深层网络的监督表示学习倾向于过度培养培训课程，而对新课程的概括是一个具有挑战性的问题。经常评估在同一培训课程的固定图像上学习的嵌入。但是，在实际应用中，数据来自新来源，新颖的类可能会出现。我们假设将新颖类的未标记图像以半监督的方式纳入训练，这将有助于与香草监督的表述相比，有效地检索新颖级别的图像。为了以一种综合的方式验证这一假设，我们提出了一种原始的评估方法，该方法可以通过随机或语义上的数据集类别进行分区，即通过对基础和新颖类之间的共享语义进行最小化，从而改变了新颖类的新颖性程度。该评估程序允许盲目训练一台新型级标签，并评估基础或新型阶级检索的冷冻表示。我们发现，香草的监督表现不足，因此在新颖阶级的检索中差不多，因此当语义差距更高时。半监督算法可以部分弥合这一性能差距，但仍然有很大的改进空间。

产生表现力和上下文适当的韵律仍然是现代文本到语音（TTS）系统的挑战。对于长，多句的输入，这一点尤其明显。在本文中，我们检查了基于变压器的快速语音系统的简单扩展，目的是改善多句子TT的韵律。我们发现，漫长的上下文，强大的文本功能以及对多演讲者数据的培训都改善了韵律。更有趣的是，它们产生协同作用。长篇小说席卷了韵律，改善了连贯性，并发挥了变形金刚的优势。来自强大的语言模型（例如BERT）的微调单词级功能似乎从更多培训数据中获利，在多演讲者设置中很容易获得。我们调查有关暂停和起搏的客观指标，并对语音自然进行彻底的主观评估。我们的主要系统结合了所有扩展，取得了始终如一的良好结果，包括对所有竞争对手的言语自然性的显着改善。

在现代分类任务中，标签数量越来越大，实际上遇到的数据集的大小也越来越大。随着班级数量的增加，阶级的歧义和阶级失衡变得越来越有问题，以达到高顶级1的准确性。同时，TOP-K指标（允许K猜测的指标）变得流行，尤其是用于性能报告。然而，提出为深度学习量身定制的Top-K损失仍然是一个挑战，无论是理论上还是实际的。在本文中，我们引入了由Top-K校准损失的最新发展启发的随机TOP-K铰链损失。我们的建议基于在灵活的“扰动优化器”框架上的Top-K操作员建筑的平滑。我们表明，在平衡数据集的情况下，我们的损失函数的性能非常出色，同时，与最先进的TOP-K损失函数相比，计算时间明显低。此外，我们为不平衡案例提出了一个简单的损失变体。在重尾数据集上的实验表明，我们的损失函数显着优于其他基线损失函数。

当可能的许多标签是可能的时，选择单个可以导致低精度。一个常见的替代方案，称为顶级k $分类，是选择一些数字$ k $（通常约5），并返回最高分数的$ k $标签。不幸的是，对于明确的案例，$ k> 1 $太多，对于非常暧昧的情况，$ k \ leq 5 $（例如）太小。另一种明智的策略是使用一种自适应方法，其中返回的标签数量随着计算的歧义而变化，但必须平均到所有样本的某些特定的$ k $。我们表示这种替代方案 - $ k $分类。本文在平均值的含量较低的误差率时，本文正式地表征了模糊性曲线，比固定的顶级k $分类更低。此外，它为固定尺寸和自适应分类器提供了自然估计程序，并证明了它们的一致性。最后，它报告了实际图像数据集的实验，揭示了平均值的效益 - 在实践中的价格超过高度k $分类。总的来说，当含糊不清的歧义时，平均值-$ k $永远不会比Top-$ K $更差，并且在我们的实验中，当估计时，这也持有。

Machine learning model development and optimisation can be a rather cumbersome and resource-intensive process. Custom models are often more difficult to build and deploy, and they require infrastructure and expertise which are often costly to acquire and maintain. Machine learning product development lifecycle must take into account the need to navigate the difficulties of developing and deploying machine learning models. evoML is an AI-powered tool that provides automated functionalities in machine learning model development, optimisation, and model code optimisation. Core functionalities of evoML include data cleaning, exploratory analysis, feature analysis and generation, model optimisation, model evaluation, model code optimisation, and model deployment. Additionally, a key feature of evoML is that it embeds code and model optimisation into the model development process, and includes multi-objective optimisation capabilities.

