保存隐私的神经网络（NN）推理解决方案最近在几种提供不同的延迟带宽权衡的解决方案方面获得了重大吸引力。其中，许多人依靠同态加密（HE），这是一种对加密数据进行计算的方法。但是，与他们的明文对应物相比，他的操作即使是最先进的计划仍然很慢。修剪NN模型的参数是改善推理潜伏期的众所周知的方法。但是，在明文上下文中有用的修剪方法可能对HE案的改善几乎可以忽略不计，这在最近的工作中也证明了这一点。在这项工作中，我们提出了一套新颖的修剪方法，以减少潜伏期和记忆要求，从而将明文修剪方法的有效性带到HE中。至关重要的是，我们的建议采用两种关键技术，即。堆积模型权重的置换和扩展，使修剪能够明显更多的密封性下文并分别恢复大部分精度损失。我们证明了我们的方法在完全连接的层上的优势，其中使用最近提出的称为瓷砖张量的包装技术填充了权重，该技术允许在非相互作用模式下执行Deep NN推断。我们在各种自动编码器架构上评估了我们的方法，并证明，对于MNIST上的小均值重建损失为1.5*10^{ - 5}，我们将HE-SEAMABLE推断的内存要求和延迟减少了60％。

我们提出了Pac-Bayes风格的概括结合，该结合可以用各种积分概率指标（IPM）替换KL-Divergence。我们提供了这种结合的实例，IPM是总变异度量和Wasserstein距离。获得的边界的一个显着特征是，它们在最坏的情况下（当前和后距离彼此远距离时）在经典均匀收敛边界之间自然插值，并且在更好的情况下（后验和先验都关闭时）优选界限。这说明了使用算法和数据依赖性组件加强经典概括界限的可能性，从而使它们更适合分析使用大假设空间的算法。

保留隐私深度神经网络（DNN）推理是不同受监管行业的必要条件，如医疗保健，金融和零售。最近，同性恋加密（HE）已被用作在解决隐私问题的同时启用分析的方法。他能够通过加密数据安全预测。然而，与使用他的使用有几个挑战，包括DNN尺寸限制以及对某些操作类型的支持缺乏支持。最值得注意的是，在某些HE方案下不支持常用的Relu激活。我们提出了一种结构化方法来用二次多项式激活替换Relu。为了解决准确性的降级问题，我们使用预先训练的模型，该模型列举了另一个他友好的模型，使用诸如“可训练激活”功能和知识蒸馏等技术。我们使用用于Covid-19检测的胸部X射线和CT数据集，在AlexNet架构上展示了我们的方法。我们的实验表明，通过使用我们的方法，F1分数和用Relu培训的模型的准确性与He-insive模型之间的差距缩小到仅为1.1-5.3％的劣化。

保留保护解决方案使公司能够在履行政府法规的同时将机密数据卸载到第三方服务。为了实现这一点，它们利用了各种密码技术，例如同性恋加密（HE），其允许对加密数据执行计算。大多数他计划以SIMD方式工作，数据包装方法可以显着影响运行时间和内存成本。找到导致最佳性能实现的包装方法是一个艰难的任务。我们提出了一种简单而直观的框架，摘要为用户提供包装决定。我们解释其底层数据结构和优化器，并提出了一种用于执行2D卷积操作的新算法。我们使用此框架来实现他友好的AlexNet版本，在三分钟内运行，比其他最先进的解决方案更快的数量级，只能使用他。

Existing automated techniques for software documentation typically attempt to reason between two main sources of information: code and natural language. However, this reasoning process is often complicated by the lexical gap between more abstract natural language and more structured programming languages. One potential bridge for this gap is the Graphical User Interface (GUI), as GUIs inherently encode salient information about underlying program functionality into rich, pixel-based data representations. This paper offers one of the first comprehensive empirical investigations into the connection between GUIs and functional, natural language descriptions of software. First, we collect, analyze, and open source a large dataset of functional GUI descriptions consisting of 45,998 descriptions for 10,204 screenshots from popular Android applications. The descriptions were obtained from human labelers and underwent several quality control mechanisms. To gain insight into the representational potential of GUIs, we investigate the ability of four Neural Image Captioning models to predict natural language descriptions of varying granularity when provided a screenshot as input. We evaluate these models quantitatively, using common machine translation metrics, and qualitatively through a large-scale user study. Finally, we offer learned lessons and a discussion of the potential shown by multimodal models to enhance future techniques for automated software documentation.

