基于模型的增强学习（RL）通过学习动态模型来生成用于策略学习的样本，在实践中实现了实践中的样本效率更高。先前的作品学习了一个“全球”动力学模型，以适合所有历史政策的国家行动探视分布。但是，在本文中，我们发现学习全球动力学模型并不一定会受益于当前策略的模型预测，因为使用的策略正在不断发展。培训期间不断发展的政策将导致州行动探访分配变化。我们理论上分析了历史政策的分布如何影响模型学习和模型推出。然后，我们提出了一种基于模型的新型RL方法，名为\ textit {策略适应模型基于contor-Critic（PMAC）}，该方法基于策略适应机制学习了一个基于策略适应的动力学模型。该机制会动态调整历史政策混合分布，以确保学习模型可以不断适应不断发展的政策的国家行动探视分布。在Mujoco中的一系列连续控制环境上进行的实验表明，PMAC可以实现最新的渐近性能，而样品效率几乎是基于模型的方法的两倍。

在许多增强学习（RL）应用中，观察空间由人类开发人员指定并受到物理实现的限制，因此可能会随时间的巨大变化（例如，观察特征的数量增加）。然而，当观察空间发生变化时，前一项策略可能由于输入特征不匹配而失败，并且另一个策略必须从头开始培训，这在计算和采样复杂性方面效率低。在理论上见解之后，我们提出了一种新颖的算法，该算法提取源任务中的潜在空间动态，并将动态模型传送到目标任务用作基于模型的常规程序。我们的算法适用于观察空间的彻底变化（例如，从向量的基于矢量的观察到图像的观察），没有任何任务映射或目标任务的任何先前知识。实证结果表明，我们的算法显着提高了目标任务中学习的效率和稳定性。

对心理健康支持的需求不断增长，强调了对话代理在全球和中国作为人类支持者的重要性。这些代理可以增加可用性并降低心理健康支持的相对成本。提供的支持可以分为两种主要类型：认知和情感支持。关于该主题的现有工作主要集中在采用认知行为疗法（CBT）原理的构造药物上。此类代理根据预定义的模板和练习来运行，以提供认知支持。但是，使用此类药物对情绪支持的研究是有限的。此外，大多数建设的代理商都以英语运作，强调了在中国进行此类研究的重要性。在这项研究中，我们分析了表情符疾病在减少精神痛苦症状方面的有效性。 Emohaa是一种对话剂，通过基于CBT的练习和指导性对话提供认知支持。它还通过使用户能够发泄所需的情绪问题来支持情感上的支持。该研究包括134名参与者，分为三组：Emohaa（基于CBT），Emohaa（Full）和控制。实验结果表明，与对照组相比，使用Emohaa的参与者在精神困扰症状方面的改善得到了更大的改善。我们还发现，添加情感支持剂对这种改善，主要是抑郁和失眠有互补的影响。根据获得的结果和参与者对平台的满意，我们得出结论，Emohaa是减少精神困扰的实用和有效工具。

很少有课堂学习（FSCIL）旨在仅用几个样本不断学习新概念，这很容易遭受灾难性的遗忘和过度拟合的问题。旧阶级的无法获得性和新颖样本的稀缺性使实现保留旧知识和学习新颖概念之间的权衡很大。受到不同模型的启发，我们在学习新颖概念时记住了不同的知识，我们提出了一个记忆的补充网络（MCNET），以整合多个模型，以在新任务中相互补充不同的记忆知识。此外，为了用很少的新样本更新模型，我们开发了一个原型平滑的硬矿三元组（PSHT）损失，以将新型样品不仅在当前任务中彼此远离，而且在旧分布中脱颖而出。在三个基准数据集（例如CIFAR100，Miniimagenet和Cub200）上进行了广泛的实验，证明了我们提出的方法的优势。

最近，大脑网络已被广泛采用来研究脑动力学，脑发育和脑部疾病。大脑功能网络上的图表学习技术可以促进发现用于临床表型和神经退行性疾病的新型生物标志物。但是，当前的图形学习技术在大脑网络挖掘上存在几个问题。首先，大多数当前的图形学习模型都是为无符号图设计的，这阻碍了对许多签名网络数据（例如大脑功能网络）的分析。同时，大脑网络数据的不足限制了临床表型预测的模型性能。此外，当前的图形学习模型很少是可以解释的，这可能无法为模型结果提供生物学见解。在这里，我们提出了一个可解释的层次签名的图形表示模型，以从大脑功能网络中提取图形表示，可用于不同的预测任务。为了进一步提高模型性能，我们还提出了一种新策略，以增强功能性脑网络数据以进行对比学习。我们使用HCP和OASIS的数据评估了有关不同分类和回归任务的框架。我们来自广泛的实验的结果表明，与几种最新技术相比，该模型的优越性。此外，我们使用从这些预测任务得出的图形显着性图来证明表型生物标志物的检测和解释。

