Self-supervision for data interpretability in image classification and sample efficiency in reinforcement learning

L'apprentissage auto-surveillé (AAS), c'est-à-dire l'apprentissage de connaissances en exploitant la structure intrinsèque présente dans un ensemble de données non étiquettées, a beaucoup fait progresser l'apprentissage automatique dans la dernière décennie, et plus particulièrement dans les dernières deux années en vision informatique. Dans cet ouvrage, nous nous servons de l'AAS comme outil dans deux champs applicatifs: Pour interpréter efficacement les ensembles de données et les décisions prises par des modèles statistiques, et pour pré-entrainer un modèle d'apprentissage par renforcement pour grandement augmenter l'efficacité de son échantillonnage dans son contexte d'entraînement. Le Chapitre 1 présente les connaissances de fond nécessaires à la compréhension du reste du mémoire. Il offre un aperçu de l'apprentissage automatique, de l'apprentissage profond, de l'apprentissage auto-surveillé et de l'apprentissage par renforcement (profond). Le Chapitre 2 se détourne brièvement du sujet de l'auto-surveillance pour étudier comment le phénomène de la mémorisation se manifeste dans les réseaux de neurones profonds. Les observations que nous ferons seront alors utilisées comme pièces justificatives pour les travaux présentés dans le Chapitre 3. Ce chapitre aborde la manière dont l'auto-surveillance peut être utilisée pour découvrir efficacement les régularités structurelles présentes dans un ensemble de données d'entraînement, estimer le degré de mémorisation de celui-ci par le modèle, et l'influence d'un échantillon d'entraînement sur les résultats pour un échantillon-test. Nous passons aussi en revue de récents travaux touchant à l'importance de mémoriser la ``longue traîne'' d'un jeu de données. Le Chapitre 4 fait la démonstration d'une combinaison d'objectifs de pré-entraînement AAS axés sur les caractéristiques des données en apprentissage par renforcement, de ce fait élevant l'efficacité d'échantillonnage à un niveau comparable à celui d'un humain. De plus, nous montrons que l'AAS ouvre la porte à de plus grands modèles, ce qui a été par le passé un défi à surmonter en apprentissage par renforcement profond. Finalement, le Chapitre 5 conclut l'ouvrage avec un bref survol des contributions scientifiques et propose quelque avenues pour des recherches poussées dans le futur.Self-Supervised Learning (SSL), or learning representations of data by exploiting inherent structure present in it without labels, has driven significant progress in machine learning over the past decade, and in computer vision in particular over the past two years. In this work, we explore applications of SSL towards two separate goals - first, as a tool for efficiently interpreting datasets and model decisions, and second, as a tool for pretraining in reinforcement learning (RL) to greatly advance sample efficiency in that setting. Chapter 1 introduces background material necessary to understand the remainder of this thesis. In particular, it provides an overview of Machine Learning, Deep Learning, Self-Supervised Representation Learning, and (Deep) Reinforcement Learning. Chapter 2 briefly detours away from this thesis' focus on self-supervision, to examine how the phenomena of memorization manifests in deep neural networks. These results are then used to partially justify work presented in Chapter 3, which examines how self-supervision can be used to efficiently uncover structural regularity in training datasets, and to estimate training memorization and the influence of training samples on test samples. Recent experimental work on understanding the importance of memorizing the long-tail of data is also revisited. Chapter 4 demonstrates how a combination of SSL pretraining objectives designed for the structure of data in RL can greatly improve sample efficiency to nearly human-level performance. Furthermore, it is shown that SSL enables the use of larger models, which has historically been a challenge in deep RL. Chapter 5 concludes by reviewing the contributions of this work, and discusses future directions

Video-efficient foundation models

The thesis strives to endow video-efficiency in video understanding by addressing the research question ''What enables video-efficient video foundation models?'' Video-efficiency encompasses developing video foundation models that are not only accurate but also exhibit label-efficiency i.e. require fewer labels, domain-efficiency i.e. applicable to a variety of video learning scenarios, and data-efficiency i.e. reduce the amount of video data needed for learning. The research question is addressed for RGB and non-RGB video modalities. In Chapter 2, we focus on improving the label- and domain-efficiency of non-RGB action recognition and detection. Chapter 3 introduces a new self-supervised approach for learning feature representations for 3D-skeleton video sequences. In Chapter 4, we conduct a large-scale study of existing RGB-based self-supervised video models to assess their performance across different facets of video-efficiency. Chapter 5 presents a new method for video self-supervision that explicitly aims to learn motion focused video-representations. To summarize, this thesis presents several novel approaches to improve the video-efficiency of video foundation models. Our research highlights the importance of transferring knowledge between RGB and non-RGB video modalities, exploring self-supervision for non-RGB video modeling, analyzing self-supervised models beyond canonical setups and carefully designing new self-supervised tasks to develop video foundation models that can exhibit different facets of video-efficiency. We hope that our work will inspire further research and development in this area, leading to even more video-efficient foundation models

Proceedings of the Eighth Italian Conference on Computational Linguistics CliC-it 2021

The eighth edition of the Italian Conference on Computational Linguistics (CLiC-it 2021) was held at Università degli Studi di Milano-Bicocca from 26th to 28th January 2022. After the edition of 2020, which was held in fully virtual mode due to the health emergency related to Covid-19, CLiC-it 2021 represented the first moment for the Italian research community of Computational Linguistics to meet in person after more than one year of full/partial lockdown

