Thèses sur le sujet « Self-supervised learning (artificial intelligence) »
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Denize, Julien. « Self-supervised representation learning and applications to image and video analysis ». Electronic Thesis or Diss., Normandie, 2023. http://www.theses.fr/2023NORMIR37.
Texte intégralIn this thesis, we develop approaches to perform self-supervised learning for image and video analysis. Self-supervised representation learning allows to pretrain neural networks to learn general concepts without labels before specializing in downstream tasks faster and with few annotations. We present three contributions to self-supervised image and video representation learning. First, we introduce the theoretical paradigm of soft contrastive learning and its practical implementation called Similarity Contrastive Estimation (SCE) connecting contrastive and relational learning for image representation. Second, SCE is extended to global temporal video representation learning. Lastly, we propose COMEDIAN a pipeline for local-temporal video representation learning for transformers. These contributions achieved state-of-the-art results on multiple benchmarks and led to several academic and technical published contributions
Nett, Ryan. « Dataset and Evaluation of Self-Supervised Learning for Panoramic Depth Estimation ». DigitalCommons@CalPoly, 2020. https://digitalcommons.calpoly.edu/theses/2234.
Texte intégralStanescu, Ana. « Semi-supervised learning for biological sequence classification ». Diss., Kansas State University, 2015. http://hdl.handle.net/2097/35810.
Texte intégralDepartment of Computing and Information Sciences
Doina Caragea
Successful advances in biochemical technologies have led to inexpensive, time-efficient production of massive volumes of data, DNA and protein sequences. As a result, numerous computational methods for genome annotation have emerged, including machine learning and statistical analysis approaches that practically and efficiently analyze and interpret data. Traditional machine learning approaches to genome annotation typically rely on large amounts of labeled data in order to build quality classifiers. The process of labeling data can be expensive and time consuming, as it requires domain knowledge and expert involvement. Semi-supervised learning approaches that can make use of unlabeled data, in addition to small amounts of labeled data, can help reduce the costs associated with labeling. In this context, we focus on semi-supervised learning approaches for biological sequence classification. Although an attractive concept, semi-supervised learning does not invariably work as intended. Since the assumptions made by learning algorithms cannot be easily verified without considerable domain knowledge or data exploration, semi-supervised learning is not always "safe" to use. Advantageous utilization of the unlabeled data is problem dependent, and more research is needed to identify algorithms that can be used to increase the effectiveness of semi-supervised learning, in general, and for bioinformatics problems, in particular. At a high level, we aim to identify semi-supervised algorithms and data representations that can be used to learn effective classifiers for genome annotation tasks such as cassette exon identification, splice site identification, and protein localization. In addition, one specific challenge that we address is the "data imbalance" problem, which is prevalent in many domains, including bioinformatics. The data imbalance phenomenon arises when one of the classes to be predicted is underrepresented in the data because instances belonging to that class are rare (noteworthy cases) or difficult to obtain. Ironically, minority classes are typically the most important to learn, because they may be associated with special cases, as in the case of splice site prediction. We propose two main techniques to deal with the data imbalance problem, namely a technique based on "dynamic balancing" (augmenting the originally labeled data only with positive instances during the semi-supervised iterations of the algorithms) and another technique based on ensemble approaches. The results show that with limited amounts of labeled data, semisupervised approaches can successfully leverage the unlabeled data, thereby surpassing their completely supervised counterparts. A type of semi-supervised learning, known as "transductive" learning aims to classify the unlabeled data without generalizing to new, previously not encountered instances. Theoretically, this aspect makes transductive learning particularly suitable for the task of genome annotation, in which an entirely sequenced genome is typically available, sometimes accompanied by limited annotation. We study and evaluate various transductive approaches (such as transductive support vector machines and graph based approaches) and sequence representations for the problems of cassette exon identification. The results obtained demonstrate the effectiveness of transductive algorithms in sequence annotation tasks.
Abou-Moustafa, Karim. « Metric learning revisited : new approaches for supervised and unsupervised metric learning with analysis and algorithms ». Thesis, McGill University, 2012. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=106370.
