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。

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。

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。

Masked image modeling (MIM) performs strongly in pre-training large vision Transformers (ViTs). However, small models that are critical for real-world applications cannot or only marginally benefit from this pre-training approach. In this paper, we explore distillation techniques to transfer the success of large MIM-based pre-trained models to smaller ones. We systematically study different options in the distillation framework, including distilling targets, losses, input, network regularization, sequential distillation, etc, revealing that: 1) Distilling token relations is more effective than CLS token- and feature-based distillation; 2) An intermediate layer of the teacher network as target perform better than that using the last layer when the depth of the student mismatches that of the teacher; 3) Weak regularization is preferred; etc. With these findings, we achieve significant fine-tuning accuracy improvements over the scratch MIM pre-training on ImageNet-1K classification, using all the ViT-Tiny, ViT-Small, and ViT-base models, with +4.2%/+2.4%/+1.4% gains, respectively. Our TinyMIM model of base size achieves 52.2 mIoU in AE20K semantic segmentation, which is +4.1 higher than the MAE baseline. Our TinyMIM model of tiny size achieves 79.6% top-1 accuracy on ImageNet-1K image classification, which sets a new record for small vision models of the same size and computation budget. This strong performance suggests an alternative way for developing small vision Transformer models, that is, by exploring better training methods rather than introducing inductive biases into architectures as in most previous works. Code is available at https://github.com/OliverRensu/TinyMIM.

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.

Blind image quality assessment (BIQA) remains challenging due to the diversity of distortion and image content variation, which complicate the distortion patterns crossing different scales and aggravate the difficulty of the regression problem for BIQA. However, existing BIQA methods often fail to consider multi-scale distortion patterns and image content, and little research has been done on learning strategies to make the regression model produce better performance. In this paper, we propose a simple yet effective Progressive Multi-Task Image Quality Assessment (PMT-IQA) model, which contains a multi-scale feature extraction module (MS) and a progressive multi-task learning module (PMT), to help the model learn complex distortion patterns and better optimize the regression issue to align with the law of human learning process from easy to hard. To verify the effectiveness of the proposed PMT-IQA model, we conduct experiments on four widely used public datasets, and the experimental results indicate that the performance of PMT-IQA is superior to the comparison approaches, and both MS and PMT modules improve the model's performance.

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.