近年来，我们在自动驾驶汽车的发展中看到了显着的步骤。多家公司开始推出令人印象深刻的系统，这些系统在各种环境中工作。这些系统有时可以给人一种印象，即完全自动驾驶即将到来，我们很快就会在没有方向盘的情况下建造汽车。给予AI的自主权和控制水平的增加为人道交互的新模式提供了机会。然而，调查表明，在自动驾驶汽车中对AI提供更多控制伴随着乘客的一定程度的不安。在尝试缓解这个问题时，最近的作品通过允许乘客提供指导到视觉场景中的特定对象的命令来采取自然语言的方法。尽管如此，这只是汽车的一半，因为汽车也应该了解该命令的物理目的地，这就是我们在本文中的专注。我们提出了一个扩展，其中我们向3D目的地注释了在执行给定的命令之后需要达到的3D目的地，并在预测该目的地位置进行多个不同的基线进行评估。此外，我们介绍一个胜过适用于这种特定设置的先前作品的模型。

当前的自动驾驶汽车技术主要集中于将乘客从A点带到B。但是，已经证明乘客害怕乘坐自动驾驶汽车。减轻此问题的一种方法是允许乘客给汽车提供自然语言命令。但是，汽车可能会误解发布的命令或视觉环境，这可能导致不确定的情况。希望自动驾驶汽车检测到这些情况并与乘客互动以解决它们。本文提出了一个模型，该模型检测到命令时不确定的情况并找到引起该命令的视觉对象。可选地，包括描述不确定对象的系统生成的问题。我们认为，如果汽车可以以人类的方式解释这些物体，乘客就可以对汽车能力获得更多信心。因此，我们研究了如何（1）检测不确定的情况及其根本原因，以及（2）如何为乘客产生澄清的问题。在对Talk2CAR数据集进行评估时，我们表明所提出的模型\ acrfull {pipeline}，改善\ gls {m：模棱两可 - absolute-Increse}，与$ iou _ {.5} $相比，与不使用\ gls {pipeline {pipeline {pipeline { }。此外，我们设计了一个引用表达生成器（reg）\ acrfull {reg_model}量身定制的自动驾驶汽车设置，该设置可产生\ gls {m：流星伴侣} Meteor的相对改进，\ gls \ gls {m：rouge felative}}与最先进的REG模型相比，Rouge-L的速度快三倍。

Convolutional Neural Networks (CNNs) have demonstrated superiority in learning patterns, but are sensitive to label noises and may overfit noisy labels during training. The early stopping strategy averts updating CNNs during the early training phase and is widely employed in the presence of noisy labels. Motivated by biological findings that the amplitude spectrum (AS) and phase spectrum (PS) in the frequency domain play different roles in the animal's vision system, we observe that PS, which captures more semantic information, can increase the robustness of DNNs to label noise, more so than AS can. We thus propose early stops at different times for AS and PS by disentangling the features of some layer(s) into AS and PS using Discrete Fourier Transform (DFT) during training. Our proposed Phase-AmplituDe DisentangLed Early Stopping (PADDLES) method is shown to be effective on both synthetic and real-world label-noise datasets. PADDLES outperforms other early stopping methods and obtains state-of-the-art performance.

We present a novel neural model for modern poetry generation in French. The model consists of two pretrained neural models that are fine-tuned for the poem generation task. The encoder of the model is a RoBERTa based one while the decoder is based on GPT-2. This way the model can benefit from the superior natural language understanding performance of RoBERTa and the good natural language generation performance of GPT-2. Our evaluation shows that the model can create French poetry successfully. On a 5 point scale, the lowest score of 3.57 was given by human judges to typicality and emotionality of the output poetry while the best score of 3.79 was given to understandability.

Graph Neural Networks (GNNs) have been successfully applied in many applications in computer sciences. Despite the success of deep learning architectures in other domains, deep GNNs still underperform their shallow counterparts. There are many open questions about deep GNNs, but over-smoothing and over-squashing are perhaps the most intriguing issues. When stacking multiple graph convolutional layers, the over-smoothing and over-squashing problems arise and have been defined as the inability of GNNs to learn deep representations and propagate information from distant nodes, respectively. Even though the widespread definitions of both problems are similar, these phenomena have been studied independently. This work strives to understand the underlying relationship between over-smoothing and over-squashing from a topological perspective. We show that both problems are intrinsically related to the spectral gap of the Laplacian of the graph. Therefore, there is a trade-off between these two problems, i.e., we cannot simultaneously alleviate both over-smoothing and over-squashing. We also propose a Stochastic Jost and Liu curvature Rewiring (SJLR) algorithm based on a bound of the Ollivier's Ricci curvature. SJLR is less expensive than previous curvature-based rewiring methods while retaining fundamental properties. Finally, we perform a thorough comparison of SJLR with previous techniques to alleviate over-smoothing or over-squashing, seeking to gain a better understanding of both problems.