The Graph Protocol indexes historical blockchain transaction data and makes it available for querying. As the protocol is decentralized, there are many independent Indexers that index and compete with each other for serving queries to the Consumers. One dimension along which Indexers compete is pricing. In this paper, we propose a bandit-based algorithm for maximization of Indexers' revenue via Consumer budget discovery. We present the design and the considerations we had to make for a dynamic pricing algorithm being used by multiple agents simultaneously. We discuss the results achieved by our dynamic pricing bandits both in simulation and deployed into production on one of the Indexers operating on Ethereum. We have open-sourced both the simulation framework and tools we created, which other Indexers have since started to adapt into their own workflows.

This thesis introduces quantum natural language processing (QNLP) models based on a simple yet powerful analogy between computational linguistics and quantum mechanics: grammar as entanglement. The grammatical structure of text and sentences connects the meaning of words in the same way that entanglement structure connects the states of quantum systems. Category theory allows to make this language-to-qubit analogy formal: it is a monoidal functor from grammar to vector spaces. We turn this abstract analogy into a concrete algorithm that translates the grammatical structure onto the architecture of parameterised quantum circuits. We then use a hybrid classical-quantum algorithm to train the model so that evaluating the circuits computes the meaning of sentences in data-driven tasks. The implementation of QNLP models motivated the development of DisCoPy (Distributional Compositional Python), the toolkit for applied category theory of which the first chapter gives a comprehensive overview. String diagrams are the core data structure of DisCoPy, they allow to reason about computation at a high level of abstraction. We show how they can encode both grammatical structures and quantum circuits, but also logical formulae, neural networks or arbitrary Python code. Monoidal functors allow to translate these abstract diagrams into concrete computation, interfacing with optimised task-specific libraries. The second chapter uses DisCopy to implement QNLP models as parameterised functors from grammar to quantum circuits. It gives a first proof-of-concept for the more general concept of functorial learning: generalising machine learning from functions to functors by learning from diagram-like data. In order to learn optimal functor parameters via gradient descent, we introduce the notion of diagrammatic differentiation: a graphical calculus for computing the gradients of parameterised diagrams.

As aerial robots are tasked to navigate environments of increased complexity, embedding collision tolerance in their design becomes important. In this survey we review the current state-of-the-art within the niche field of collision-tolerant micro aerial vehicles and present different design approaches identified in the literature, as well as methods that have focused on autonomy functionalities that exploit collision resilience. Subsequently, we discuss the relevance to biological systems and provide our view on key directions of future fruitful research.

Given a particular embodiment, we propose a novel method (C3PO) that learns policies able to achieve any arbitrary position and pose. Such a policy would allow for easier control, and would be re-useable as a key building block for downstream tasks. The method is two-fold: First, we introduce a novel exploration algorithm that optimizes for uniform coverage, is able to discover a set of achievable states, and investigates its abilities in attaining both high coverage, and hard-to-discover states; Second, we leverage this set of achievable states as training data for a universal goal-achievement policy, a goal-based SAC variant. We demonstrate the trained policy's performance in achieving a large number of novel states. Finally, we showcase the influence of massive unsupervised training of a goal-achievement policy with state-of-the-art pose-based control of the Hopper, Walker, Halfcheetah, Humanoid and Ant embodiments.

Compared with model-based control and optimization methods, reinforcement learning (RL) provides a data-driven, learning-based framework to formulate and solve sequential decision-making problems. The RL framework has become promising due to largely improved data availability and computing power in the aviation industry. Many aviation-based applications can be formulated or treated as sequential decision-making problems. Some of them are offline planning problems, while others need to be solved online and are safety-critical. In this survey paper, we first describe standard RL formulations and solutions. Then we survey the landscape of existing RL-based applications in aviation. Finally, we summarize the paper, identify the technical gaps, and suggest future directions of RL research in aviation.