In this paper, we propose a robust 3D detector, named Cross Modal Transformer (CMT), for end-to-end 3D multi-modal detection. Without explicit view transformation, CMT takes the image and point clouds tokens as inputs and directly outputs accurate 3D bounding boxes. The spatial alignment of multi-modal tokens is performed implicitly, by encoding the 3D points into multi-modal features. The core design of CMT is quite simple while its performance is impressive. CMT obtains 73.0% NDS on nuScenes benchmark. Moreover, CMT has a strong robustness even if the LiDAR is missing. Code will be released at https://github.com/junjie18/CMT.

Knowledge graphs (KG) have served as the key component of various natural language processing applications. Commonsense knowledge graphs (CKG) are a special type of KG, where entities and relations are composed of free-form text. However, previous works in KG completion and CKG completion suffer from long-tail relations and newly-added relations which do not have many know triples for training. In light of this, few-shot KG completion (FKGC), which requires the strengths of graph representation learning and few-shot learning, has been proposed to challenge the problem of limited annotated data. In this paper, we comprehensively survey previous attempts on such tasks in the form of a series of methods and applications. Specifically, we first introduce FKGC challenges, commonly used KGs, and CKGs. Then we systematically categorize and summarize existing works in terms of the type of KGs and the methods. Finally, we present applications of FKGC models on prediction tasks in different areas and share our thoughts on future research directions of FKGC.

Few Shot Instance Segmentation (FSIS) requires models to detect and segment novel classes with limited several support examples. In this work, we explore a simple yet unified solution for FSIS as well as its incremental variants, and introduce a new framework named Reference Twice (RefT) to fully explore the relationship between support/query features based on a Transformer-like framework. Our key insights are two folds: Firstly, with the aid of support masks, we can generate dynamic class centers more appropriately to re-weight query features. Secondly, we find that support object queries have already encoded key factors after base training. In this way, the query features can be enhanced twice from two aspects, i.e., feature-level and instance-level. In particular, we firstly design a mask-based dynamic weighting module to enhance support features and then propose to link object queries for better calibration via cross-attention. After the above steps, the novel classes can be improved significantly over our strong baseline. Additionally, our new framework can be easily extended to incremental FSIS with minor modification. When benchmarking results on the COCO dataset for FSIS, gFSIS, and iFSIS settings, our method achieves a competitive performance compared to existing approaches across different shots, e.g., we boost nAP by noticeable +8.2/+9.4 over the current state-of-the-art FSIS method for 10/30-shot. We further demonstrate the superiority of our approach on Few Shot Object Detection. Code and model will be available.

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.

This paper focuses on designing efficient models with low parameters and FLOPs for dense predictions. Even though CNN-based lightweight methods have achieved stunning results after years of research, trading-off model accuracy and constrained resources still need further improvements. This work rethinks the essential unity of efficient Inverted Residual Block in MobileNetv2 and effective Transformer in ViT, inductively abstracting a general concept of Meta-Mobile Block, and we argue that the specific instantiation is very important to model performance though sharing the same framework. Motivated by this phenomenon, we deduce a simple yet efficient modern \textbf{I}nverted \textbf{R}esidual \textbf{M}obile \textbf{B}lock (iRMB) for mobile applications, which absorbs CNN-like efficiency to model short-distance dependency and Transformer-like dynamic modeling capability to learn long-distance interactions. Furthermore, we design a ResNet-like 4-phase \textbf{E}fficient \textbf{MO}del (EMO) based only on a series of iRMBs for dense applications. Massive experiments on ImageNet-1K, COCO2017, and ADE20K benchmarks demonstrate the superiority of our EMO over state-of-the-art methods, \eg, our EMO-1M/2M/5M achieve 71.5, 75.1, and 78.4 Top-1 that surpass \textbf{SoTA} CNN-/Transformer-based models, while trading-off the model accuracy and efficiency well.