我们为旨在降低公平性的对抗神经网络（GNN）的对抗性攻击（GNN）的存在和有效性提供了证据。这些攻击可能不利基于GNN的节点分类中的特定节点子组，其中基础网络的节点具有敏感的属性，例如种族或性别。我们进行了定性和实验分析，以解释对抗链接注射如何损害GNN预测的公平性。例如，攻击者可以通过在属于相反子组和相反类标签的节点之间注入对抗性链接来损害基于GNN的节点分类的公平性。我们在经验数据集上的实验表明，对抗公平性攻击可以显着降低GNN预测的公平性（攻击是有效的），其扰动率较低（攻击是有效的），并且没有明显的准确性下降（攻击是欺骗性的）。这项工作证明了GNN模型对对抗公平性攻击的脆弱性。我们希望我们的发现在社区中提高人们对这个问题的认识，并为GNN模型的未来发展奠定了基础，这些模型对这种攻击更为强大。

本文探讨了强化学习（RL）模型用于自动赛车的使用。与安全车是头等大事的乘用车相反，赛车的目的是最大程度地减少单圈时间。我们将问题视为一项强化学习任务，其中包括由车辆遥测组成的多维输入和连续的动作空间。为了找出哪种RL方法更好地解决了问题，以及获得的模型是否推广到未知轨道上，我们将10种深层确定性策略梯度（DDPG）变体进行了两个实验：i）〜研究RL方法如何学习驱动驱动赛车和ii）研究学习方案如何影响模型的推广能力。我们的研究表明，接受RL训练的模型不仅能够比基线开源手工机器人更快地驾驶，而且还可以推广到未知轨道。

深度学习属于人工智能领域，机器执行通常需要某种人类智能的任务。类似于大脑的基本结构，深度学习算法包括一种人工神经网络，其类似于生物脑结构。利用他们的感官模仿人类的学习过程，深入学习网络被送入（感官）数据，如文本，图像，视频或声音。这些网络在不同的任务中优于最先进的方法，因此，整个领域在过去几年中看到了指数增长。这种增长在过去几年中每年超过10,000多种出版物。例如，只有在医疗领域中的所有出版物中覆盖的搜索引擎只能在Q3 2020中覆盖所有出版物的子集，用于搜索术语“深度学习”，其中大约90％来自过去三年。因此，对深度学习领域的完全概述已经不可能在不久的将来获得，并且在不久的将来可能会难以获得难以获得子场的概要。但是，有几个关于深度学习的综述文章，这些文章专注于特定的科学领域或应用程序，例如计算机愿景的深度学习进步或在物体检测等特定任务中进行。随着这些调查作为基础，这一贡献的目的是提供对不同科学学科的深度学习的第一个高级，分类的元调查。根据底层数据来源（图像，语言，医疗，混合）选择了类别（计算机愿景，语言处理，医疗信息和其他工程）。此外，我们还审查了每个子类别的常见架构，方法，专业，利弊，评估，挑战和未来方向。

Recent advances in deep learning have enabled us to address the curse of dimensionality (COD) by solving problems in higher dimensions. A subset of such approaches of addressing the COD has led us to solving high-dimensional PDEs. This has resulted in opening doors to solving a variety of real-world problems ranging from mathematical finance to stochastic control for industrial applications. Although feasible, these deep learning methods are still constrained by training time and memory. Tackling these shortcomings, Tensor Neural Networks (TNN) demonstrate that they can provide significant parameter savings while attaining the same accuracy as compared to the classical Dense Neural Network (DNN). In addition, we also show how TNN can be trained faster than DNN for the same accuracy. Besides TNN, we also introduce Tensor Network Initializer (TNN Init), a weight initialization scheme that leads to faster convergence with smaller variance for an equivalent parameter count as compared to a DNN. We benchmark TNN and TNN Init by applying them to solve the parabolic PDE associated with the Heston model, which is widely used in financial pricing theory.

