神经网络越来越依赖于复杂安全系统（例如自动驾驶汽车）的组成部分。对在更大的验证周期中嵌入神经网络验证的工具和方法的需求很高。但是，由于关注的广泛验证属性，很难进行神经网络验证，通常每个验证属性仅适用于专用求解器中的验证。在本文中，我们展示了最初设计用于验证，验证和仿真金融基础架构的功能编程语言的Imandra如何为神经网络验证提供整体基础架构。我们开发了一个新颖的图书馆Checkinn，该图书馆在Imandra的神经网络上形式化，并涵盖了神经网络验证的不同重要方面。

近年来，神经网络（NNS）的普及及其在现实世界应用中的普遍性的日益普及引起了人们对其验证的重要性的关注。虽然验证在理论上是计算困难的，但在实践中提出了许多解决该验证的技术。在文献中已经观察到，默认情况下，神经网络很少满足我们想要验证的逻辑约束。良好的行动是在验证验证之前训练给定的NN满足上述约束。这个想法有时被称为持续验证，指训练和验证之间的循环。通常，通过将给定正式逻辑语言的翻译指定为损失功能，可以实现带有约束的培训。然后，这些损失功能用于训练神经网络。因为为了培训目的，这些功能需要可区分，因此这些翻译称为可区分逻辑（DL）。这提出了几个研究问题。什么样的可区分逻辑是可能的？在连续验证的背景下，DL的特定选择有什么区别？从最终损失函数的角度来看，DL的理想标准是什么？在这个扩展的摘要中，我们将讨论并回答这些问题。

随着深度机器学习对现实生活应用的扩散，该技术的一种特殊属性引起了人们的注意：稳健性神经网络臭名昭著地表现出低的鲁棒性，并且对小输入扰动非常敏感。最近，已经提出了许多用于验证网络鲁棒性的一般特性的方法，但是它们主要用于计算机视觉。在本文中，我们提出了基于较大感兴趣区域的自然语言理解分类的验证规范，我们讨论了此类任务的挑战。我们观察到，尽管数据几乎是线性可分离的，但验证者努力输出积极的结果，我们解释了问题和含义。

神经网络在检测嘈杂数据中的模式方面非常成功，并且已成为许多领域的首选技术。但是，他们对对抗攻击的敏感性阻碍了它们的有用性。最近，已经提出了许多用于衡量和改善网络对对抗性扰动的鲁棒性的方法，并且这项不断增长的研究体现了许多明确或隐性的鲁棒性观念。这些概念之间的联系通常是微妙的，文献中缺少它们之间的系统比较。在本文中，我们开始解决这一差距，通过在网络的培训阶段，其验证和部署之后设置对网络鲁棒性作为数学属性的经验分析和评估的一般原则。然后，我们应用这些原则并进行案例研究，以展示我们一般方法的实际好处。

Missing values are a common problem in data science and machine learning. Removing instances with missing values can adversely affect the quality of further data analysis. This is exacerbated when there are relatively many more features than instances, and thus the proportion of affected instances is high. Such a scenario is common in many important domains, for example, single nucleotide polymorphism (SNP) datasets provide a large number of features over a genome for a relatively small number of individuals. To preserve as much information as possible prior to modeling, a rigorous imputation scheme is acutely needed. While Denoising Autoencoders is a state-of-the-art method for imputation in high-dimensional data, they still require enough complete cases to be trained on which is often not available in real-world problems. In this paper, we consider missing value imputation as a multi-label classification problem and propose Chains of Autoreplicative Random Forests. Using multi-label Random Forests instead of neural networks works well for low-sampled data as there are fewer parameters to optimize. Experiments on several SNP datasets show that our algorithm effectively imputes missing values based only on information from the dataset and exhibits better performance than standard algorithms that do not require any additional information. In this paper, the algorithm is implemented specifically for SNP data, but it can easily be adapted for other cases of missing value imputation.

During training, reinforcement learning systems interact with the world without considering the safety of their actions. When deployed into the real world, such systems can be dangerous and cause harm to their surroundings. Often, dangerous situations can be mitigated by defining a set of rules that the system should not violate under any conditions. For example, in robot navigation, one safety rule would be to avoid colliding with surrounding objects and people. In this work, we define safety rules in terms of the relationships between the agent and objects and use them to prevent reinforcement learning systems from performing potentially harmful actions. We propose a new safe epsilon-greedy algorithm that uses safety rules to override agents' actions if they are considered to be unsafe. In our experiments, we show that a safe epsilon-greedy policy significantly increases the safety of the agent during training, improves the learning efficiency resulting in much faster convergence, and achieves better performance than the base model.

This paper examines the encoding of analogy in large-scale pretrained language models, such as BERT and GPT-2. Existing analogy datasets typically focus on a limited set of analogical relations, with a high similarity of the two domains between which the analogy holds. As a more realistic setup, we introduce the Scientific and Creative Analogy dataset (SCAN), a novel analogy dataset containing systematic mappings of multiple attributes and relational structures across dissimilar domains. Using this dataset, we test the analogical reasoning capabilities of several widely-used pretrained language models (LMs). We find that state-of-the-art LMs achieve low performance on these complex analogy tasks, highlighting the challenges still posed by analogy understanding.

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

It has been experimentally demonstrated that humans are able to learn in a manner that allows them to make predictions on categories for which they have not seen any examples (Malaviya et al., 2022). Sucholutsky and Schonlau (2020) have recently presented a machine learning approach that aims to do the same. They utilise synthetically generated data and demonstrate that it is possible to achieve sub-linear scaling and develop models that can learn to recognise N classes from M training samples where M is less than N - aka less-than-one shot learning. Their method was, however, defined for univariate or simple multivariate data (Sucholutsky et al., 2021). We extend it to work on large, high-dimensional and real-world datasets and empirically validate it in this new and challenging setting. We apply this method to learn previously unseen NLP tasks from very few examples (4, 8 or 16). We first generate compact, sophisticated less-than-one shot representations called soft-label prototypes which are fitted on training data, capturing the distribution of different classes across the input domain space. We then use a modified k-Nearest Neighbours classifier to demonstrate that soft-label prototypes can classify data competitively, even outperforming much more computationally complex few-shot learning methods.

我们提出了Rudsi，这是俄罗斯语言感官诱导（WSI）的新基准。该数据集是使用单词用法图（WUGS）的手动注释和半自动聚类创建的。与俄罗斯的先前WSI数据集不同，Rudsi完全由数据驱动（基于俄罗斯国家语料库的文本），没有对注释者强加的外部词感官。根据图聚类的参数，可以从原始注释中产生不同的导数数据集。我们报告了几种基线WSI方法在Rudsi上获得的性能，并讨论了改善这些分数的可能性。