Texte intégralDans cette thèse, je propose deux algorithmes pour l'apprentissage de la métrique dX; le premier pour l'apprentissage supervisé, et le deuxième pour l'apprentissage non-supervisé, ainsi que pour l'apprentissage supervisé et semi-supervisé. En particulier, je propose des algorithmes qui prennent en considération la structure et la géométrie de X d'une part, et les caractéristiques des ensembles de données du monde réel d'autre part. Cependant, si on cherche également la réduction de dimension, donc sous certaines hypothèses légères sur la topologie de X, et en même temps basé sur des informations disponibles a priori, on peut apprendre une intégration de X dans un espace Euclidien de petite dimension Rp0 p0 << p, où la distance Euclidienne révèle mieux les ressemblances entre les éléments de X et leurs groupements (clusters). Alors, comme un sous-produit, on obtient simultanément une réduction de dimension et un apprentissage métrique. Pour l'apprentissage supervisé, je propose PARDA, ou Pareto discriminant analysis, pour la discriminante réduction linéaire de dimension. PARDA est basé sur le mécanisme d'optimisation à multi-objectifs; optimisant simultanément plusieurs fonctions objectives, éventuellement des fonctions contradictoires. Cela permet à PARDA de s'adapter à la topologie de classe dans un espace dimensionnel plus petit, et naturellement gère le problème de masquage de classe associé au discriminant Fisher dans le cadre d'analyse de problèmes à multi-classes. En conséquence, PARDA permet des meilleurs résultats de classification par rapport aux techniques modernes de réduction discriminante de dimension. Pour l'apprentissage non-supervisés, je propose un cadre algorithmique, noté par ??, qui encapsule les algorithmes spectraux d'apprentissage formant an algorithme d'apprentissage de métrique. Le cadre ?? capture la structure locale et la densité locale d'information de chaque point dans un ensemble de données, et donc il porte toutes les informations sur la densité d'échantillon différente dans l'espace d'entrée. La structure de ?? induit deux métriques de distance pour ses éléments: la métrique Bhattacharyya-Riemann dBR et la métrique Jeffreys-Riemann dJR. Les deux mesures réorganisent la proximité entre les points de X basé sur la structure locale et la densité autour de chaque point. En conséquence, lorsqu'on combine l'espace métrique (??, dBR) ou (??, dJR) avec les algorithmes de "spectral clustering" et "Euclidean embedding", ils donnent des améliorations significatives dans les précisions de regroupement et les taux d'erreur pour une grande variété de tâches de clustering et de classification.
Halpern, Yonatan. « Semi-Supervised Learning for Electronic Phenotyping in Support of Precision Medicine ». Thesis, New York University, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10192124.
Texte intégralMedical informatics plays an important role in precision medicine, delivering the right information to the right person, at the right time. With the introduction and widespread adoption of electronic medical records, in the United States and world-wide, there is now a tremendous amount of health data available for analysis.
Electronic record phenotyping refers to the task of determining, from an electronic medical record entry, a concise descriptor of the patient, comprising of their medical history, current problems, presentation, etc. In inferring such a phenotype descriptor from the record, a computer, in a sense, "understands'' the relevant parts of the record. These phenotypes can then be used in downstream applications such as cohort selection for retrospective studies, real-time clinical decision support, contextual displays, intelligent search, and precise alerting mechanisms.
We are faced with three main challenges:
First, the unstructured and incomplete nature of the data recorded in the electronic medical records requires special attention. Relevant information can be missing or written in an obscure way that the computer does not understand.
Second, the scale of the data makes it important to develop efficient methods at all steps of the machine learning pipeline, including data collection and labeling, model learning and inference.
Third, large parts of medicine are well understood by health professionals. How do we combine the expert knowledge of specialists with the statistical insights from the electronic medical record?
Probabilistic graphical models such as Bayesian networks provide a useful abstraction for quantifying uncertainty and describing complex dependencies in data. Although significant progress has been made over the last decade on approximate inference algorithms and structure learning from complete data, learning models with incomplete data remains one of machine learning’s most challenging problems. How can we model the effects of latent variables that are not directly observed?
The first part of the thesis presents two different structural conditions under which learning with latent variables is computationally tractable. The first is the "anchored'' condition, where every latent variable has at least one child that is not shared by any other parent. The second is the "singly-coupled'' condition, where every latent variable is connected to at least three children that satisfy conditional independence (possibly after transforming the data).
Variables that satisfy these conditions can be specified by an expert without requiring that the entire structure or its parameters be specified, allowing for effective use of human expertise and making room for statistical learning to do some of the heavy lifting. For both the anchored and singly-coupled conditions, practical algorithms are presented.
The second part of the thesis describes real-life applications using the anchored condition for electronic phenotyping. A human-in-the-loop learning system and a functioning emergency informatics system for real-time extraction of important clinical variables are described and evaluated.
The algorithms and discussion presented here were developed for the purpose of improving healthcare, but are much more widely applicable, dealing with the very basic questions of identifiability and learning models with latent variables - a problem that lies at the very heart of the natural and social sciences.
Taylor, Farrell R. « Evaluation of Supervised Machine Learning for Classifying Video Traffic ». NSUWorks, 2016. http://nsuworks.nova.edu/gscis_etd/972.
Texte intégralCoursey, Kino High. « An Approach Towards Self-Supervised Classification Using Cyc ». Thesis, University of North Texas, 2006. https://digital.library.unt.edu/ark:/67531/metadc5470/.
Texte intégralLivi, Federico. « Supervised Learning with Graph Structured Data for Transprecision Computing ». Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/19714/.
Texte intégralRossi, Alex. « Self-supervised information retrieval : a novel approach based on Deep Metric Learning and Neural Language Models ». Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.
Trouver le texte intégralStroulia, Eleni. « Failure-driven learning as model-based self-redesign ». Diss., Georgia Institute of Technology, 1994. http://hdl.handle.net/1853/8291.
Texte intégralWatkins, Andrew B. « AIRS : a resource limited artificial immune classifier ». Master's thesis, Mississippi State : Mississippi State University, 2001. http://library.msstate.edu/etd/show.asp?etd=etd-11052001-102048.
