设计机器学习算法准确但公平，而不是基于任何敏感属性进行区分，对于社会接受对关键应用的AI至关重要。在本文中，我们提出了一种新颖的公平表示方法，称为R \'enyi公平信息瓶颈方法（RFIB），该方法包含了代表性的效用，公平性和紧凑性的约束，并将其应用于图像分类。我们方法的一个关键属性是，与大多数先前的工作相比，我们认为人口统计学奇偶ant和均衡的赔率是公平的约束，从而使对这两个标准的满意度更加细致。利用各种方法，我们表明我们的目标产生了涉及经典信息瓶颈（IB）措施的损失函数，并根据r \'enyi nyi nyi差异$ \ alpha $在共同信息上的r \'enyi差异ib术语IB术语测量紧凑度上建立上限在输入及其编码嵌入之间。在三个不同的图像数据集（Eyepacs，celeba和Fairface）上进行实验，我们研究了$ \ alpha $参数的影响以及其他两个可调IB参数对实现效用/公平性权衡目标的影响，并表明$ \ \ \ \ Alpha $参数提供了一个额外的自由度，可用于控制表示的紧凑性。我们使用各种效用，公平性和复合效用/公平指标评估方法的性能，表明RFIB的表现优于当前最新方法。

保证AI在不受限制的环境中是一个关键问题。我们的框架解决了域名适应，公平性和反事实分析的AI保证挑战，通过发现和干预对数据（例如天气或照明条件）的变化的因素进行操作，从而显着影响AI模型的稳健性。这里的鲁棒性被理解为模型性能对敏感因素的变化的不敏感性。敏感因素传统上被设置在监督环境中，由此有人知道a-priori（例如，对于公平性，这可能是性别或比赛等因素）。相比之下，我们的动机是现实生活场景，其中较少或没有，实际上是一个关于某些导致模型失败的某些因素的先验。这导致我们考虑各种设置（无监督，域泛化，半监督），其对应于对这些因素的不同程度的不完整知识。因此，我们的两步方法是通过a）发现导致AI系统以无监督的方式失败的敏感因素，然后b）干预模型以减少这些因素的影响。我们的方法考虑了由增强，一致性和对抗性干预（ACAI）组成的3个干预措施。我们展示了对所发现/源极端因素的干预措施，以概括为目标/真实因素。我们还展示了如何在半监督的情况下对某些目标因子标签的半监督方式进行适应性，通过自动化干预选择。实验表明，我们的方法改善了基线模型，关于实现最佳效用与敏感性/鲁棒性权衡。

虽然对抗性攻击检测得到了相当大的关注，但它仍然是两个观点的基本上具有挑战性的问题。首先，虽然威胁模型可以明确定义，但攻击者策略可能在这些限制范围内仍然很大。因此，检测应被视为开放式问题，与大多数电流检测方法相比，站立相反。这些方法采用封闭式视图和火车二进制探测器，从而偏置检测探测器训练期间看到的攻击。其次，有限的信息可在测试时间上获得，并且通常通过滋扰因子混淆，包括标签和图像的底层内容。我们通过基于随机子空间分析的新策略来解决这些挑战。我们提出了一种利用随机投影的性质的技术，以表征在各种子空间中的清洁和对抗性示例的行为。模型激活的自我一致性（或不一致）被利用从对抗例中辨别清洁。性能评估表明，我们的技术（$ AUC \在[0.92,0.98] $）优于竞争检测策略（$ AUC \在[0.30,0.79]中），同时仍然真正无法对攻击战略（针对目标/未确定的攻击） ）。它还需要显着更少的校准数据（仅由干净的例子组成）而不是实现这种性能的竞争方法。

Over the past decade, neural networks have been successful at making predictions from biological sequences, especially in the context of regulatory genomics. As in other fields of deep learning, tools have been devised to extract features such as sequence motifs that can explain the predictions made by a trained network. Here we intend to go beyond explainable machine learning and introduce SEISM, a selective inference procedure to test the association between these extracted features and the predicted phenotype. In particular, we discuss how training a one-layer convolutional network is formally equivalent to selecting motifs maximizing some association score. We adapt existing sampling-based selective inference procedures by quantizing this selection over an infinite set to a large but finite grid. Finally, we show that sampling under a specific choice of parameters is sufficient to characterize the composite null hypothesis typically used for selective inference-a result that goes well beyond our particular framework. We illustrate the behavior of our method in terms of calibration, power and speed and discuss its power/speed trade-off with a simpler data-split strategy. SEISM paves the way to an easier analysis of neural networks used in regulatory genomics, and to more powerful methods for genome wide association studies (GWAS).

In intensively managed forests in Europe, where forests are divided into stands of small size and may show heterogeneity within stands, a high spatial resolution (10 - 20 meters) is arguably needed to capture the differences in canopy height. In this work, we developed a deep learning model based on multi-stream remote sensing measurements to create a high-resolution canopy height map over the "Landes de Gascogne" forest in France, a large maritime pine plantation of 13,000 km$^2$ with flat terrain and intensive management. This area is characterized by even-aged and mono-specific stands, of a typical length of a few hundred meters, harvested every 35 to 50 years. Our deep learning U-Net model uses multi-band images from Sentinel-1 and Sentinel-2 with composite time averages as input to predict tree height derived from GEDI waveforms. The evaluation is performed with external validation data from forest inventory plots and a stereo 3D reconstruction model based on Skysat imagery available at specific locations. We trained seven different U-net models based on a combination of Sentinel-1 and Sentinel-2 bands to evaluate the importance of each instrument in the dominant height retrieval. The model outputs allow us to generate a 10 m resolution canopy height map of the whole "Landes de Gascogne" forest area for 2020 with a mean absolute error of 2.02 m on the Test dataset. The best predictions were obtained using all available satellite layers from Sentinel-1 and Sentinel-2 but using only one satellite source also provided good predictions. For all validation datasets in coniferous forests, our model showed better metrics than previous canopy height models available in the same region.

