联合学习是一种来自分散数据集的培训模型的新兴技术。在许多应用程序中，参与联合学习系统的数据所有者不仅拥有数据，还拥有一组域知识。这些知识包括人类的知识和工艺，对联邦学习任务非常有帮助。在这项工作中，我们提出了一个联合学习框架，该框架允许注入参与者的领域知识，其中关键思想是通过本地知识来完善全球模型。我们认为的方案是由真正的行业级应用激励的，我们证明了我们采用该应用的有效性。

学习条件密度和识别影响整个分布的因素是数据驱动应用程序中的重要任务。常规方法主要与摘要统计数据合作，因此不足以进行全面的调查。最近，关于功能回归方法的发展，将密度曲线作为功能结果建模。开发此类模型的一个主要挑战在于非阴性的固有约束和密度结果功能空间的单位积分。为了克服这个基本问题，我们建议Wasserstein分销学习（WDL），这是一个柔性在尺度回归建模框架，始于Wasserstein距离$ W_2 $，作为密度结果空间的适当指标。然后，我们将半参数条件高斯混合模型（SCGMM）作为模型类$ \ mathfrak {f} \ otimes \ Mathcal {t} $作为模型类$ \ mathfrak {scgmm）介绍。生成的度量空间$（\ Mathfrak {f} \ otimes \ Mathcal {t}，W_2）$满足所需的约束，并提供密集且封闭的功能子空间。为了拟合所提出的模型，我们基于增强树的大量最小化优化进一步开发了有效的算法。与以前的文献中的方法相比，WDL更好地表征了条件密度的非线性依赖性及其得出的摘要统计。我们通过模拟和现实世界应用来证明WDL框架的有效性。

旨在解决不完整的多视图数据中缺少部分视图的聚类问题的不完整的多视图聚类，近年来受到了越来越多的关注。尽管已经开发了许多方法，但大多数方法要么无法灵活地处理不完整的多视图数据，因此使用任意丢失的视图，或者不考虑视图之间信息失衡的负面因素。此外，某些方法并未完全探索所有不完整视图的局部结构。为了解决这些问题，本文提出了一种简单但有效的方法，称为局部稀疏不完整的多视图聚类（LSIMVC）。与现有方法不同，LSIMVC打算通过优化一个稀疏的正则化和新颖的图形嵌入式多视图矩阵分数模型来从不完整的多视图数据中学习稀疏和结构化的潜在表示。具体而言，在基于矩阵分解的这种新型模型中，引入了基于L1规范的稀疏约束，以获得稀疏的低维单个表示和稀疏共识表示。此外，引入了新的本地图嵌入项以学习结构化共识表示。与现有作品不同，我们的本地图嵌入术语汇总了图形嵌入任务和共识表示任务中的简洁术语。此外，为了减少多视图学习的不平衡因素，将自适应加权学习方案引入LSIMVC。最后，给出了有效的优化策略来解决我们提出的模型的优化问题。在六个不完整的多视图数据库上执行的全面实验结果证明，我们的LSIMVC的性能优于最新的IMC方法。该代码可在https://github.com/justsmart/lsimvc中找到。

深度学习（DL）模型为各种医学成像基准挑战提供了最先进的性能，包括脑肿瘤细分（BRATS）挑战。然而，局灶性病理多隔室分割（例如，肿瘤和病变子区）的任务特别具有挑战性，并且潜在的错误阻碍DL模型转化为临床工作流程。量化不确定形式的DL模型预测的可靠性，可以实现最不确定的地区的临床审查，从而建立信任并铺平临床翻译。最近，已经引入了许多不确定性估计方法，用于DL医学图像分割任务。开发指标评估和比较不确定性措施的表现将有助于最终用户制定更明智的决策。在本研究中，我们探索并评估在Brats 2019-2020任务期间开发的公制，以对不确定量化量化（Qu-Brats），并旨在评估和排列脑肿瘤多隔室分割的不确定性估计。该公制（1）奖励不确定性估计，对正确断言产生高置信度，以及在不正确的断言处分配低置信水平的估计数，（2）惩罚导致更高百分比的无关正确断言百分比的不确定性措施。我们进一步基准测试由14个独立参与的Qu-Brats 2020的分割不确定性，所有这些都参与了主要的Brats细分任务。总体而言，我们的研究结果证实了不确定性估计提供了分割算法的重要性和互补价值，因此突出了医学图像分析中不确定性量化的需求。我们的评估代码在HTTPS://github.com/ragmeh11/qu-brats公开提供。

随着机器学习（ml）的进步及其日益增长的意识，许多拥有数据但不是ML专业知识（数据所有者）的组织希望汇集他们的数据并与那些具有专业知识的人合作，但需要来自不同来源的数据，以便训练真正普遍的资料模型（模型所有者）。在这种协作ML中，数据所有者希望保护其培训数据的隐私，而模型所有者希望模型的机密性和可能包含知识产权的培训方法。但是，现有的私人ML解决方案，如联合学习和分裂学习，不能同时满足数据和模型所有者的隐私要求。本文介绍了城可扩展的协作ML系统，可根据英特尔SGX在不受信任的基础架构中保护两个数据所有者和模型所有者的隐私。 CITADEL在代表数据所有者和代表模型所有者运行的多个训练环路中执行分布式训练。 CITADEL通过零和屏蔽和分层聚合进一步在这些外地之间建立了强大的信息屏障，以防止在协同培训期间防止数据/模型泄漏。与现有的SGX保护培训系统相比，Citadel实现了合作ML的更好的可扩展性和更强大的隐私保障。具有各种ML模型的云部署显示，Citadel缩放到大量的环路，由SGX引起的小于1.73x放缓。

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.

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.