Texte intégralApprey-Hermann, Joseph Kwame. « Evaluating The Predictability of Pseudo-Random Number Generators Using Supervised Machine Learning Algorithms ». Youngstown State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1588805461290138.
Texte intégralNasrin, Mst Shamima. « Pathological Image Analysis with Supervised and Unsupervised Deep Learning Approaches ». University of Dayton / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1620052562772676.
Texte intégralOthmani-Guibourg, Mehdi. « Supervised learning for distribution of centralised multiagent patrolling strategies ». Electronic Thesis or Diss., Sorbonne université, 2019. http://www.theses.fr/2019SORUS534.
Texte intégralFor nearly two decades, patrolling has received significant attention from the multiagent community. Multiagent patrolling (MAP) consists in modelling a patrol task to optimise as a multiagent system. The problem of optimising a patrol task is to distribute the most efficiently agents over the area to patrol in space and time, which constitutes a decision-making problem. A range of algorithms based on reactive, cognitive, reinforcement learning, centralised and decentralised strategies, amongst others, have been developed to make such a task ever more efficient. However, the existing patrolling-specific approaches based on supervised learning were still at preliminary stages, although a few works addressed this issue. Central to supervised learning, which is a set of methods and tools that allow inferring new knowledge, is the idea of learning a function mapping any input to an output from a sample of data composed of input-output pairs; learning, in this case, enables the system to generalise to new data never observed before. Until now, the best online MAP strategy, namely without precalculation, has turned out to be a centralised strategy with a coordinator. However, as for any centralised decision process in general, such a strategy is hardly scalable. The purpose of this work is then to develop and implement a new methodology aiming at turning any high-performance centralised strategy into a distributed strategy. Indeed, distributed strategies are by design resilient, more adaptive to changes in the environment, and scalable. In doing so, the centralised decision process, generally represented in MAP by a coordinator, is distributed into patrolling agents by means of supervised learning methods, so that each agent of the resultant distributed strategy tends to capture a part of the algorithm executed by the centralised decision process. The outcome is a new distributed decision-making algorithm based on machine learning. In this dissertation therefore, such a procedure of distribution of centralised strategy is established, then concretely implemented using some artificial neural networks architectures. By doing so, after having exposed the context and motivations of this work, we pose the problematic that led our study. The main multiagent strategies devised until now as part of MAP are then described, particularly a high-performance coordinated strategy, which is the centralised strategy studied in this work, as well as a simple decentralised strategy used as reference for decentralised strategies. Among others, some existing strategies based on supervised learning are also described. Thereafter, the model as well as certain of key concepts of MAP are defined. We also define the methodology laid down to address and study this problematic. This methodology comes in the form of a procedure that allows decentralising any centralised strategy by means of supervised learning. Then, the software ecosystem we developed for the needs of this work is also described, particularly PyTrol a discrete-time simulator dedicated to MAP developed with the aim of performing MAP simulation, to assess strategies and generate data, and MAPTrainer, a framework hinging on the PyTorch machine learning library, dedicated to research in machine learning in the context of MAP
Mao, Yi. « Domain knowledge, uncertainty, and parameter constraints ». Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37295.
Texte intégralCalderon-Vilca, Hugo D., William I. Wun-Rafael et Roberto Miranda-Loarte. « Simulation of suicide tendency by using machine learning ». IEEE Computer Society, 2018. http://hdl.handle.net/10757/624720.
Texte intégralSuicide is one of the most distinguished causes of death on the news worldwide. There are several factors and variables that can lead a person to commit this act, for example, stress, self-esteem, depression, among others. The causes and profiles of suicide cases are not revealed in detail by the competent institutions. We propose a simulation with a systematically generated dataset; such data reflect the adolescent population with suicidal tendency in Peru. We will evaluate three algorithms of supervised machine learning as a result of the algorithm C4.5 which is based on the trees to classify in a better way the suicidal tendency of adolescents. We finally propose a desktop tool that determines the suicidal tendency level of the adolescent.
Revisión por pares
Charnay, Clément. « Enhancing supervised learning with complex aggregate features and context sensitivity ». Thesis, Strasbourg, 2016. http://www.theses.fr/2016STRAD025/document.
Texte intégralIn this thesis, we study model adaptation in supervised learning. Firstly, we adapt existing learning algorithms to the relational representation of data. Secondly, we adapt learned prediction models to context change.In the relational setting, data is modeled by multiples entities linked with relationships. We handle these relationships using complex aggregate features. We propose stochastic optimization heuristics to include complex aggregates in relational decision trees and Random Forests, and assess their predictive performance on real-world datasets.We adapt prediction models to two kinds of context change. Firstly, we propose an algorithm to tune thresholds on pairwise scoring models to adapt to a change of misclassification costs. Secondly, we reframe numerical attributes with affine transformations to adapt to a change of attribute distribution between a learning and a deployment context. Finally, we extend these transformations to complex aggregates
Buchi, Baptiste. « Learning system for self-reconfiguration of micro-robot networks ». Electronic Thesis or Diss., Bourgogne Franche-Comté, 2023. http://www.theses.fr/2023UBFCA017.