Knowledge Distillation (KD) is a commonly used technique for improving the generalization of compact Pre-trained Language Models (PLMs) on downstream tasks. However, such methods impose the additional burden of training a separate teacher model for every new dataset. Alternatively, one may directly work on the improvement of the optimization procedure of the compact model toward better generalization. Recent works observe that the flatness of the local minimum correlates well with better generalization. In this work, we adapt Stochastic Weight Averaging (SWA), a method encouraging convergence to a flatter minimum, to fine-tuning PLMs. We conduct extensive experiments on various NLP tasks (text classification, question answering, and generation) and different model architectures and demonstrate that our adaptation improves the generalization without extra computation cost. Moreover, we observe that this simple optimization technique is able to outperform the state-of-the-art KD methods for compact models.

This work addresses the problems of (a) designing utilization measurements of trained artificial intelligence (AI) models and (b) explaining how training data are encoded in AI models based on those measurements. The problems are motivated by the lack of explainability of AI models in security and safety critical applications, such as the use of AI models for classification of traffic signs in self-driving cars. We approach the problems by introducing theoretical underpinnings of AI model utilization measurement and understanding patterns in utilization-based class encodings of traffic signs at the level of computation graphs (AI models), subgraphs, and graph nodes. Conceptually, utilization is defined at each graph node (computation unit) of an AI model based on the number and distribution of unique outputs in the space of all possible outputs (tensor-states). In this work, utilization measurements are extracted from AI models, which include poisoned and clean AI models. In contrast to clean AI models, the poisoned AI models were trained with traffic sign images containing systematic, physically realizable, traffic sign modifications (i.e., triggers) to change a correct class label to another label in a presence of such a trigger. We analyze class encodings of such clean and poisoned AI models, and conclude with implications for trojan injection and detection.

White matter bundle segmentation is a cornerstone of modern tractography to study the brain's structural connectivity in domains such as neurological disorders, neurosurgery, and aging. In this study, we present FIESTA (FIber gEneration and bundle Segmentation in Tractography using Autoencoders), a reliable and robust, fully automated, and easily semi-automatically calibrated pipeline based on deep autoencoders that can dissect and fully populate WM bundles. Our framework allows the transition from one anatomical bundle definition to another with marginal calibrating time. This pipeline is built upon FINTA, CINTA, and GESTA methods that demonstrated how autoencoders can be used successfully for streamline filtering, bundling, and streamline generation in tractography. Our proposed method improves bundling coverage by recovering hard-to-track bundles with generative sampling through the latent space seeding of the subject bundle and the atlas bundle. A latent space of streamlines is learned using autoencoder-based modeling combined with contrastive learning. Using an atlas of bundles in standard space (MNI), our proposed method segments new tractograms using the autoencoder latent distance between each tractogram streamline and its closest neighbor bundle in the atlas of bundles. Intra-subject bundle reliability is improved by recovering hard-to-track streamlines, using the autoencoder to generate new streamlines that increase each bundle's spatial coverage while remaining anatomically meaningful. Results show that our method is more reliable than state-of-the-art automated virtual dissection methods such as RecoBundles, RecoBundlesX, TractSeg, White Matter Analysis and XTRACT. Overall, these results show that our framework improves the practicality and usability of current state-of-the-art bundling framework

There are many potential benefits to news readers accessing diverse sources. Modern news aggregators do the hard work of organizing the news, offering readers a plethora of source options, but choosing which source to read remains challenging. We propose a new framework to assist readers in identifying source differences and gaining an understanding of news coverage diversity. The framework is based on the generation of Discord Questions: questions with a diverse answer pool, explicitly illustrating source differences. To assemble a prototype of the framework, we focus on two components: (1) discord question generation, the task of generating questions answered differently by sources, for which we propose an automatic scoring method, and create a model that improves performance from current question generation (QG) methods by 5%, (2) answer consolidation, the task of grouping answers to a question that are semantically similar, for which we collect data and repurpose a method that achieves 81% balanced accuracy on our realistic test set. We illustrate the framework's feasibility through a prototype interface. Even though model performance at discord QG still lags human performance by more than 15%, generated questions are judged to be more interesting than factoid questions and can reveal differences in the level of detail, sentiment, and reasoning of sources in news coverage.

In a fissile material, the inherent multiplicity of neutrons born through induced fissions leads to correlations in their detection statistics. The correlations between neutrons can be used to trace back some characteristics of the fissile material. This technique known as neutron noise analysis has applications in nuclear safeguards or waste identification. It provides a non-destructive examination method for an unknown fissile material. This is an example of an inverse problem where the cause is inferred from observations of the consequences. However, neutron correlation measurements are often noisy because of the stochastic nature of the underlying processes. This makes the resolution of the inverse problem more complex since the measurements are strongly dependent on the material characteristics. A minor change in the material properties can lead to very different outputs. Such an inverse problem is said to be ill-posed. For an ill-posed inverse problem the inverse uncertainty quantification is crucial. Indeed, seemingly low noise in the data can lead to strong uncertainties in the estimation of the material properties. Moreover, the analytical framework commonly used to describe neutron correlations relies on strong physical assumptions and is thus inherently biased. This paper addresses dual goals. Firstly, surrogate models are used to improve neutron correlations predictions and quantify the errors on those predictions. Then, the inverse uncertainty quantification is performed to include the impact of measurement error alongside the residual model bias.