The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

Federated Learning (FL) is pervasive in privacy-focused IoT environments since it enables avoiding privacy leakage by training models with gradients instead of data. Recent works show the uploaded gradients can be employed to reconstruct data, i.e., gradient leakage attacks, and several defenses are designed to alleviate the risk by tweaking the gradients. However, these defenses exhibit weak resilience against threatening attacks, as the effectiveness builds upon the unrealistic assumptions that deep neural networks are simplified as linear models. In this paper, without such unrealistic assumptions, we present a novel defense, called Refiner, instead of perturbing gradients, which refines ground-truth data to craft robust data that yields sufficient utility but with the least amount of privacy information, and then the gradients of robust data are uploaded. To craft robust data, Refiner promotes the gradients of critical parameters associated with robust data to close ground-truth ones while leaving the gradients of trivial parameters to safeguard privacy. Moreover, to exploit the gradients of trivial parameters, Refiner utilizes a well-designed evaluation network to steer robust data far away from ground-truth data, thereby alleviating privacy leakage risk. Extensive experiments across multiple benchmark datasets demonstrate the superior defense effectiveness of Refiner at defending against state-of-the-art threats.

为了构建推荐系统，不仅考虑用户 - 项目交互表示为序数变量，而且还利用了描述用户之间关系的社交网络，我们开发了一个层次结构的贝叶斯模型称为序数图因子分析（OGFA），该模型共同对用户建模 - 项目和用户 - 用户交互。 OGFA不仅可以实现良好的建议性能，而且还提取与代表性用户偏好相对应的可解释潜在因素。我们进一步将OGFA扩展到Oldinal Graph Gamma信念网络，该网络是一个多策略层的深层概率模型，可在多个语义级别捕获用户的偏好和社交社区。为了有效的推断，我们开发了一种并行的混合吉布斯 - EM算法，该算法利用了图的稀疏性，可扩展到大数据集。我们的实验结果表明，所提出的模型不仅在具有明确或隐式反馈的推荐数据集上的最新基准，而且还提供了可解释的潜在表示。

具有数百万参数的过度参数化模型取得了巨大成功。在这项工作中，我们问：至少由于学习者的\ emph {计算}限制，对大型模型的需求至少可以部分原因吗？此外，我们问，这种情况是否加剧了\ emph {robust}学习？我们证明确实可能是这种情况。我们展示了与信息理论学习者所需的学习任务相比，计算有限的学习者需要\ emph {明显更多的模型参数。此外，我们表明，对于健壮的学习可能需要更多的模型参数。特别是，对于计算有限的学习者，我们扩展了Bubeck and Sellke [Neurips'2021]的最新结果，该结果表明，强大的模型可能需要更多的参数，并表明有限学习者可能需要更多的参数数量。然后，我们解决以下相关的问题：我们是否希望通过限制\ emph {fersversaries}来纠正强大计算界限学习的情况，以便为了获得更少的参数获得模型而在计算上进行计算？再次，我们证明这是可能的。具体而言，在Garg，Jha，Mahloujifar和Mahmoody [Alt'2020]的基础上，我们演示了一项学习任务，可以有效，强大地对计算界限的攻击者进行有效，强大的学习，同时对信息理论攻击者需要强大学习者要使用更多参数。

离线增强学习（RL）旨在使用先前收集的静态数据集学习最佳策略，是RL的重要范式。由于函数近似错误在分布外动作上的功能近似错误，因此在此任务上的标准RL方法通常会表现较差。尽管已经提出了各种正规化方法来减轻此问题，但它们通常受到表达有限的策略类别的限制，有时会导致次优的解决方案。在本文中，我们提出了利用条件扩散模型作为行为克隆和策略正则化的高度表达政策类别的扩散-QL。在我们的方法中，我们学习了一个动作值函数，并在有条件扩散模型的训练损失中添加了最大化动作值的术语，这导致损失寻求接近行为政策的最佳动作。我们展示了基于扩散模型的策略的表现力以及在扩散模型下的行为克隆和策略改进的耦合都有助于扩散-QL的出色性能。我们在具有多模式行为策略的简单2D强盗示例中说明了我们的方法和先前的工作。然后，我们证明我们的方法可以在离线RL的大多数D4RL基准任务上实现最先进的性能。