Texte intégralThe problem of self-reconfiguration of micro-robot networks is one of the major challenges of modular robotics. A set of micro-robots connected by electromagnetic or mechanical links reorganize themselves in order to reach given target shapes. The self-reconfiguration problem is a complex problem for three reasons. First, the number of distinct configurations of a modular robot network is very high. Secondly, as the modules are free to move independently of each other, from each configuration it is possible to reach a very high number of other configurations. Thirdly and as a consequence of the previous point, the search space connecting two configurations is exponential which prevents the determination of the optimal schedule of the self-reconfiguration.In this work, we propose, firstly, a distributed autonomous self-reconfiguration approach TBSR, focused on the optimization of movements for a better distribution of tasks. In other words, it involves distributing the effort made by each robot to reach the final shape.Secondly, we propose hybrid approaches that take advantage of the advantages of centralized methods and distributed methods. These approaches make it possible to select the best distributed algorithm before launching the reconfiguration procedure. A range of distributed algorithms are pre-installed on each modular robot. At the start of the self-reconfiguration procedure, a coordinator broadcasts to all the micro-robots the data relating to the final shape to be achieved and the distributed algorithm.To do this, we determined the relevant characteristics of self-reconfiguration problems allowing us to identify the most suitable algorithmic approach.A study of the impact of each reconfiguration method and performance parameters was conducted to establish a knowledge base. This database records the performance of various algorithms based on different parameters for a diverse range of self-reconfiguration problem scenarios.Using a classification system, it is thus possible to establish for each self-reconfiguration method the characteristics of the self-reconfiguration scenarios for which it is effective. The learning mechanisms developed by AI (e.g., neural networks) are implemented. A first proposed hybrid CNNSR approach uses artificial neural networks to predict the optimal approach for self-reconfiguration. A CNN2SR approach (an improved version of CNNSR), was introduced for accuracy and error reduction, by refining the classification.Thirdly, a modeling of energy consumption, resulting from real experiments with physical modular robots (Catom 2D) was established. This made it possible to implement a third hybrid CNN3SR approach focused on energy optimization for modular robots
Dhyani, Dushyanta Dhyani. « Boosting Supervised Neural Relation Extraction with Distant Supervision ». The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1524095334803486.
Texte intégralBalasubramanian, Krishnakumar. « Learning without labels and nonnegative tensor factorization ». Thesis, Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/33926.
Texte intégralMinakshi, Mona. « A Machine Learning Framework to Classify Mosquito Species from Smart-phone Images ». Scholar Commons, 2018. https://scholarcommons.usf.edu/etd/7340.
Texte intégralYu, Chen-Ping. « Computational model of MST neuron receptive field and interaction effect for the perception of self-motion / ». Online version of thesis, 2008. http://hdl.handle.net/1850/9588.
Texte intégralCampbell, Benjamin W. « Supervised and Unsupervised Machine Learning Strategies for Modeling Military Alliances ». The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1558024695617708.
Texte intégralKilinc, Ismail Ozsel. « Graph-based Latent Embedding, Annotation and Representation Learning in Neural Networks for Semi-supervised and Unsupervised Settings ». Scholar Commons, 2017. https://scholarcommons.usf.edu/etd/7415.
Texte intégralLeoni, Cristian. « Interpretation of Dimensionality Reduction with Supervised Proxies of User-defined Labels ». Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-105622.
Texte intégralCao, Xi Hang. « On Leveraging Representation Learning Techniques for Data Analytics in Biomedical Informatics ». Diss., Temple University Libraries, 2019. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/586006.
Texte intégralPh.D.
Representation Learning is ubiquitous in state-of-the-art machine learning workflow, including data exploration/visualization, data preprocessing, data model learning, and model interpretations. However, the majority of the newly proposed Representation Learning methods are more suitable for problems with a large amount of data. Applying these methods to problems with a limited amount of data may lead to unsatisfactory performance. Therefore, there is a need for developing Representation Learning methods which are tailored for problems with ``small data", such as, clinical and biomedical data analytics. In this dissertation, we describe our studies of tackling the challenging clinical and biomedical data analytics problem from four perspectives: data preprocessing, temporal data representation learning, output representation learning, and joint input-output representation learning. Data scaling is an important component in data preprocessing. The objective in data scaling is to scale/transform the raw features into reasonable ranges such that each feature of an instance will be equally exploited by the machine learning model. For example, in a credit flaw detection task, a machine learning model may utilize a person's credit score and annual income as features, but because the ranges of these two features are different, a machine learning model may consider one more heavily than another. In this dissertation, I thoroughly introduce the problem in data scaling and describe an approach for data scaling which can intrinsically handle the outlier problem and lead to better model prediction performance. Learning new representations for data in the unstandardized form is a common task in data analytics and data science applications. Usually, data come in a tubular form, namely, the data is represented by a table in which each row is a feature (row) vector of an instance. However, it is also common that the data are not in this form; for example, texts, images, and video/audio records. In this dissertation, I describe the challenge of analyzing imperfect multivariate time series data in healthcare and biomedical research and show that the proposed method can learn a powerful representation to encounter various imperfections and lead to an improvement of prediction performance. Learning output representations is a new aspect of Representation Learning, and its applications have shown promising results in complex tasks, including computer vision and recommendation systems. The main objective of an output representation algorithm is to explore the relationship among the target variables, such that a prediction model can efficiently exploit the similarities and potentially improve prediction performance. In this dissertation, I describe a learning framework which incorporates output representation learning to time-to-event estimation. Particularly, the approach learns the model parameters and time vectors simultaneously. Experimental results do not only show the effectiveness of this approach but also show the interpretability of this approach from the visualizations of the time vectors in 2-D space. Learning the input (feature) representation, output representation, and predictive modeling are closely related to each other. Therefore, it is a very natural extension of the state-of-the-art by considering them together in a joint framework. In this dissertation, I describe a large-margin ranking-based learning framework for time-to-event estimation with joint input embedding learning, output embedding learning, and model parameter learning. In the framework, I cast the functional learning problem to a kernel learning problem, and by adopting the theories in Multiple Kernel Learning, I propose an efficient optimization algorithm. Empirical results also show its effectiveness on several benchmark datasets.
