部署AI驱动的系统需要支持有效人类互动的值得信赖的模型，超出了原始预测准确性。概念瓶颈模型通过在类似人类的概念的中间级别调节分类任务来促进可信度。这使得人类干预措施可以纠正错误预测的概念以改善模型的性能。但是，现有的概念瓶颈模型无法在高任务准确性，基于概念的强大解释和对概念的有效干预措施之间找到最佳的妥协，尤其是在稀缺完整和准确的概念主管的现实情况下。为了解决这个问题，我们提出了概念嵌入模型，这是一种新型的概念瓶颈模型，它通过学习可解释的高维概念表示形式而超出了当前的准确性-VS解关性权衡。我们的实验表明，嵌入模型（1）达到更好或竞争性的任务准确性W.R.T. W.R.T.没有概念的标准神经模型，（2）提供概念表示，以捕获有意义的语义，包括其地面真相标签，（3）支持测试时间概念干预措施，其在测试准确性中的影响超过了标准概念瓶颈模型，以及（4）规模对于稀缺的完整概念监督的现实条件。

解释性是决策系统的压迫问题。已经提出了许多后的HOC方法来解释任何机器学习模型的预测。但是，业务流程和决策系统很少归属于单个独立的模型。这些系统组合了产生关键预测的多个模型，然后应用决策规则以生成最终决定。为了解释此类决定，我们呈现SMACE，半模型 - 不可知论式解释器，一种新的解释方法，该方法将决策规则与现有的机器学习模型进行决策规则，以生成对最终用户身份定制的直观特征排名。我们表明，建立的模型 - 无可止境方法在这一框架中产生了不良的结果。

The deployment of Deep Learning (DL) models is still precluded in those contexts where the amount of supervised data is limited. To answer this issue, active learning strategies aim at minimizing the amount of labelled data required to train a DL model. Most active strategies are based on uncertain sample selection, and even often restricted to samples lying close to the decision boundary. These techniques are theoretically sound, but an understanding of the selected samples based on their content is not straightforward, further driving non-experts to consider DL as a black-box. For the first time, here we propose a different approach, taking into consideration common domain-knowledge and enabling non-expert users to train a model with fewer samples. In our Knowledge-driven Active Learning (KAL) framework, rule-based knowledge is converted into logic constraints and their violation is checked as a natural guide for sample selection. We show that even simple relationships among data and output classes offer a way to spot predictions for which the model need supervision. The proposed approach (i) outperforms many active learning strategies in terms of average F1 score, particularly in those contexts where domain knowledge is rich. Furthermore, we empirically demonstrate that (ii) KAL discovers data distribution lying far from the initial training data unlike uncertainty-based strategies, (iii) it ensures domain experts that the provided knowledge is respected by the model on test data, and (iv) it can be employed even when domain-knowledge is not available by coupling it with a XAI technique. Finally, we also show that KAL is also suitable for object recognition tasks and, its computational demand is low, unlike many recent active learning strategies.

An important class of techniques for resonant anomaly detection in high energy physics builds models that can distinguish between reference and target datasets, where only the latter has appreciable signal. Such techniques, including Classification Without Labels (CWoLa) and Simulation Assisted Likelihood-free Anomaly Detection (SALAD) rely on a single reference dataset. They cannot take advantage of commonly-available multiple datasets and thus cannot fully exploit available information. In this work, we propose generalizations of CWoLa and SALAD for settings where multiple reference datasets are available, building on weak supervision techniques. We demonstrate improved performance in a number of settings with realistic and synthetic data. As an added benefit, our generalizations enable us to provide finite-sample guarantees, improving on existing asymptotic analyses.

Following the advent of immersive technologies and the increasing interest in representing interactive geometrical format, 3D Point Clouds (PC) have emerged as a promising solution and effective means to display 3D visual information. In addition to other challenges in immersive applications, objective and subjective quality assessments of compressed 3D content remain open problems and an area of research interest. Yet most of the efforts in the research area ignore the local geometrical structures between points representation. In this paper, we overcome this limitation by introducing a novel and efficient objective metric for Point Clouds Quality Assessment, by learning local intrinsic dependencies using Graph Neural Network (GNN). To evaluate the performance of our method, two well-known datasets have been used. The results demonstrate the effectiveness and reliability of our solution compared to state-of-the-art metrics.