徒手3D超声（US）由于其低成本和不受限制的视野而具有重要的临床价值。最近，深度学习算法已消除了其对笨重且昂贵的外部定位设备的依赖。然而，难以高程位移估计和大量累积漂移仍阻碍了改善重建精度。在这种情况下，我们提出了一个新颖的深度运动网络（MONET），该网络集成了图像和轻巧的传感器，从速度的角度来看，称为惯性测量单元（IMU），以减轻上述障碍。我们的贡献是两个方面。首先，我们首次介绍IMU加速度，以估计飞机外的高度位移。我们提出了一个时间和多分支结构，以挖掘低信噪比（SNR）加速度的宝贵信息。其次，我们提出了一种多模式的在线自制策略，该策略利用IMU信息作为弱标签进行自适应优化，以减少漂移错误并进一步改善加速噪声的影响。实验表明，我们所提出的方法实现了优越的重建性能，超过了最先进的方法。

本文提出了概率共形预测（PCP），这是一种预测推理算法，该算法通过不连续的预测集估算目标变量。给定输入，PCP基于估计生成模型的随机样品构建预测集。它有效且与显式或隐式有条件生成模型兼容。从理论上讲，我们表明PCP可以保证使用有限样品正确的边际覆盖范围。从经验上讲，我们研究了PCP在各种模拟和真实数据集上。与现有的共形推断方法相比，PCP提供了更清晰的预测集。

为了稳定地训练生成对抗网络（GAN），将实例噪声注入歧视器的输入中被认为是理论上的声音解决方案，但是，在实践中尚未实现其承诺。本文介绍了采用高斯混合物分布的扩散 - 在正向扩散链的所有扩散步骤中定义，以注入实例噪声。从观察到或生成的数据扩散的混合物中的随机样品被作为歧视器的输入。通过将其梯度通过前向扩散链进行反向传播来更新，该链的长度可自适应地调节以控制每个训练步骤允许的最大噪声与数据比率。理论分析验证了所提出的扩散gan的声音，该扩散器提供了模型和域 - 不可分割的可区分增强。在各种数据集上进行的一系列实验表明，扩散 - GAN可以提供稳定且具有数据效率的GAN训练，从而使对强GAN基准的性能保持一致，以综合构成照片现实的图像。

It is well believed that the higher uncertainty in a word of the caption, the more inter-correlated context information is required to determine it. However, current image captioning methods usually consider the generation of all words in a sentence sequentially and equally. In this paper, we propose an uncertainty-aware image captioning framework, which parallelly and iteratively operates insertion of discontinuous candidate words between existing words from easy to difficult until converged. We hypothesize that high-uncertainty words in a sentence need more prior information to make a correct decision and should be produced at a later stage. The resulting non-autoregressive hierarchy makes the caption generation explainable and intuitive. Specifically, we utilize an image-conditioned bag-of-word model to measure the word uncertainty and apply a dynamic programming algorithm to construct the training pairs. During inference, we devise an uncertainty-adaptive parallel beam search technique that yields an empirically logarithmic time complexity. Extensive experiments on the MS COCO benchmark reveal that our approach outperforms the strong baseline and related methods on both captioning quality as well as decoding speed.

The interaction and dimension of points are two important axes in designing point operators to serve hierarchical 3D models. Yet, these two axes are heterogeneous and challenging to fully explore. Existing works craft point operator under a single axis and reuse the crafted operator in all parts of 3D models. This overlooks the opportunity to better combine point interactions and dimensions by exploiting varying geometry/density of 3D point clouds. In this work, we establish PIDS, a novel paradigm to jointly explore point interactions and point dimensions to serve semantic segmentation on point cloud data. We establish a large search space to jointly consider versatile point interactions and point dimensions. This supports point operators with various geometry/density considerations. The enlarged search space with heterogeneous search components calls for a better ranking of candidate models. To achieve this, we improve the search space exploration by leveraging predictor-based Neural Architecture Search (NAS), and enhance the quality of prediction by assigning unique encoding to heterogeneous search components based on their priors. We thoroughly evaluate the networks crafted by PIDS on two semantic segmentation benchmarks, showing ~1% mIOU improvement on SemanticKITTI and S3DIS over state-of-the-art 3D models.