Temple University--Theses
Budnyk, Ivan. « Contribution to the Study and Implementation of Intelligent Modular Self-organizing Systems ». Phd thesis, Université Paris-Est, 2009. http://tel.archives-ouvertes.fr/tel-00481367.
Texte intégralKim, Seungyeon. « Novel document representations based on labels and sequential information ». Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53946.
Texte intégralVelander, Alice, et Harrysson David Gumpert. « Do Judge a Book by its Cover ! : Predicting the genre of book covers using supervised deep learning. Analyzing the model predictions using explanatory artificial intelligence methods and techniques ». Thesis, Linköpings universitet, Datorseende, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177691.
Texte intégralLindkvist, Emilie. « Learning-by-modeling : Novel Computational Approaches for Exploring the Dynamics of Learning and Self-governance in Social-ecological Systems ». Doctoral thesis, Stockholms universitet, Stockholm Resilience Centre, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-122395.
Texte intégralI vårt antropocena tidevarv är ett långsiktigt förvaltarskap av naturresurser inom social-ekologiska system av yttersta vikt. Detta kräver en djup förståelse av människan, ekologin, interaktionerna sinsemellan och deras utveckling över tid. Syftet med denna avhandling är att nå en djupare och mer nyanserad förståelse kring två av grundpelarna inom forskningen av hållbar förvaltning av naturresurser–kontinuerligt lärande genom learning-by-doing (LBD) för att förstå naturresursens dynamik, samt vad som kan kallas socialt kapital, i detta sammanhang i betydelsen tillit mellan individer, som naturligtvis ligger till grund för framgångsrik gemensam förvaltning. Denna föresats operationaliseras genom att använda två olika simuleringsmodeller. Den ena modellen undersöker hur en hållbar förvaltning av en förnyelsebar resurs, i denna avhandling exemplifierad av en fiskepopulation, kan uppnås genom LBD. Den andra modellen söker blottlägga det komplexa sociala samspel som krävs för att praktisera gemensam förvaltning genom att använda ett fiskesamhälle som fallstudie. Tidigare forskning på båda dessa två områden är relativt omfattade. Emellertid har den forskning som specialiserat sig på LBD i huvudsak inskränkt sig till empiriska fallstudier. Vad som bryter ny mark i denna avhandling är att vi konstruerar en simuleringsmodell av LBD där vi kan studera lärandeprocessen i detalj för att uppnå en mer hållbar förvaltning över tid. Beträffande modellen som behandlar socialt kapital så har tidigare forskning fokuserat på hur en organisation, eller grupp, kan uppnå hållbar förvaltning. Dock saknas ett helhetsgrepp där som tar hänsyn till alla nivåer; från individnivå (mikro), via gruppnivå (meso), till samhällsnivå (makro). Detta är något som denna avhandling försöker avhjälpa genom att undersöka betydelsen av individers egenskaper, uppbyggnaden av socialt kapital, samt hur detta påverkar emergens av ett samhälle dominerat av mer kooperativa förvaltningsformer respektive mer hierarkiska diton. I papper I and II studeras kärnan av LBD som återkoppling mellan en aktör och en resurs, där aktören lär sig genom upprepade interaktioner med en resurs. Resultaten visar att LBD är av avgörande betydelse för en hållbar förvaltning, speciellt då naturresursens dynamik är stadd i förändring. I den mest hållbara strategin bör aktören värdera nuvarande och framtida fångster lika högt, försiktigt experimentera kring vad aktören upplever som bästa strategi, för att sedan anpassa sin mentala modell till upplevda förändringar i fångst relativt dess insats någorlunda kraftigt. I papper III och IV behandlas uppbyggnaden av förtroende mellan individer och grupp, samt själv-organiserat styre. Genom att använda småskaligt fiske i Mexiko som en illustrativ fallstudie, utvecklades en agent-baserad modell av ett arketypiskt småskaligt fiskesamhälle. Resultaten indikerar att kooperativa förvaltningsformer är mer dominanta i samhällen där de som utför fisket har liknande pålitlighet, starkt gemensamt socialt kapital vid kooperativets start, och då resursen fluktuerar säsongsmässigt (papper III). Papper IV visar att för att uppnå en transformation från hierarkiska förvaltningsformer till kooperativa diton krävs interventioner som inriktar sig på både socialt och finansiellt kapital. Denna avhandling bidrar således till en djupare förståelse kring hur socialt kapital växer fram, samt hur mer strategiska LBD processer bör utformas när abrupta och osäkra förändringar i ekosystemen blir allt vanligare på grund av människans ökade tryck på planeten.