Despite the recent success of multi-task learning and pre-finetuning for natural language understanding, few works have studied the effects of task families on abstractive text summarization. Task families are a form of task grouping during the pre-finetuning stage to learn common skills, such as reading comprehension. To close this gap, we analyze the influence of multi-task learning strategies using task families for the English abstractive text summarization task. We group tasks into one of three strategies, i.e., sequential, simultaneous, and continual multi-task learning, and evaluate trained models through two downstream tasks. We find that certain combinations of task families (e.g., advanced reading comprehension and natural language inference) positively impact downstream performance. Further, we find that choice and combinations of task families influence downstream performance more than the training scheme, supporting the use of task families for abstractive text summarization.

The recent success of large language models for text generation poses a severe threat to academic integrity, as plagiarists can generate realistic paraphrases indistinguishable from original work. However, the role of large autoregressive transformers in generating machine-paraphrased plagiarism and their detection is still developing in the literature. This work explores T5 and GPT-3 for machine-paraphrase generation on scientific articles from arXiv, student theses, and Wikipedia. We evaluate the detection performance of six automated solutions and one commercial plagiarism detection software and perform a human study with 105 participants regarding their detection performance and the quality of generated examples. Our results suggest that large models can rewrite text humans have difficulty identifying as machine-paraphrased (53% mean acc.). Human experts rate the quality of paraphrases generated by GPT-3 as high as original texts (clarity 4.0/5, fluency 4.2/5, coherence 3.8/5). The best-performing detection model (GPT-3) achieves a 66% F1-score in detecting paraphrases.

Deep generative models parametrized up to a normalizing constant (e.g. energy-based models) are difficult to train by maximizing the likelihood of the data because the likelihood and/or gradients thereof cannot be explicitly or efficiently written down. Score matching is a training method, whereby instead of fitting the likelihood $\log p(x)$ for the training data, we instead fit the score function $\nabla_x \log p(x)$ -- obviating the need to evaluate the partition function. Though this estimator is known to be consistent, its unclear whether (and when) its statistical efficiency is comparable to that of maximum likelihood -- which is known to be (asymptotically) optimal. We initiate this line of inquiry in this paper, and show a tight connection between statistical efficiency of score matching and the isoperimetric properties of the distribution being estimated -- i.e. the Poincar\'e, log-Sobolev and isoperimetric constant -- quantities which govern the mixing time of Markov processes like Langevin dynamics. Roughly, we show that the score matching estimator is statistically comparable to the maximum likelihood when the distribution has a small isoperimetric constant. Conversely, if the distribution has a large isoperimetric constant -- even for simple families of distributions like exponential families with rich enough sufficient statistics -- score matching will be substantially less efficient than maximum likelihood. We suitably formalize these results both in the finite sample regime, and in the asymptotic regime. Finally, we identify a direct parallel in the discrete setting, where we connect the statistical properties of pseudolikelihood estimation with approximate tensorization of entropy and the Glauber dynamics.

自适应多机构系统（AMAS）将机器学习问题转变为代理之间的本地合作问题。我们提出了Smapy，这是一种基于合奏的AMA用于移动性预测的实施，除合作规则外，还为其代理提供了机器学习模型。通过详细的方法，我们表明，如果将线性模型集成到合作多代理结构中，则可以在基准传输模式检测数据集上使用线性模型进行非线性分类。获得的结果表明，由于多代理方法，在非线性环境中线性模型的性能有了显着改善。

弱监督（WS）是一种有力的方法，可以构建标记的数据集，面对几乎没有标记的数据，用于培训监督模型。它用标签函数（LFS）表达的多个嘈杂但廉价标签的估计取代了手持标签数据。尽管它已成功地用于许多域中，但弱监督的应用程序范围受到构造具有复杂或高维特征的域的标记功能的困难。为了解决这个问题，少数方法提出了使用一小部分地面真实标签自动化LF设计过程的方法。在这项工作中，我们介绍了aettos-bench-101：在挑战WS设置中评估自动化WS（autows）技术的框架 - 以前难以或不可能应用传统的WS技术是一组不同的应用程序域。虽然AtoW是扩展WS应用程序范围的有希望的方向，但诸如零击基础模型之类的强大方法的出现揭示了需要了解介绍技术如何与现代零射击或几次学习者进行比较或合作。这为autows-bench-101的中心问题提供了信息：给定每个任务的初始集100个标签，我们询问从业者是否应使用autows方法生成其他标签或使用一些简单的基线，例如来自基础模型或监督学习。我们观察到，在许多情况下，如果启动方法要超越基础模型的信号，则有必要超越简单的几个基线，而autows bench-101可以促进该方向的未来研究。我们以详尽的介绍方法进行彻底消融研究。