We introduce the Conditional Independence Regression CovariancE (CIRCE), a measure of conditional independence for multivariate continuous-valued variables. CIRCE applies as a regularizer in settings where we wish to learn neural features $\varphi(X)$ of data $X$ to estimate a target $Y$, while being conditionally independent of a distractor $Z$ given $Y$. Both $Z$ and $Y$ are assumed to be continuous-valued but relatively low dimensional, whereas $X$ and its features may be complex and high dimensional. Relevant settings include domain-invariant learning, fairness, and causal learning. The procedure requires just a single ridge regression from $Y$ to kernelized features of $Z$, which can be done in advance. It is then only necessary to enforce independence of $\varphi(X)$ from residuals of this regression, which is possible with attractive estimation properties and consistency guarantees. By contrast, earlier measures of conditional feature dependence require multiple regressions for each step of feature learning, resulting in more severe bias and variance, and greater computational cost. When sufficiently rich features are used, we establish that CIRCE is zero if and only if $\varphi(X) \perp \!\!\! \perp Z \mid Y$. In experiments, we show superior performance to previous methods on challenging benchmarks, including learning conditionally invariant image features.

Neural networks are increasingly applied in safety critical domains, their verification thus is gaining importance. A large class of recent algorithms for proving input-output relations of feed-forward neural networks are based on linear relaxations and symbolic interval propagation. However, due to variable dependencies, the approximations deteriorate with increasing depth of the network. In this paper we present DPNeurifyFV, a novel branch-and-bound solver for ReLU networks with low dimensional input-space that is based on symbolic interval propagation with fresh variables and input-splitting. A new heuristic for choosing the fresh variables allows to ameliorate the dependency problem, while our novel splitting heuristic, in combination with several other improvements, speeds up the branch-and-bound procedure. We evaluate our approach on the airborne collision avoidance networks ACAS Xu and demonstrate runtime improvements compared to state-of-the-art tools.

Nowadays, copy detection patterns (CDP) appear as a very promising anti-counterfeiting technology for physical object protection. However, the advent of deep learning as a powerful attacking tool has shown that the general authentication schemes are unable to compete and fail against such attacks. In this paper, we propose a new mathematical model of printing-imaging channel for the authentication of CDP together with a new detection scheme based on it. The results show that even deep learning created copy fakes unknown at the training stage can be reliably authenticated based on the proposed approach and using only digital references of CDP during authentication.

Static subword tokenization algorithms have been an essential component of recent works on language modeling. However, their static nature results in important flaws that degrade the models' downstream performance and robustness. In this work, we propose MANTa, a Module for Adaptive Neural TokenizAtion. MANTa is a differentiable tokenizer trained end-to-end with the language model. The resulting system offers a trade-off between the expressiveness of byte-level models and the speed of models trained using subword tokenization. In addition, our tokenizer is highly explainable since it produces an explicit segmentation of sequences into blocks. We evaluate our pre-trained model on several English datasets from different domains as well as on synthetic noise. We find that MANTa improves robustness to character perturbations and out-of-domain data. We then show that MANTa performs comparably to other models on the general-domain GLUE benchmark. Finally, we show that it is considerably faster than strictly byte-level models.

Machine Learning models capable of handling the large datasets collected in the financial world can often become black boxes expensive to run. The quantum computing paradigm suggests new optimization techniques, that combined with classical algorithms, may deliver competitive, faster and more interpretable models. In this work we propose a quantum-enhanced machine learning solution for the prediction of credit rating downgrades, also known as fallen-angels forecasting in the financial risk management field. We implement this solution on a neutral atom Quantum Processing Unit with up to 60 qubits on a real-life dataset. We report competitive performances against the state-of-the-art Random Forest benchmark whilst our model achieves better interpretability and comparable training times. We examine how to improve performance in the near-term validating our ideas with Tensor Networks-based numerical simulations.

This paper presents the development of an AI-based language learning platform Revita. It is a freely available intelligent online tutor, developed to support learners of multiple languages, from low-intermediate to advanced levels. It has been in pilot use by hundreds of students at several universities, whose feedback and needs are shaping the development. One of the main emerging features of Revita is the introduction of a system of linguistic constructs as the representation of domain knowledge. The system of constructs is developed in close collaboration with experts in language teaching. Constructs define the types of exercises, the content of the feedback, and enable the detailed modeling and evaluation of learning progress.