At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 2: Submitted. Paper 3: Submitted. Paper 4: Manuscript.
Jezequel, Loïc. « Vers une détection d'anomalie unifiée avec une application à la détection de fraude ». Electronic Thesis or Diss., CY Cergy Paris Université, 2023. http://www.theses.fr/2023CYUN1190.
Texte intégralDetecting observations straying apart from a baseline case is becoming increasingly critical in many applications. It is found in fraud detection, medical imaging, video surveillance or even in manufacturing defect detection with data ranging from images to sound. Deep anomaly detection was introduced to tackle this challenge by properly modeling the normal class, and considering anything significantly different as anomalous. Given the anomalous class is not well-defined, classical binary classification will not be suitable and lack robustness and reliability outside its training domain. Nevertheless, the best-performing anomaly detection approaches still lack generalization to different types of anomalies. Indeed, each method is either specialized on high-scale object anomalies or low-scale local anomalies.In this context, we first introduce a more generic one-class pretext-task anomaly detector. The model, named OC-MQ, computes an anomaly score by learning to solve a complex pretext task on the normal class. The pretext task is composed of several sub-tasks allowing it to capture a wide variety of visual cues. More specifically, our model is made of two branches each representing discriminative and generative tasks.Nevertheless, an additional anomalous dataset is in reality often available in many applications and can provide harder edge-case anomalous examples. In this light, we explore two approaches for outlier-exposure. First, we generalize the concept of pretext task to outlier-exposure by dynamically learning the pretext task itself with normal and anomalous samples. We propose two the models SadTPS and SadRest that respectively learn a discriminative pretext task of thin plate transform recognition and generative task of image restoration. In addition, we present a new anomaly-distance model SadCLR, where the training of previously unreliable anomaly-distance models is stabilized by adding contrastive regularization on the representation direction. We further enrich existing anomalies by generating several types of pseudo-anomalies.Finally, we extend the two previous approaches to be usable in both one-class and outlier-exposure setting. Firstly, we introduce the AnoMem model which memorizes a set of multi-scale normal prototypes by using modern Hopfield layers. Anomaly distance estimators are then fitted on the deviations between the input and normal prototypes in a one-class or outlier-exposure manner. Secondly, we generalize learnable pretext tasks to be learned only using normal samples. Our proposed model HEAT adversarially learns the pretext task to be just challenging enough to keep good performance on normal samples, while failing on anomalies. Besides, we choose the recently proposed Busemann distance in the hyperbolic Poincaré ball model to compute the anomaly score.Extensive testing was conducted for each proposed method, varying from coarse and subtle style anomalies to a fraud detection dataset of face presentation attacks with local anomalies. These tests yielded state-of-the-art results, showing the significant success of our methods
Guerreiro, Lucas [UNESP]. « Aprendizado semi-supervisionado utilizando modelos de caminhada de partículas em grafos ». Universidade Estadual Paulista (UNESP), 2017. http://hdl.handle.net/11449/151923.
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O Aprendizado de Máquina é uma área que vem crescendo nos últimos anos e é um dos destaques dentro do campo de Inteligência Artificial. Atualmente, uma das subáreas mais estudadas é o Aprendizado Semi-Supervisionado, principalmente pela sua característica de ter um menor custo na rotulação de dados de exemplo. A categoria de modelos baseados em grafos é a mais ativa nesta subárea, fazendo uso de estruturas de redes complexas. O algoritmo de competição e cooperação entre partículas é uma das técnicas deste domínio. O algoritmo provê acurácia de classificação compatível com a de algoritmos do estado da arte, e oferece um custo computacional inferior à maioria dos métodos da mesma categoria. Neste trabalho é apresentado um estudo sobre Aprendizado Semi-Supervisionado, com ênfase em modelos baseados em grafos e, em particular, no Algoritmo de Competição e Cooperação entre Partículas (PCC). O objetivo deste trabalho é propor um novo algoritmo de competição e cooperação entre partículas baseado neste modelo, com mudanças na caminhada pelo grafo, com informações de dominância sendo mantidas nas arestas ao invés dos nós; as quais possam melhorar a acurácia de classificação ou ainda o tempo de execução em alguns cenários. É proposta também uma metodologia de avaliação da rede obtida com o modelo de competição e cooperação entre partículas, para se identificar a melhor métrica de distância a ser aplicada em cada caso. Nos experimentos apresentados neste trabalho, pode ser visto que o algoritmo proposto teve melhor acurácia do que o PCC em algumas bases de dados, enquanto o método de avaliação de métricas de distância atingiu também bom nível de precisão na maioria dos casos.