This short report reviews the current state of the research and methodology on theoretical and practical aspects of Artificial Neural Networks (ANN). It was prepared to gather state-of-the-art knowledge needed to construct complex, hypercomplex and fuzzy neural networks. The report reflects the individual interests of the authors and, by now means, cannot be treated as a comprehensive review of the ANN discipline. Considering the fast development of this field, it is currently impossible to do a detailed review of a considerable number of pages. The report is an outcome of the Project 'The Strategic Research Partnership for the mathematical aspects of complex, hypercomplex and fuzzy neural networks' meeting at the University of Warmia and Mazury in Olsztyn, Poland, organized in September 2022.

When designing a new API for a large project, developers need to make smart design choices so that their code base can grow sustainably. To ensure that new API components are well designed, developers can learn from existing API components. However, the lack of standardized method for comparing API designs makes this learning process time-consuming and difficult. To address this gap we developed the API-Spector, to the best of our knowledge one of the first API-to-API specification recommendation engines. API-Spector retrieves relevant specification components written in OpenAPI (a widely adopted language used to describe web APIs). API-Spector presents several significant contributions, including: (1) novel methods of processing and extracting key information from OpenAPI specifications, (2) innovative feature extraction techniques that are optimized for the highly technical API specification domain, and (3) a novel log-linear probabilistic model that combines multiple signals to retrieve relevant and high quality OpenAPI specification components given a query specification. We evaluate API-Spector in both quantitative and qualitative tasks and achieve an overall of 91.7% recall@1 and 56.2% F1, which surpasses baseline performance by 15.4% in recall@1 and 3.2% in F1. Overall, API-Spector will allow developers to retrieve relevant OpenAPI specification components from a public or internal database in the early stages of the API development cycle, so that they can learn from existing established examples and potentially identify redundancies in their work. It provides the guidance developers need to accelerate development process and contribute thoughtfully designed APIs that promote code maintainability and quality.

We first prove that Littlestone classes, those which model theorists call stable, characterize learnability in a new statistical model: a learner in this new setting outputs the same hypothesis, up to measure zero, with probability one, after a uniformly bounded number of revisions. This fills a certain gap in the literature, and sets the stage for an approximation theorem characterizing Littlestone classes in terms of a range of learning models, by analogy to definability of types in model theory. We then give a complete analogue of Shelah's celebrated (and perhaps a priori untranslatable) Unstable Formula Theorem in the learning setting, with algorithmic arguments taking the place of the infinite.

We construct a universally Bayes consistent learning rule that satisfies differential privacy (DP). We first handle the setting of binary classification and then extend our rule to the more general setting of density estimation (with respect to the total variation metric). The existence of a universally consistent DP learner reveals a stark difference with the distribution-free PAC model. Indeed, in the latter DP learning is extremely limited: even one-dimensional linear classifiers are not privately learnable in this stringent model. Our result thus demonstrates that by allowing the learning rate to depend on the target distribution, one can circumvent the above-mentioned impossibility result and in fact, learn \emph{arbitrary} distributions by a single DP algorithm. As an application, we prove that any VC class can be privately learned in a semi-supervised setting with a near-optimal \emph{labeled} sample complexity of $\tilde{O}(d/\varepsilon)$ labeled examples (and with an unlabeled sample complexity that can depend on the target distribution).

The ability to effectively reuse prior knowledge is a key requirement when building general and flexible Reinforcement Learning (RL) agents. Skill reuse is one of the most common approaches, but current methods have considerable limitations.For example, fine-tuning an existing policy frequently fails, as the policy can degrade rapidly early in training. In a similar vein, distillation of expert behavior can lead to poor results when given sub-optimal experts. We compare several common approaches for skill transfer on multiple domains including changes in task and system dynamics. We identify how existing methods can fail and introduce an alternative approach to mitigate these problems. Our approach learns to sequence existing temporally-extended skills for exploration but learns the final policy directly from the raw experience. This conceptual split enables rapid adaptation and thus efficient data collection but without constraining the final solution.It significantly outperforms many classical methods across a suite of evaluation tasks and we use a broad set of ablations to highlight the importance of differentc omponents of our method.