在大规模统计学习中，子采样或子数据选择是一种有用的方法。大多数现有研究的重点是基于模型的亚采样方法，这些方法显着取决于模型假设。在本文中，我们考虑了从原始完整数据中生成子数据的无模型亚采样策略。为了衡量subdata在原始数据方面的表示优点，我们提出了一个标准，广义的经验F-歧义（GEFD），并研究其与经典的广义L2票有关的理论特性统一设计。这些属性使我们能够根据现有统一设计开发一种低GEFD数据驱动的子采样方法。通过仿真示例和实际案例研究，我们表明所提出的亚采样方法优于随机抽样方法。此外，我们的方法在不同的模型规范下保持稳健，而其他流行的亚采样方法的表现不佳。实际上，这种无模型的属性比基于模型的亚采样方法更具吸引力，在我们的仿真研究中证明，后者的性能可能较差。

尽管具有Relu激活功能的神经网络（NNS）在广泛的应用中找到了成功，但它们在风险敏感环境中的采用受到对稳健性和可解释性的担忧受到限制。以前的作品来检查稳健性，并改善解释性部分地利用了Relu Nn的分段线性函数形式。在本文中，我们探讨了relu nns在输入空间中创建的独特拓扑结构，识别分区本地多台之间的邻接并基于这种邻接的遍历算法。我们的Polytope Travering算法可以适用于验证与鲁棒性和解释性相关的广泛网络属性，提供统一的方法来检查网络行为。由于遍历算法显式访问所有本地多台面，因此它返回遍历区域内的网络行为清晰和完整的图像。遍历算法的时间和空间复杂性由通过穿过遍历区域的Relu NN分区超平面的数量来确定。

可解释的机器学习（IML）在与人类健康和安全或基本权利有关的高度监管的行业方面变得越来越重要。通常，由于它们的透明度和解释性，应采用固有的IML模型，而具有模型无关的解释性的黑匣子型号可能更难以在监管审查下抵御。为了评估机器学习模型的固有可解释性，我们提出了一种基于特征效果和模型架构约束的定性模板。它为高性能IML模型开发提供了设计原则，其中通过审查我们最近的exnn，gami-net，simtree和aletheia工具包的实例，以实现深度Relu网络的局部线性解释性。我们进一步展示了如何设计一种可解释的Relu DNN模型，评估概念性的概念性研究，用于预测家庭贷款中的信用违约。我们希望这项工作将在银行业的高风险应用中，以及其他行业提供实用的IML模型的实用指导。

近年来，图表表示学习一直是一个非常活跃的研究领域。图表学习的目标是生成图表向量，以准确捕获大图的结构和特征。这尤其重要，因为图表向量的质量将影响这些向量在下游任务中的性能，例如节点分类，链接预测和异常检测。提出了许多用于生成有效图表向量的技术。图形表示学习的两个最普遍的类别是图形嵌入方法，而无需使用图神经网（GNN），我们将其表示为基于非GNN的图形嵌入方法，以及基于图形神经网（GNN）方法。非GNN图嵌入方法基于随机步行，时间点过程和神经网络学习方法等技术。另一方面，基于GNN的方法是对图数据进行深度学习的应用。在本调查中，我们提供了这两种类别的概述，并涵盖了静态图和动态图的当前最新方法。最后，我们探索了一些未来工作的开放和正在进行的研究方向。

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.

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.