Machine Learning is an increasing area over the last few years and it is one of the highlights in Artificial Intelligence area. Nowadays, one of the most studied areas is Semi-supervised learning, mainly due to its characteristic of lower cost in labeling sample data. The most active category in this subarea is that of graph-based models, using complex networks concepts. The Particle Competition and Cooperation in Networks algorithm (PCC) is one of the techniques in this field. The algorithm provides accuracy compatible with state of the art algorithms, and it presents a lower computational cost when compared to most techniques in the same category. In this project, it is presented a research about semi-supervised learning, with focus on graphbased models and, in special, the Particle Competition and Cooperation in Networks algorithm. The objective of this study is to base proposals of new particle competition and cooperation algorithms based on this model, with new dynamics on the graph walking, keeping dominance information on the edges instead of the nodes; which may improve the accuracy classification or yet the runtime in some situations. It is also proposed a method of evaluation of the network built with the Particle Competition and Cooperation model, in order to infer the best distance metric to be used in each case. In the experiments presented in this work, it can be seen that the proposed algorithm presented better accuracy when compared to the PCC for some datasets, while the proposed distance metrics evaluation achieved a high precision level in most cases.
Oliveira, Clayton Silva. « Classificadores baseados em vetores de suporte gerados a partir de dados rotulados e não-rotulados ». Universidade de São Paulo, 2006. http://www.teses.usp.br/teses/disponiveis/3/3152/tde-22072007-192518/.
Texte intégralSemi-supervised learning is a machine learning methodology that mixes features of supervised and unsupervised learning. It allows the use of partially labeled databases (databases with labeled and unlabeled data) to train classifiers. The addition of unlabeled data, which are cheaper and generally more available than labeled data, can enhance the performance and/or decrease the costs of learning such classifiers (by diminishing the quantity of required labeled data). This work analyzes two strategies to perform semi-supervised learning, specifically with Support Vector Machines (SVMs): direct and indirect concepts. The direct strategy is currently more popular and studied; it allows the use of labeled and unlabeled data, concomitantly, in learning classifiers tasks. However, the addition of many unlabeled data can lead to very long training times. The indirect strategy is more recent; it is able to attain the advantages of the direct semi-supervised learning with shorter training times. This alternative uses the unlabeled data to pre-process the database prior to the learning task; it allows denoising and rewriting the data in better feature espaces. The main contribution of this Master thesis lies within the indirect strategy: conceptualization, experimentation, and analysis of the split learning algorithm, that can be used to perform indirect semi-supervised learning using SVMs. We have obtained promising empirical results with this algorithm, which is efficient to train better performance SVMs in shorter times from partially labeled databases.
Bharti, Pratool. « Context-based Human Activity Recognition Using Multimodal Wearable Sensors ». Scholar Commons, 2017. http://scholarcommons.usf.edu/etd/7000.
Texte intégralJones, Joshua K. « Empirically-based self-diagnosis and repair of domain knowledge ». Diss., Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/33931.
Texte intégralIAPAOLO, FABIO. « De-Individuation of the Modern Subject in the Age of Artificial Intelligence. The Case of Self-Driving Cars and Algorithms for Decision Making ». Doctoral thesis, Politecnico di Torino, 2021. http://hdl.handle.net/11583/2875755.
Texte intégralChandra, Nagasai. « Node Classification on Relational Graphs using Deep-RGCNs ». DigitalCommons@CalPoly, 2021. https://digitalcommons.calpoly.edu/theses/2265.
Texte intégralMatsubara, Edson Takashi. « Relações entre ranking, análise ROC e calibração em aprendizado de máquina ». Universidade de São Paulo, 2008. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-04032009-114050/.
Texte intégralSupervised learning has been used mostly for classification. In this work we show the benefits of a welcome shift in attention from classification to ranking. A ranker is an algorithm that sorts a set of instances from highest to lowest expectation that the instance is positive, and a ranking is the outcome of this sorting. Usually a ranking is obtained by sorting scores given by classifiers. In this work, we are concerned about novel approaches to promote the use of ranking. Therefore, we present the differences and relations between ranking and classification followed by a proposal of a novel ranking algorithm called LEXRANK, whose rankings are derived not from scores, but from a simple ranking of attribute values obtained from the training data. One very important field which uses rankings as its main input is ROC analysis. The study of decision trees and ROC analysis suggested an interesting way to visualize the tree construction in ROC graphs, which has been implemented in a system called PROGROC. Focusing on ROC analysis, we observed that the slope of segments obtained from the ROC convex hull is equivalent to the likelihood ratio, which can be converted into probabilities. Interestingly, this ROC convex hull calibration method is equivalent to Pool Adjacent Violators (PAV). Furthermore, the ROC convex hull calibration method optimizes Brier Score, and the exploration of this measure leads us to find an interesting connection between the Brier Score and ROC Curves. Finally, we also investigate rankings build in the selection method which increments the labelled set of CO-TRAINING, a semi-supervised multi-view learning algorithm
Oliverio, Vinicius. « Detecção de contradições em um sistema de aprendizado sem fim ». Universidade Federal de São Carlos, 2012. https://repositorio.ufscar.br/handle/ufscar/505.
