人们对算法偏见风险的认识越来越多，促进了围绕偏见缓解策略的努力。大多数提议的方法都属于两个类别之一：（1）对预测模型施加算法公平限制，以及（2）收集其他培训样本。最近以及在这两个类别的交集中，已经开发了在公平限制下提出主动学习的方法。但是，提出的缓解策略通常忽略了观察到的标签中呈现的偏差。在这项工作中，我们研究了在有标签偏见的情况下对主动数据收集策略的公平考虑。我们首先概述了在监督学习系统的背景下，不同类型的标签偏差。然后，我们从经验上表明，当忽略标签偏差时，收集更多数据会加剧偏见，并施加依赖数据收集过程中观察到的标签的公平约束可能无法解决问题。我们的结果说明了部署试图减轻单一类型偏见的模型的意外后果数据收集期间的偏差。

顺序推荐是推荐系统的广泛流行的主题。现有的作品有助于提高基于各种方法的顺序推荐系统的预测能力，例如经常性网络和自我关注机制。然而，他们未能发现和区分项目之间的各种关系，这可能是激励用户行为的潜在因素。在本文中，我们提出了一个边缘增强的全面解散图神经网络（EGD-GNN）模型，以捕获全局项目表示和本地用户意图学习项目之间的关系信息。在全球级别，我们通过所有序列构建全局链接图来模拟项目关系。然后，频道感知的解缠绕学习层被设计成将边缘信息分解为不同的信道，这可以聚合以将目标项从其邻居表示。在本地层面，我们应用一个变化的自动编码器框架来学习用户在当前序列上的意图。我们在三个现实世界数据集中评估我们提出的方法。实验结果表明，我们的模型可以通过最先进的基线获得至关重要的改进，能够区分项目特征。

Instead of mining coherent topics from a given text corpus in a completely unsupervised manner, seed-guided topic discovery methods leverage user-provided seed words to extract distinctive and coherent topics so that the mined topics can better cater to the user's interest. To model the semantic correlation between words and seeds for discovering topic-indicative terms, existing seed-guided approaches utilize different types of context signals, such as document-level word co-occurrences, sliding window-based local contexts, and generic linguistic knowledge brought by pre-trained language models. In this work, we analyze and show empirically that each type of context information has its value and limitation in modeling word semantics under seed guidance, but combining three types of contexts (i.e., word embeddings learned from local contexts, pre-trained language model representations obtained from general-domain training, and topic-indicative sentences retrieved based on seed information) allows them to complement each other for discovering quality topics. We propose an iterative framework, SeedTopicMine, which jointly learns from the three types of contexts and gradually fuses their context signals via an ensemble ranking process. Under various sets of seeds and on multiple datasets, SeedTopicMine consistently yields more coherent and accurate topics than existing seed-guided topic discovery approaches.

Given a few seed entities of a certain type (e.g., Software or Programming Language), entity set expansion aims to discover an extensive set of entities that share the same type as the seeds. Entity set expansion in software-related domains such as StackOverflow can benefit many downstream tasks (e.g., software knowledge graph construction) and facilitate better IT operations and service management. Meanwhile, existing approaches are less concerned with two problems: (1) How to deal with multiple types of seed entities simultaneously? (2) How to leverage the power of pre-trained language models (PLMs)? Being aware of these two problems, in this paper, we study the entity set co-expansion task in StackOverflow, which extracts Library, OS, Application, and Language entities from StackOverflow question-answer threads. During the co-expansion process, we use PLMs to derive embeddings of candidate entities for calculating similarities between entities. Experimental results show that our proposed SECoExpan framework outperforms previous approaches significantly.

本文研究了以任务为导向的对话系统中的曝光偏差问题，其中模型在多个转弯中生成的内容驱动对话框上下文远离训练时间的地面真相分布，从而引入了错误传播并损害了TOD系统的稳健性。为了弥合训练和推理多转弯任务导向对话框之间的差距，我们建议会话级抽样，该采样将模型明确地暴露于培训期间对话框上下文的采样生成的内容。此外，我们采用基于辍学的一致性正规化与屏蔽策略R掩码，以进一步提高模型的鲁棒性和性能。拟议的UBARV2在标准化评估基准Multiwoz上实现了最先进的性能，并且广泛的实验显示了所提出的方法的有效性。

