Graph Neural Networks (GNNs) have shown satisfying performance on various graph learning tasks. To achieve better fitting capability, most GNNs are with a large number of parameters, which makes these GNNs computationally expensive. Therefore, it is difficult to deploy them onto edge devices with scarce computational resources, e.g., mobile phones and wearable smart devices. Knowledge Distillation (KD) is a common solution to compress GNNs, where a light-weighted model (i.e., the student model) is encouraged to mimic the behavior of a computationally expensive GNN (i.e., the teacher GNN model). Nevertheless, most existing GNN-based KD methods lack fairness consideration. As a consequence, the student model usually inherits and even exaggerates the bias from the teacher GNN. To handle such a problem, we take initial steps towards fair knowledge distillation for GNNs. Specifically, we first formulate a novel problem of fair knowledge distillation for GNN-based teacher-student frameworks. Then we propose a principled framework named RELIANT to mitigate the bias exhibited by the student model. Notably, the design of RELIANT is decoupled from any specific teacher and student model structures, and thus can be easily adapted to various GNN-based KD frameworks. We perform extensive experiments on multiple real-world datasets, which corroborates that RELIANT achieves less biased GNN knowledge distillation while maintaining high prediction utility.

Graph Machine Learning最近在学术界和行业中都引起了人们的关注。大多数图形机器学习模型，例如图形神经网络（GNN），都经过大量的图形数据训练。但是，在许多实际情况下，例如医疗保健系统中的住院预测，图形数据通常存储在多个数据所有者中，并且由于隐私问题和法规限制，任何其他方都无法直接访问。联合图机器学习（FGML）是一种有前途的解决方案，可以通过以联合方式训练图机学习模型来应对这一挑战。在这项调查中，我们对FGML文献进行了全面的综述。具体而言，我们首先提供了一种新的分类法，将FGML中的现有问题分为两个设置，即，\ emph {fl带有结构化数据}和\ emph {结构化的fl}。然后，我们回顾每种环境中的主流技术，并详细介绍它们如何应对FGML下的挑战。此外，我们总结了来自不同域中FGML的现实应用程序，并介绍FGML中采用的开放图数据集和平台。最后，我们在现有研究中提出了一些局限性，并在该领域的研究方向有前途的方向。

图形神经网络（GNN）表现出令人满意的各种图分析问题的性能。因此，在各种决策方案中，它们已成为\ emph {de exto}解决方案。但是，GNN可以针对某些人口亚组产生偏差的结果。最近的一些作品在经验上表明，输入网络的偏见结构是GNN的重要来源。然而，没有系统仔细检查输入网络结构的哪一部分会导致对任何给定节点的偏见预测。对输入网络的结构如何影响GNN结果的偏见的透明度很大，在很大程度上限制了在各种决策方案中的安全采用GNN。在本文中，我们研究了GNN中偏见的结构解释的新研究问题。具体而言，我们提出了一个新颖的事后解释框架，以识别可以最大程度地解释出偏见的两个边缘集，并最大程度地促进任何给定节点的GNN预测的公平水平。这种解释不仅提供了对GNN预测的偏见/公平性的全面理解，而且在建立有效但公平的GNN模型方面具有实际意义。对现实世界数据集的广泛实验验证了拟议框架在为GNN偏见提供有效的结构解释方面的有效性。可以在https://github.com/yushundong/referee上找到开源代码。

图神经网络（GNN）在图形上学习节点表示方面表现出很大的力量。但是，他们可能会从训练数据中继承历史偏见，从而导致预测的歧视性偏见。尽管某些工作已经开发出公平的GNN，但其中大多数直接从非图形域借用了公平代表性学习技术，而没有考虑GNN中特征传播引起的敏感属性泄漏的潜在问题。但是，我们从经验上观察到，特征传播可能会改变以前无害特征与敏感特征的相关性。这可以看作是敏感信息的泄漏，可以进一步加剧预测中的歧视。因此，我们根据特征相关性设计了两个特征掩盖策略，以突出考虑特征传播和相关性变化在减轻歧视中的重要性。通过我们的分析，我们提出了公平视图图神经网络（FAIRVGNN），以通过自动识别和掩盖敏感的相关特征来生成特征的公平视图，以考虑特征传播后的相关变化。鉴于博学的公平视图，我们适应编码器的夹紧权重，以避免使用敏感相关的功能。现实世界数据集的实验表明，Fairvgnn在模型实用程序和公平性之间取得了更好的权衡。我们的代码可在https://github.com/yuwvandy/fairvgnn上公开获取。

Despite significant progress in object categorization, in recent years, a number of important challenges remain; mainly, the ability to learn from limited labeled data and to recognize object classes within large, potentially open, set of labels. Zero-shot learning is one way of addressing these challenges, but it has only been shown to work with limited sized class vocabularies and typically requires separation between supervised and unsupervised classes, allowing former to inform the latter but not vice versa. We propose the notion of vocabulary-informed learning to alleviate the above mentioned challenges and address problems of supervised, zero-shot, generalized zero-shot and open set recognition using a unified framework. Specifically, we propose a weighted maximum margin framework for semantic manifold-based recognition that incorporates distance constraints from (both supervised and unsupervised) vocabulary atoms. Distance constraints ensure that labeled samples are projected closer to their correct prototypes, in the embedding space, than to others. We illustrate that resulting model shows improvements in supervised, zero-shot, generalized zero-shot, and large open set recognition, with up to 310K class vocabulary on Animal with Attributes and ImageNet datasets.

