Normalizing flows are constructed from a base distribution with a known density and a diffeomorphism with a tractable Jacobian. The base density of a normalizing flow can be parameterised by a different normalizing flow, thus allowing maps to be found between arbitrary distributions. We demonstrate and explore the utility of this approach and show it is particularly interesting in the case of conditional normalizing flows and for introducing optimal transport constraints on maps that are constructed using normalizing flows.

The sensitivity of many physics analyses can be enhanced by constructing discriminants that preferentially select signal events. Such discriminants become much more useful if they are uncorrelated with a set of protected attributes. In this paper we show a normalizing flow conditioned on the protected attributes can be used to find a decorrelated representation for any discriminant. As a normalizing flow is invertible the separation power of the resulting discriminant will be unchanged at any fixed value of the protected attributes. We demonstrate the efficacy of our approach by building supervised jet taggers that produce almost no sculpting in the mass distribution of the background.

我们呈现Turbo-SIM，是可以用作生成模型的信息理论原理的广义自动统计学框架。通过最大化输入和编码器和解码器的输出之间的相互信息，我们能够重新发现通常在对手自身额外的损失术语和生成的对抗网络中发现的损失术语，以及各种更复杂的相关模型。我们的广义框架使这些模型在数学上解释，通过分别设置每个损失项的重量来允许新的新功能。该框架还与编码器的内在架构和解码器无关，因此为整个网络的构建块留下了广泛的选择。我们将Turbo-SIM应用于碰撞机物理生成问题：在实验中检测到检测后，在碰撞之后，在碰撞之后的理论空间，在观察空间之后，从理论空间转换几个粒子的性质。

条件生成是生成问题的子类，其中生成的输出由属性信息调节。在本文中，我们提出了一种随机对比条件生成的对抗网络（InfoSCC-GaN），具有易诺的潜在空间。 InfoSCC-GaN架构基于内置于Infonce Paradigm的无监督对比编码器，属性分类器和Eigengan生成器。我们提出了一种新颖的训练方法，基于每次$ N $第-th迭代的外部或内部属性使用外部或内部属性，使用预先培训的对比编码器和预先训练的分类器。基于输入数据和潜在空间表示之间的相互信息最大化以及潜在空间和生成的数据来导出所提出的INFOSCC-GAN。因此，我们展示了训练目标函数与上述信息理论制剂之间的联系。实验结果表明，InfoSCC-GaN在AFHQ和Celeba数据集上的图像生成中优于“vanilla”Eigengan。此外，我们通过进行消融研究调查鉴别员架构和损失功能的影响。最后，我们证明，由于eigengan发电机，所提出的框架与Vanilla确定性GAN相比，与现有框架相比，与Vanilla确定性GAN相比，与Vanilla确定性GAN相反。代码，实验结果和演示可在HTTPS://github.com/vkinakh/infoscc-在线提供。

不连续分布的生成是大多数已知框架的困难任务，例如生成的自动化器和生成的对抗网络。生成的非可逆模型无法准确地生成此类分布，需要长期训练，并且经常受模式崩溃。变形AutoEncoders（VAES），基于保持潜在空间的想法是为了简单的采样，允许准确的重建，同时在生成任务中遇到重大限制。在这项工作中，我们使用预先训练的对比编码器来获得聚类潜空间来保持潜在的空间。然后，对于每个群集表示单向子多种子区，我们训练专用的低复杂性网络以从高斯分布生成该子多种。所提出的框架基于输入数据和潜在空间表示之间的相互信息最大化的信息定理制定。我们派生了成本函数与信息理论制定之间的联系。我们将我们的方法应用于合成2D分布，以展示使用连续随机网络的重建和产生不连续分布的方法。

归一化流量是漫射的，通常是维持尺寸保存，使用模型的可能性训练的模型。我们使用Surve Framework通过新的层构建尺寸减少调节流量，称为漏斗。我们展示了对各种数据集的功效，并表明它改善或匹配现有流量的性能，同时具有降低的潜在空间尺寸。漏斗层可以由各种变换构成，包括限制卷积和馈送前部。

We present a detailed study on Variational Autoencoders (VAEs) for anomalous jet tagging at the Large Hadron Collider. By taking in low-level jet constituents' information, and training with background QCD jets in an unsupervised manner, the VAE is able to encode important information for reconstructing jets, while learning an expressive posterior distribution in the latent space. When using the VAE as an anomaly detector, we present different approaches to detect anomalies: directly comparing in the input space or, instead, working in the latent space. In order to facilitate general search approaches such as bump-hunt, mass-decorrelated VAEs based on distance correlation regularization are also studied. We find that the naive mass-decorrelated VAEs fail at maintaining proper detection performance, by assigning higher probabilities to some anomalous samples. To build a performant mass-decorrelated anomalous jet tagger, we propose the Outlier Exposed VAE (OE-VAE), for which some outlier samples are introduced in the training process to guide the learned information. OE-VAEs are employed to achieve two goals at the same time: increasing sensitivity of outlier detection and decorrelating jet mass from the anomaly score. We succeed in reaching excellent results from both aspects. Code implementation of this work can be found at https://github.com/taolicheng/VAE-Jet

The release of ChatGPT, a language model capable of generating text that appears human-like and authentic, has gained significant attention beyond the research community. We expect that the convincing performance of ChatGPT incentivizes users to apply it to a variety of downstream tasks, including prompting the model to simplify their own medical reports. To investigate this phenomenon, we conducted an exploratory case study. In a questionnaire, we asked 15 radiologists to assess the quality of radiology reports simplified by ChatGPT. Most radiologists agreed that the simplified reports were factually correct, complete, and not potentially harmful to the patient. Nevertheless, instances of incorrect statements, missed key medical findings, and potentially harmful passages were reported. While further studies are needed, the initial insights of this study indicate a great potential in using large language models like ChatGPT to improve patient-centered care in radiology and other medical domains.

In recent years, several metrics have been developed for evaluating group fairness of rankings. Given that these metrics were developed with different application contexts and ranking algorithms in mind, it is not straightforward which metric to choose for a given scenario. In this paper, we perform a comprehensive comparative analysis of existing group fairness metrics developed in the context of fair ranking. By virtue of their diverse application contexts, we argue that such a comparative analysis is not straightforward. Hence, we take an axiomatic approach whereby we design a set of thirteen properties for group fairness metrics that consider different ranking settings. A metric can then be selected depending on whether it satisfies all or a subset of these properties. We apply these properties on eleven existing group fairness metrics, and through both empirical and theoretical results we demonstrate that most of these metrics only satisfy a small subset of the proposed properties. These findings highlight limitations of existing metrics, and provide insights into how to evaluate and interpret different fairness metrics in practical deployment. The proposed properties can also assist practitioners in selecting appropriate metrics for evaluating fairness in a specific application.

In recent years distributional reinforcement learning has produced many state of the art results. Increasingly sample efficient Distributional algorithms for the discrete action domain have been developed over time that vary primarily in the way they parameterize their approximations of value distributions, and how they quantify the differences between those distributions. In this work we transfer three of the most well-known and successful of those algorithms (QR-DQN, IQN and FQF) to the continuous action domain by extending two powerful actor-critic algorithms (TD3 and SAC) with distributional critics. We investigate whether the relative performance of the methods for the discrete action space translates to the continuous case. To that end we compare them empirically on the pybullet implementations of a set of continuous control tasks. Our results indicate qualitative invariance regarding the number and placement of distributional atoms in the deterministic, continuous action setting.