存在预训练模型在各种文本分类任务上取得了最先进的性能。这些模型已被证明可用于学习普遍语言表示。然而，通过先进的预训练模型无法有效地区分类似文本之间的语义差异，这对难以区分类的性能产生了很大的影响。为了解决这个问题，我们在这项工作中提出了一种与标签距离（CLLD）的新型对比学习。灵感来自最近对比学习的进步，我们专门设计了一种具有标签距离的分类方法，用于学习对比类。 CLLD可确保在导致不同标签分配的细微差别中的灵活性，并为同时具有相似性的每个类生成不同的表示。关于公共基准和内部数据集的广泛实验表明，我们的方法提高了预先训练模型在分类任务上的性能。重要的是，我们的实验表明，学习的标签距离减轻了细胞的对抗性质。

自2016年成立以来，Alexa奖计划使数百名大学生能够通过Socialbot Grand Challenge探索和竞争以发展对话代理商。挑战的目的是建立能够与人类在流行主题上连贯而诱人的代理人20分钟，同时达到至少4.0/5.0的平均评分。但是，由于对话代理商试图帮助用户完成日益复杂的任务，因此需要新的对话AI技术和评估平台。成立于2021年的Alexa奖Taskbot Challenge建立在Socialbot Challenge的成功基础上，通过引入交互式协助人类进行现实世界烹饪和做自己动手做的任务的要求，同时同时使用语音和视觉方式。这项挑战要求TaskBots识别和理解用户的需求，识别和集成任务和域知识，并开发新的方式，不分散用户的注意力，而不必分散他们的任务，以及其他挑战。本文概述了Taskbot挑战赛，描述了使用Cobot Toolkit提供给团队提供的基础架构支持，并总结了参与团队以克服研究挑战所采取的方法。最后，它分析了比赛第一年的竞争任务机器人的性能。

人工智能（AI）辅助方法在风险领域（例如疾病诊断）受到了很多关注。与疾病类型的分类不同，将医学图像归类为良性或恶性肿瘤是一项精细的任务。但是，大多数研究仅着重于提高诊断准确性，而忽略了模型可靠性的评估，从而限制了其临床应用。对于临床实践，校准对过度参数化的模型和固有的噪声极为明显地提出了低数据表格的主要挑战。特别是，我们发现建模与数据相关的不确定性更有利于置信度校准。与测试时间增强（TTA）相比，我们通过混合数据增强策略提出了一个修改后的自举损失（BS损耗）功能，可以更好地校准预测性不确定性并捕获数据分布转换而无需额外推断时间。我们的实验表明，与标准数据增强，深度集合和MC辍学相比，混合（BSM）模型的BS损失（BSM）模型可以将预期校准误差（ECE）减半。在BSM模型下，不确定性与相似性之间的相关性高达-0.4428。此外，BSM模型能够感知室外数据的语义距离，这表明在现实世界中的临床实践中潜力很高。

在本文中，我们以高效统一的方式呈现Grecx，这是一种开源Tensorflow框架，用于基于GNN的推荐模型。 GreCX由核心库组成，用于构建基于GNN的推荐基准，以及基于流行的基于GNN的推荐模型的实现。核心库为构建高效和统一的基准，包括FastMetrics（高效度量计算库），VectorSearch（密集向量的高效相似性搜索库），Batcheval（高效迷你批量评估库）和DataManager（Unified DataSet Management库（Unified DataSet Management库） ）。特别是为提供统一的基准测试，以进行不同复杂的基于GNN的推荐模型的公平比较，我们设计了一种新的公制GRMF-X并将其集成到FastMetrics组件中。基于Tensorflow GNN Library TF_Geometric，Grecx仔细实现了各种基于GNN的推荐模型。我们仔细实施了这些基线模型来重现文献中报告的性能，我们的实现通常更有效和友好。总之，GreCX使用来以高效统一的方式培训和基于GNN的推荐基线。我们与Grecx进行实验，实验结果表明，Grecx允许我们以有效和统一的方式培训和基于GNN的推荐基准。 grecx的源代码可在https://github.com/maenzhier/grecx上获得。

使用产量曲线预测核肉的文献通常使用10年 - 三个月的财政收益率，而无需验证该对选择。本研究通过让机器学习算法识别最佳成熟度对和系数来调查是否可以改善传播的预测能力。我们的综合分析表明，由于估计误差，即尽管有可能增益，机器学习方法不会显着提高预测。这对预测地平线，控制变量，样品期和经济衰退观察的过采样是强大的。我们的发现支持使用10年 - 三个月的传播。

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.