半监督的对象检测在平均教师驱动的自我训练的发展中取得了重大进展。尽管结果有令人鼓舞，但在先前的工作中尚未完全探索标签不匹配问题，从而导致自训练期间严重确认偏见。在本文中，我们从两个不同但互补的角度（即分布级别和实例级别）提出了一个简单而有效的标签框架。对于前者，根据Monte Carlo采样，可以合理地近似来自标记数据的未标记数据的类分布。在这种弱监督提示的指导下，我们引入了一个重新分配卑鄙的老师，该老师利用自适应标签 - 分布意识到的信心阈值来生成无偏见的伪标签来推动学生学习。对于后一个，存在着跨教师模型的被忽视的标签分配歧义问题。为了解决这个问题，我们提出了一种新的标签分配机制，用于自我训练框架，即提案自我分配，该机制将学生的建议注入教师，并生成准确的伪标签，以相应地匹配学生模型中的每个建议。 MS-Coco和Pascal-VOC数据集的实验证明了我们提出的框架与其他最先进的框架相当优越。代码将在https://github.com/hikvision-research/ssod上找到。

新闻库中的自动事件检测是开采快速发展的结构化知识的至关重要的任务。由于现实世界事件具有不同的粒度，从顶级主题到关键事件，然后再提及与具体行动相对应的事件，因此通常有两条研究：（1）主题检测从新闻语料库的主要主题中标识（例如，。 ，“ 2019年香港抗议活动”与“ 2020年美国总统大选”），具有非常不同的语义； （2）从一份文件提取的行动提取提取级别的行动（例如，“警察击中抗议者的左臂”），无法理解该事件。在本文中，我们提出了一项新任务，即在中间级别的关键事件检测，目的是从新闻语料库的关键事件（例如，“ 8月12日至14日的HK机场抗议”）中进行检测，每一次都发生在特定时间/位置并专注于同一主题。由于新闻文章的快速发展性质，这项任务可以弥合事件的理解和结构，并且由于关键事件的主题和时间紧密以及标记的数据的稀缺而具有固有的挑战。为了应对这些挑战，我们开发了一个无监督的关键事件检测框架Evmine，（1）使用新颖的TTF-ITF分数提取时间频繁的峰值短语，（2）将峰值短语合并为事件 - 指示特征集，通过从我们的我们检测我们的社区中。设计的峰短语图可以捕获文档的共发生，语义相似性和时间亲密信号，以及（3）迭代地检索与每个关键事件相关的文档，通过训练具有从事件指标特征集中自动生成的伪标签的分类器，并完善该分类器使用检索的文档检测到关键事件。广泛的实验和案例研究表明，Evmine的表现优于所有基线方法及其在两个现实世界新闻机构上的消融。

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.

Dataset distillation has emerged as a prominent technique to improve data efficiency when training machine learning models. It encapsulates the knowledge from a large dataset into a smaller synthetic dataset. A model trained on this smaller distilled dataset can attain comparable performance to a model trained on the original training dataset. However, the existing dataset distillation techniques mainly aim at achieving the best trade-off between resource usage efficiency and model utility. The security risks stemming from them have not been explored. This study performs the first backdoor attack against the models trained on the data distilled by dataset distillation models in the image domain. Concretely, we inject triggers into the synthetic data during the distillation procedure rather than during the model training stage, where all previous attacks are performed. We propose two types of backdoor attacks, namely NAIVEATTACK and DOORPING. NAIVEATTACK simply adds triggers to the raw data at the initial distillation phase, while DOORPING iteratively updates the triggers during the entire distillation procedure. We conduct extensive evaluations on multiple datasets, architectures, and dataset distillation techniques. Empirical evaluation shows that NAIVEATTACK achieves decent attack success rate (ASR) scores in some cases, while DOORPING reaches higher ASR scores (close to 1.0) in all cases. Furthermore, we conduct a comprehensive ablation study to analyze the factors that may affect the attack performance. Finally, we evaluate multiple defense mechanisms against our backdoor attacks and show that our attacks can practically circumvent these defense mechanisms.

Automatic music generation with artificial intelligence typically requires a large amount of data which is hard to obtain for many less common genres and musical instruments. To tackle this issue, we present ongoing work and preliminary findings on the possibility for deep models to transfer knowledge from language to music, by finetuning large language models pre-trained on a massive text corpus on only hundreds of MIDI files of drum performances. We show that by doing so, one of the largest, state-of-the-art models (GPT3) is capable of generating reasonable drum grooves, while models that are not pre-trained (Transformer) shows no such ability beyond naive repetition. Evaluating generated music is a challenging task, more so is evaluating drum grooves with little precedence in literature. Hence, we propose a tailored structural evaluation method and analyze drum grooves produced by GPT3 compared to those played by human professionals, exposing the strengths and weaknesses of such generation by language-to-music transfer. Our findings suggest that language-to-music transfer learning with large language models is viable and promising.