Deep learning has emerged as an effective solution for solving the task of object detection in images but at the cost of requiring large labeled datasets. To mitigate this cost, semi-supervised object detection methods, which consist in leveraging abundant unlabeled data, have been proposed and have already shown impressive results. However, most of these methods require linking a pseudo-label to a ground-truth object by thresholding. In previous works, this threshold value is usually determined empirically, which is time consuming, and only done for a single data distribution. When the domain, and thus the data distribution, changes, a new and costly parameter search is necessary. In this work, we introduce our method Adaptive Self-Training for Object Detection (ASTOD), which is a simple yet effective teacher-student method. ASTOD determines without cost a threshold value based directly on the ground value of the score histogram. To improve the quality of the teacher predictions, we also propose a novel pseudo-labeling procedure. We use different views of the unlabeled images during the pseudo-labeling step to reduce the number of missed predictions and thus obtain better candidate labels. Our teacher and our student are trained separately, and our method can be used in an iterative fashion by replacing the teacher by the student. On the MS-COCO dataset, our method consistently performs favorably against state-of-the-art methods that do not require a threshold parameter, and shows competitive results with methods that require a parameter sweep search. Additional experiments with respect to a supervised baseline on the DIOR dataset containing satellite images lead to similar conclusions, and prove that it is possible to adapt the score threshold automatically in self-training, regardless of the data distribution.

基于模拟的推理的现代方法依赖于深度学习代理来实现与计算机模拟器的近似推断。在实践中，估计的后代的计算忠诚度很少得到保证。例如，Hermans等。 （2021）表明，当前基于仿真的推理算法可以产生过度自信的后代，因此可能会出现虚假推断。在这项工作中，我们引入了平衡的神经比估计（BNRE），该算法的变体旨在产生后近似值，往往更保守，从而提高了其可靠性，同时共享同样的贝叶斯最佳解决方案。我们通过执行平衡条件来实现这一目标，从而增加了小型模拟预算制度中的量化不确定性，同时仍会随着预算的增加而融合到确切的后部。我们提供的理论论点表明，BNRE倾向于产生比NRE更保守的后替代物。我们对BNRE进行了多种任务的评估，并表明它在所有测试的基准和仿真预算上产生了保守的后验代替代物。最后，我们强调BNRE可以直接实施NRE，并且不引入任何计算开销。

计算神经网络的贝叶斯后部是由于参数空间的高度的挑战任务。锚定的合奏通过培训神经网络的集合来近似于锚定损失，专为Optima遵循贝叶斯山脉。然而，培训集合变得昂贵，因为对于每个成员重复完整的训练程序，因此成员的数量增加了昂贵。在本说明中，我们呈现循环锚定的合奏（SAE），一个轻量级的替代方案锚定的合奏。代替从划痕训练集合的每个成员，而是在具有高自动相关的损耗的损耗中训练，因此能够快速收敛神经网络和贝叶斯后部的有效近似。 SAE优于锚定的集成，在某些基准上，在某些基准上，在某些基准上，同时表现出对其他基准以及在贝叶斯深度学习竞争中近似推断的光明和延长轨道中的2个和第三位。

随机森林已被广泛用于其提供的所谓重要措施，在输入变量的相关性来预测某一输出全局（每个数据集）级洞察能力。在另一方面，根据沙普利值方法已被引入特征相关的基于树的模型分析细化到本地（每个实例）的水平。在这种情况下，我们首先证明杂质（MDI）变量重要性得分的全球平均减少对应的Shapley值在某些条件下。然后，我们推导出变量相关的本地MDI重要的措施，这与全球MDI衡量一个非常自然的连接，并且可以与局部特征相关的一个新概念。我们进一步联系当地MDI重要性有关与沙普利值和从文献中有关措施的光进行讨论。这些措施是通过实验在几个分类和回归问题的说明。