Metric Selection and Metric Learning for Matching Tasks

A quarter of a century after the world-wide web was born, we have grown accustomed to having easy access to a wealth of data sets and open-source software. The value of these resources is restricted if they are not properly integrated and maintained. A lot of this work boils down to matching; finding existing records about entities and enriching them with information from a new data source. In the realm of code this means integrating new code snippets into a code base while avoiding duplication. In this thesis, we address two different such matching problems. First, we leverage the diverse and mature set of string similarity measures in an iterative semisupervised learning approach to string matching. It is designed to query a user to make a sequence of decisions on specific cases of string matching. We show that we can find almost optimal solutions after only a small amount of such input. The low labelling complexity of our algorithm is due to addressing the cold start problem that is inherent to Active Learning; by ranking queries by variance before the arrival of enough supervision information, and by a self-regulating mechanism that counteracts initial biases. Second, we address the matching of code fragments for deduplication. Programming code is not only a tool, but also a resource that itself demands maintenance. Code duplication is a frequent problem arising especially from modern development practice. There are many reasons to detect and address code duplicates, for example to keep a clean and maintainable codebase. In such more complex data structures, string similarity measures are inadequate. In their stead, we study a modern supervised Metric Learning approach to model code similarity with Neural Networks. We find that in such a model representing the elementary tokens with a pretrained word embedding is the most important ingredient. Our results show both qualitatively (by visualization) that relatedness is modelled well by the embeddings and quantitatively (by ablation) that the encoded information is useful for the downstream matching task. As a non-technical contribution, we unify the common challenges arising in supervised learning approaches to Record Matching, Code Clone Detection and generic Metric Learning tasks. We give a novel account to string similarity measures from a psychological standpoint and point out and document one longstanding naming conflict in string similarity measures. Finally, we point out the overlap of latest research in Code Clone Detection with the field of Natural Language Processing

Neural Methods for Effective, Efficient, and Exposure-Aware Information Retrieval

Neural networks with deep architectures have demonstrated significant performance improvements in computer vision, speech recognition, and natural language processing. The challenges in information retrieval (IR), however, are different from these other application areas. A common form of IR involves ranking of documents--or short passages--in response to keyword-based queries. Effective IR systems must deal with query-document vocabulary mismatch problem, by modeling relationships between different query and document terms and how they indicate relevance. Models should also consider lexical matches when the query contains rare terms--such as a person's name or a product model number--not seen during training, and to avoid retrieving semantically related but irrelevant results. In many real-life IR tasks, the retrieval involves extremely large collections--such as the document index of a commercial Web search engine--containing billions of documents. Efficient IR methods should take advantage of specialized IR data structures, such as inverted index, to efficiently retrieve from large collections. Given an information need, the IR system also mediates how much exposure an information artifact receives by deciding whether it should be displayed, and where it should be positioned, among other results. Exposure-aware IR systems may optimize for additional objectives, besides relevance, such as parity of exposure for retrieved items and content publishers. In this thesis, we present novel neural architectures and methods motivated by the specific needs and challenges of IR tasks.Comment: PhD thesis, Univ College London (2020

AI: Limits and Prospects of Artificial Intelligence

The emergence of artificial intelligence has triggered enthusiasm and promise of boundless opportunities as much as uncertainty about its limits. The contributions to this volume explore the limits of AI, describe the necessary conditions for its functionality, reveal its attendant technical and social problems, and present some existing and potential solutions. At the same time, the contributors highlight the societal and attending economic hopes and fears, utopias and dystopias that are associated with the current and future development of artificial intelligence

Natural Language Processing: Emerging Neural Approaches and Applications

This Special Issue highlights the most recent research being carried out in the NLP field to discuss relative open issues, with a particular focus on both emerging approaches for language learning, understanding, production, and grounding interactively or autonomously from data in cognitive and neural systems, as well as on their potential or real applications in different domains

Analysis and automatic identification of spontaneous emotions in speech from human-human and human-machine communication

383 p.This research mainly focuses on improving our understanding of human-human and human-machineinteractions by analysing paricipants¿ emotional status. For this purpose, we have developed andenhanced Speech Emotion Recognition (SER) systems for both interactions in real-life scenarios,explicitly emphasising the Spanish language. In this framework, we have conducted an in-depth analysisof how humans express emotions using speech when communicating with other persons or machines inactual situations. Thus, we have analysed and studied the way in which emotional information isexpressed in a variety of true-to-life environments, which is a crucial aspect for the development of SERsystems. This study aimed to comprehensively understand the challenge we wanted to address:identifying emotional information on speech using machine learning technologies. Neural networks havebeen demonstrated to be adequate tools for identifying events in speech and language. Most of themaimed to make local comparisons between some specific aspects; thus, the experimental conditions weretailored to each particular analysis. The experiments across different articles (from P1 to P19) are hardlycomparable due to our continuous learning of dealing with the difficult task of identifying emotions inspeech. In order to make a fair comparison, additional unpublished results are presented in the Appendix.These experiments were carried out under identical and rigorous conditions. This general comparisonoffers an overview of the advantages and disadvantages of the different methodologies for the automaticrecognition of emotions in speech

The best of both worlds: exploring the socialisation of physical education specialism graduates as generalist teachers with expertise in PE

This research investigated the self-efficacy, beliefs, and practices of generalist primary school teachers who undertook the Bachelor of Education with a specialism in PE between 2016 and 2019. Data were collected through a cross-sectional survey, and 80 valid responses were received. The findings suggest self-efficacy is high among these teachers, scoring 4.14 (±0.38 on a 5-point Likert scale). Results also indicated that the specialism positively developed content knowledge and teaching skills among this cohort. However, it was unclear if these teachers were being utilised effectively upon graduation to enhance the provision of PE in Irish primary schools.N