究平台和低端玩具车之间的空白，并提供了学习自主系统中主题的动手经验。多年的四所大学为他们的学期本科和研究生课程采用了教学模块。学生反馈用于分析第1个平台的有效性。超过80％的学生强烈同意，硬件平台和模块大大激发了他们的学习，而超过70％的学生强烈同意，硬件增强了他们对学科的理解。调查结果表明，超过80％的学生强烈同意竞争激励他们参加课程。

我们提出了针对微小神经网络的域概括（DG）的系统研究，这个问题对于机上机器学习应用至关重要，但在研究仅针对大型模型的文献中被忽略了。微小的神经网络具有较少的参数和较低的复杂性，因此不应以与DG应用的大型同行相同的方式进行训练。我们发现知识蒸馏是解决问题的有力候选者：它优于使用具有较大利润的大型模型开发的最先进的DG方法。此外，我们观察到，与域移动有关的测试数据上的教师学生绩效差距大于分布数据的绩效差距。为了改善微小神经网络而不增加部署成本的DG，我们提出了一个简单的想法，称为分布外知识蒸馏（OKD），该想法旨在教导学生如何处理（综合）分发数据和分布数据和被证明是解决问题的有前途的框架。我们还为创建DG数据集的可扩展方法（在上下文中称为域移动（DOSCO））提供了可扩展的方法，该数据可以在不大量努力的情况下按大规模应用大量数据。代码和模型以\ url {https://github.com/kaiyangzhou/on-device-dg}发布。