为了解释神经NLI模型及其推理策略，我们进行了一个系统的探测研究，调查了这些模型是否捕获了自然逻辑的至关重要：单调性和概念包容性。在向下单调上下文中正确识别有效推论是NLI性能的已知绊脚石，包括否定范围和广义量子等语言现象。要了解这种困难，我们将单调性强调为上下文的属性，并检查模型在中文嵌入中捕获单调信息的程度，这些嵌入式是其决策过程的中间嵌入。绘制最近探测范式的进步，我们比较各种模型的单调性功能的存在。我们发现，单调信息在基准测试中实现高分的流行NLI模型的表现中，并观察到基于微调策略的这些模型的改进引入了更强大的单调性功能，以及他们在挑战集上的提高性能。

本文介绍了DIFF解释器，这是可解释的多跳推断的第一个混合框架，该框架通过可区分的凸优化将明确的约束与神经体系结构集成在一起。具体而言，DIFF解释器允许在受限的优化框架内微调神经表示，以回答和解释自然语言的多跳问题。为了证明混合框架的功效，我们将现有的基于ILP的求解器与基于变压器的表示相结合。对科学和常识性质量检查任务的广泛经验评估表明，在端到端可区分框架中明确约束的整合可以显着改善非不同可差异ILP求解器的性能（8.91％-13.3％）。此外，其他分析表明，与独立的变压器和以前的多跳方法相比，DIFF解释器能够实现强大的性能，同时仍提供结构化解释以支持其预测。

Many modern research fields increasingly rely on collecting and analysing massive, often unstructured, and unwieldy datasets. Consequently, there is growing interest in machine learning and artificial intelligence applications that can harness this `data deluge'. This broad nontechnical overview provides a gentle introduction to machine learning with a specific focus on medical and biological applications. We explain the common types of machine learning algorithms and typical tasks that can be solved, illustrating the basics with concrete examples from healthcare. Lastly, we provide an outlook on open challenges, limitations, and potential impacts of machine-learning-powered medicine.

The detection of anomalies in time series data is crucial in a wide range of applications, such as system monitoring, health care or cyber security. While the vast number of available methods makes selecting the right method for a certain application hard enough, different methods have different strengths, e.g. regarding the type of anomalies they are able to find. In this work, we compare six unsupervised anomaly detection methods with different complexities to answer the questions: Are the more complex methods usually performing better? And are there specific anomaly types that those method are tailored to? The comparison is done on the UCR anomaly archive, a recent benchmark dataset for anomaly detection. We compare the six methods by analyzing the experimental results on a dataset- and anomaly type level after tuning the necessary hyperparameter for each method. Additionally we examine the ability of individual methods to incorporate prior knowledge about the anomalies and analyse the differences of point-wise and sequence wise features. We show with broad experiments, that the classical machine learning methods show a superior performance compared to the deep learning methods across a wide range of anomaly types.

Dialogue models are able to generate coherent and fluent responses, but they can still be challenging to control and may produce non-engaging, unsafe results. This unpredictability diminishes user trust and can hinder the use of the models in the real world. To address this, we introduce DialGuide, a novel framework for controlling dialogue model behavior using natural language rules, or guidelines. These guidelines provide information about the context they are applicable to and what should be included in the response, allowing the models to generate responses that are more closely aligned with the developer's expectations and intent. We evaluate DialGuide on three tasks in open-domain dialogue response generation: guideline selection, response generation, and response entailment verification. Our dataset contains 10,737 positive and 15,467 negative dialogue context-response-guideline triplets across two domains - chit-chat and safety. We provide baseline models for the tasks and benchmark their performance. We also demonstrate that DialGuide is effective in the dialogue safety domain, producing safe and engaging responses that follow developer guidelines.

To train deep learning models, which often outperform traditional approaches, large datasets of a specified medium, e.g., images, are used in numerous areas. However, for light field-specific machine learning tasks, there is a lack of such available datasets. Therefore, we create our own light field datasets, which have great potential for a variety of applications due to the abundance of information in light fields compared to singular images. Using the Unity and C# frameworks, we develop a novel approach for generating large, scalable, and reproducible light field datasets based on customizable hardware configurations to accelerate light field deep learning research.

We consider a sequential decision making task where we are not allowed to evaluate parameters that violate an a priori unknown (safety) constraint. A common approach is to place a Gaussian process prior on the unknown constraint and allow evaluations only in regions that are safe with high probability. Most current methods rely on a discretization of the domain and cannot be directly extended to the continuous case. Moreover, the way in which they exploit regularity assumptions about the constraint introduces an additional critical hyperparameter. In this paper, we propose an information-theoretic safe exploration criterion that directly exploits the GP posterior to identify the most informative safe parameters to evaluate. Our approach is naturally applicable to continuous domains and does not require additional hyperparameters. We theoretically analyze the method and show that we do not violate the safety constraint with high probability and that we explore by learning about the constraint up to arbitrary precision. Empirical evaluations demonstrate improved data-efficiency and scalability.

Counterfactuals are often described as 'retrospective,' focusing on hypothetical alternatives to a realized past. This description relates to an often implicit assumption about the structure and stability of exogenous variables in the system being modeled -- an assumption that is reasonable in many settings where counterfactuals are used. In this work, we consider cases where we might reasonably make a different assumption about exogenous variables, namely, that the exogenous noise terms of each unit do exhibit some unit-specific structure and/or stability. This leads us to a different use of counterfactuals -- a 'forward-looking' rather than 'retrospective' counterfactual. We introduce "counterfactual treatment choice," a type of treatment choice problem that motivates using forward-looking counterfactuals. We then explore how mismatches between interventional versus forward-looking counterfactual approaches to treatment choice, consistent with different assumptions about exogenous noise, can lead to counterintuitive results.

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.

Controlled text generation is a very important task in the arena of natural language processing due to its promising applications. In order to achieve this task we mainly introduce the novel soft prompt tuning method of using soft prompts at both encoder and decoder levels together in a T5 model and investigate the performance as the behaviour of an additional soft prompt related to the decoder of a T5 model in controlled text generation remained unexplored. Then we also investigate the feasibility of steering the output of this extended soft prompted T5 model at decoder level and finally analyse the utility of generated text to be used in AI related tasks such as training AI models with an interpretability analysis of the classifier trained with synthetic text, as there is a lack of proper analysis of methodologies in generating properly labelled data to be utilized in AI tasks. Through the performed in-depth intrinsic and extrinsic evaluations of this generation model along with the artificially generated data, we found that this model produced better results compared to the T5 model with a single soft prompt at encoder level and the sentiment classifier trained using this artificially generated data can produce comparable classification results to the results of a classifier trained with real labelled data and also the classifier decision is interpretable with respect to the input text content.