We present extensive empirical evidence showing that current Bayesian simulation-based inference algorithms can produce computationally unfaithful posterior approximations. Our results show that all benchmarked algorithms -- (Sequential) Neural Posterior Estimation, (Sequential) Neural Ratio Estimation, Sequential Neural Likelihood and variants of Approximate Bayesian Computation -- can yield overconfident posterior approximations, which makes them unreliable for scientific use cases and falsificationist inquiry. Failing to address this issue may reduce the range of applicability of simulation-based inference. For this reason, we argue that research efforts should be made towards theoretical and methodological developments of conservative approximate inference algorithms and present research directions towards this objective. In this regard, we show empirical evidence that ensembling posterior surrogates provides more reliable approximations and mitigates the issue.

在许多科学领域，复杂的现象是由随机参数模拟器建模的，通常具有高维参数空间和棘手的可能性。在这种情况下，执行贝叶斯推论可能具有挑战性。在这项工作中，我们提出了一种新的方法，使得可以在参数的任意子集上实现摊销推断，而无需诉诸数值集成，这使得解释后的后验更方便。我们的方法是有效的，可以用任意神经网络架构实现。我们展示了方法对来自引力波观测的二元黑洞系统参数推断的适用性。

推断基于实验观察的随机模型的参数是科学方法的核心。特别具有挑战性的设置是当模型强烈不确定时，即当不同的参数集产生相同的观察时。这在许多实际情况下出现，例如在推断无线电源的距离和功率时（是源关闭和弱或远远强，且强大且强大？）或估计电生理实验的放大器增益和底层脑活动。在这项工作中，我们通过利用由辅助观察集共享全局参数传达的附加信息来阐明这种不确定性的新方法。我们的方法基于对贝叶斯分层模型的标准化流程扩展了基于仿真的推断（SBI）的最新进展。我们通过模拟和实际EEG数据将其应用于可用于分析解决方案的激励示例，以便将其验证我们的提案，然后将其从计算神经科学逆变众所周知的非线性模型。

scikit-learn is an increasingly popular machine learning library. Written in Python, it is designed to be simple and efficient, accessible to non-experts, and reusable in various contexts. In this paper, we present and discuss our design choices for the application programming interface (API) of the project. In particular, we describe the simple and elegant interface shared by all learning and processing units in the library and then discuss its advantages in terms of composition and reusability. The paper also comments on implementation details specific to the Python ecosystem and analyzes obstacles faced by users and developers of the library.

View-dependent effects such as reflections pose a substantial challenge for image-based and neural rendering algorithms. Above all, curved reflectors are particularly hard, as they lead to highly non-linear reflection flows as the camera moves. We introduce a new point-based representation to compute Neural Point Catacaustics allowing novel-view synthesis of scenes with curved reflectors, from a set of casually-captured input photos. At the core of our method is a neural warp field that models catacaustic trajectories of reflections, so complex specular effects can be rendered using efficient point splatting in conjunction with a neural renderer. One of our key contributions is the explicit representation of reflections with a reflection point cloud which is displaced by the neural warp field, and a primary point cloud which is optimized to represent the rest of the scene. After a short manual annotation step, our approach allows interactive high-quality renderings of novel views with accurate reflection flow. Additionally, the explicit representation of reflection flow supports several forms of scene manipulation in captured scenes, such as reflection editing, cloning of specular objects, reflection tracking across views, and comfortable stereo viewing. We provide the source code and other supplemental material on https://repo-sam.inria.fr/ fungraph/neural_catacaustics/

Analogical proportions compare pairs of items (a, b) and (c, d) in terms of their differences and similarities. They play a key role in the formalization of analogical inference. The paper first discusses how to improve analogical inference in terms of accuracy and in terms of computational cost. Then it indicates the potential of analogical proportions for explanation. Finally, it highlights the close relationship between analogical proportions and multi-valued dependencies, which reveals an unsuspected aspect of the former.