Advances in computer vision and machine learning techniques have led to significant development in 2D and 3D human pose estimation from RGB cameras, LiDAR, and radars. However, human pose estimation from images is adversely affected by occlusion and lighting, which are common in many scenarios of interest. Radar and LiDAR technologies, on the other hand, need specialized hardware that is expensive and power-intensive. Furthermore, placing these sensors in non-public areas raises significant privacy concerns. To address these limitations, recent research has explored the use of WiFi antennas (1D sensors) for body segmentation and key-point body detection. This paper further expands on the use of the WiFi signal in combination with deep learning architectures, commonly used in computer vision, to estimate dense human pose correspondence. We developed a deep neural network that maps the phase and amplitude of WiFi signals to UV coordinates within 24 human regions. The results of the study reveal that our model can estimate the dense pose of multiple subjects, with comparable performance to image-based approaches, by utilizing WiFi signals as the only input. This paves the way for low-cost, broadly accessible, and privacy-preserving algorithms for human sensing.

With the increasing ability of large language models (LLMs), in-context learning (ICL) has become a new paradigm for natural language processing (NLP), where LLMs make predictions only based on contexts augmented with a few training examples. It has been a new trend exploring ICL to evaluate and extrapolate the ability of LLMs. In this paper, we aim to survey and summarize the progress, challenges, and future work in ICL. We first present a formal definition of ICL and clarify its correlation to related studies. Then, we organize and discuss advanced techniques of ICL, including training strategies, prompting strategies, and so on. Finally, we present the challenges of ICL and provide potential directions for further research. We hope our work can encourage more research on uncovering how ICL works and improving ICL in future work.

Designing better deep networks and better reinforcement learning (RL) algorithms are both important for deep RL. This work focuses on the former. Previous methods build the network with several modules like CNN, LSTM and Attention. Recent methods combine the Transformer with these modules for better performance. However, it requires tedious optimization skills to train a network composed of mixed modules, making these methods inconvenient to be used in practice. In this paper, we propose to design \emph{pure Transformer-based networks} for deep RL, aiming at providing off-the-shelf backbones for both the online and offline settings. Specifically, the Transformer in Transformer (TIT) backbone is proposed, which cascades two Transformers in a very natural way: the inner one is used to process a single observation, while the outer one is responsible for processing the observation history; combining both is expected to extract spatial-temporal representations for good decision-making. Experiments show that TIT can achieve satisfactory performance in different settings, consistently.

Recently the deep learning has shown its advantage in representation learning and clustering for time series data. Despite the considerable progress, the existing deep time series clustering approaches mostly seek to train the deep neural network by some instance reconstruction based or cluster distribution based objective, which, however, lack the ability to exploit the sample-wise (or augmentation-wise) contrastive information or even the higher-level (e.g., cluster-level) contrastiveness for learning discriminative and clustering-friendly representations. In light of this, this paper presents a deep temporal contrastive clustering (DTCC) approach, which for the first time, to our knowledge, incorporates the contrastive learning paradigm into the deep time series clustering research. Specifically, with two parallel views generated from the original time series and their augmentations, we utilize two identical auto-encoders to learn the corresponding representations, and in the meantime perform the cluster distribution learning by incorporating a k-means objective. Further, two levels of contrastive learning are simultaneously enforced to capture the instance-level and cluster-level contrastive information, respectively. With the reconstruction loss of the auto-encoder, the cluster distribution loss, and the two levels of contrastive losses jointly optimized, the network architecture is trained in a self-supervised manner and the clustering result can thereby be obtained. Experiments on a variety of time series datasets demonstrate the superiority of our DTCC approach over the state-of-the-art.

Active tracking of space noncooperative object that merely relies on vision camera is greatly significant for autonomous rendezvous and debris removal. Considering its Partial Observable Markov Decision Process (POMDP) property, this paper proposes a novel tracker based on deep recurrent reinforcement learning, named as RAMAVT which drives the chasing spacecraft to follow arbitrary space noncooperative object with high-frequency and near-optimal velocity control commands. To further improve the active tracking performance, we introduce Multi-Head Attention (MHA) module and Squeeze-and-Excitation (SE) layer into RAMAVT, which remarkably improve the representative ability of neural network with almost no extra computational cost. Extensive experiments and ablation study implemented on SNCOAT benchmark show the effectiveness and robustness of our method compared with other state-of-the-art algorithm. The source codes are available on https://github.com/Dongzhou-1996/RAMAVT.