We present a novel approach to generating news headlines in Finnish for a given news story. We model this as a summarization task where a model is given a news article, and its task is to produce a concise headline describing the main topic of the article. Because there are no openly available GPT-2 models for Finnish, we will first build such a model using several corpora. The model is then fine-tuned for the headline generation task using a massive news corpus. The system is evaluated by 3 expert journalists working in a Finnish media house. The results showcase the usability of the presented approach as a headline suggestion tool to facilitate the news production process.

We present a method for extracting a multilingual sentiment annotated dialog data set from Fallout New Vegas. The game developers have preannotated every line of dialog in the game in one of the 8 different sentiments: \textit{anger, disgust, fear, happy, neutral, pained, sad } and \textit{surprised}. The game has been translated into English, Spanish, German, French and Italian. We conduct experiments on multilingual, multilabel sentiment analysis on the extracted data set using multilingual BERT, XLMRoBERTa and language specific BERT models. In our experiments, multilingual BERT outperformed XLMRoBERTa for most of the languages, also language specific models were slightly better than multilingual BERT for most of the languages. The best overall accuracy was 54\% and it was achieved by using multilingual BERT on Spanish data. The extracted data set presents a challenging task for sentiment analysis. We have released the data, including the testing and training splits, openly on Zenodo. The data set has been shuffled for copyright reasons.

Word order, an essential property of natural languages, is injected in Transformer-based neural language models using position encoding. However, recent experiments have shown that explicit position encoding is not always useful, since some models without such feature managed to achieve state-of-the art performance on some tasks. To understand better this phenomenon, we examine the effect of removing position encodings on the pre-training objective itself (i.e., masked language modelling), to test whether models can reconstruct position information from co-occurrences alone. We do so by controlling the amount of masked tokens in the input sentence, as a proxy to affect the importance of position information for the task. We find that the necessity of position information increases with the amount of masking, and that masked language models without position encodings are not able to reconstruct this information on the task. These findings point towards a direct relationship between the amount of masking and the ability of Transformers to capture order-sensitive aspects of language using position encoding.

人类和神经语言模型都能够执行主题 - 动词数协议（SVA）。原则上，语义不应干扰此任务，这仅需要句法知识。在这项工作中，我们测试含义是否干扰了各种复杂性的句法结构中的英语一致性。为此，我们同时生成语义上良好的和荒谬的项目。我们将Bert Base与人类的表现进行了比较，该表现是通过心理语言在线众包实验获得的。我们发现伯特和人类都对我们的语义操纵敏感：出现荒谬的项目时，它们的频率更高，尤其是当它们的句法结构具有吸引子（主题和动词之间的名词短语和与该数字不同的名词短语）时主题）。我们还发现，有意义性对SVA错误的影响对于BERT而言比对人类的影响更强，显示前者对这项任务的词汇敏感性更高。

基于草图的图像检索（SBIR）是检索与语义和手绘草图查询的空间配置相匹配的自然图像（照片）的任务。草图的普遍性扩大了可能的应用程序的范围，并增加了对有效SBIR解决方案的需求。在本文中，我们研究了经典的基于三胞胎的SBIR解决方案，并表明对水平翻转（即使在模型登录之后）的持续不变性也损害了性能。为了克服这一限制，我们提出了几种方法，并深入评估它们每个方法以检查其有效性。我们的主要贡献是双重的：我们提出并评估几种直观的修改，以构建具有更好的翻转均衡性的SBIR解决方案。我们表明，视觉变压器更适合SBIR任务，并且它们的优于CNN的优于较大的CNN。我们进行了许多实验，并引入了第一个模型，以优于大规模SBIR基准（粗略）的人类表现。与以前的最新方法相比，我们的最佳模型在粗略的基准测试中达到了62.25％（在k = 1）的召回率为46.2％。