Texte intégralUniversidade Federal de Sao Carlos
NELL (Never Ending Language Learning) is a system that seeks to learn in an infinite way, extracting structured information from unstructured web pages using the semi-supervised learning paradigm as one of its basic principles. The Read the Web (RTW) project is the project where the NELL system is contained, actually it consists of 5 modules, all of them working independently where one of the modules is called Rule Learner (RL). The RL is responsible for inducing first order rules, which are used by the system to identify patterns in the knowledge generated by the other four components of the system. These rules are induced and then represented in a syntax that has Horn clauses as base. These rules can present contradictions, and in this context this paper proposes investigate, develop and implement methods to detect and solve these contradictions so that the system can learn in a more efficient way
O NELL (Never Ending Language Learning) é um sistema que busca aprender de uma maneira contínua, extraindo informação estruturada de páginas web desestruturadas utilizando o paradigma de aprendizagem semissupervisionado como um de seus princípios básicos. O Read the Web (RTW) é o projeto no qual o sistema NELL se insere. Atualmente o NELL possui cinco módulos, todos eles trabalhando independentemente onde um desses módulos é chamado Rule Learner (RL). O RL é responsável por induzir regras de primeira ordem, as quais são utilizadas pelo sistema para identificar padrões presentes no conhecimento gerado pelos outros quatro componentes do sistema. Estas regras são induzidas e, na sequência, representadas através de uma sintaxe que tem cláusulas de Horn como base. Tais regras podem apresentar contradições, e neste contexto o presente trabalho propõe a investigação, desenvolvimento e implementação de métodos para detectar e resolver estas contradições de maneira a fazer a aprendizagem mais eficiente.
Gal, Jocelyn. « Application d’algorithmes de machine learning pour l’exploitation de données omiques en oncologie ». Electronic Thesis or Diss., Université Côte d'Azur (ComUE), 2019. http://theses.univ-cotedazur.fr/2019AZUR6026.
Texte intégralThe development of computer science in medicine and biology has generated a large volume of data. The complexity and the amount of information to be integrated for optimal decision-making in medicine have largely exceeded human capacities. These data includes demographic, clinical and radiological variables, but also biological variables and particularly omics (genomics, proteomics, transcriptomics and metabolomics) characterized by a large number of measured variables relatively to a generally small number of patients. Their analysis represents a real challenge as they are frequently "noisy" and associated with situations of multi-colinearity. Nowadays, computational power makes it possible to identify clinically relevant models within these sets of data by using machine learning algorithms. Through this thesis, our goal is to apply supervised and unsupervised learning methods, to large biological data, in order to participate in the optimization of the classification and therapeutic management of patients with various types of cancer. In the first part of this work a supervised learning method is applied to germline immunogenetic data to predict the efficacy and toxicity of immune checkpoint inhibitor therapy. In the second part, different unsupervised learning methods are compared to evaluate the contribution of metabolomics in the diagnosis and management of breast cancer. Finally, the third part of this work aims to expose the contribution that simulated therapeutic trials can make in biomedical research. The application of machine learning methods in oncology offers new perspectives to clinicians allowing them to make diagnostics faster and more accurately, or to optimize therapeutic management in terms of efficacy and toxicity
Sörman, Paulsson Elsa. « Evaluation of In-Silico Labeling for Live Cell Imaging ». Thesis, Umeå universitet, Institutionen för fysik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-180590.
Texte intégralKrundel, Ludovic. « On microelectronic self-learning cognitive chip systems ». Thesis, Loughborough University, 2016. https://dspace.lboro.ac.uk/2134/21804.
Texte intégralAndersson, Melanie, Arvola Maja et Sara Hedar. « Sketch Classification with Neural Networks : A Comparative Study of CNN and RNN on the Quick, Draw ! data set ». Thesis, Uppsala universitet, Institutionen för teknikvetenskaper, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-353504.
Texte intégralDi, Marco Lionel. « Récit d'ingénierie pédagogique en santé à l'usage de l'enseignant connecté Does the acceptance of hybrid learning affect learning approaches in France ? Blended Learning for French Health Students : Does Acceptance of a Learning Management System Influence Students’ Self-Efficacy ? » Thesis, Université Grenoble Alpes, 2020. http://www.theses.fr/2020GRALS024.
Texte intégralBackground. The general objective of this thesis was to evaluate a hybrid pedagogical method using an integrated learning environment (ILE) in the training of health professionals. Three research questions followed one after the other. Does the acceptability of blended learning affect students' learning strategies and learning approaches? Does the acceptability of an ILE affect students' self-efficacy? What characteristics of a dematerialised course make students' attention variable?Materials & Methods. We carried out 2 quantitative observational studies, as well as a single-blind observational experiment coupled with a qualitative analysis, with different classes of midwifery students of Grenoble-Alpes University Faculty of Medicine.Results. Students have suited learning approaches and strategies despite the use of a hybrid teaching method which they reject; there is no correlation between poor acceptability of the ILE and different spheres of students' self-efficacy; and the variability of attention declared by students varies according to certain factors common to those detected by artificial intelligence (type of language, slide duration…).Discussion. The internal and external validities of this work highlight the close links between interest, motivation, engagement by identification, and attention. It is thus possible to put forward principles of pedagogical engineering in health within the framework of dematerialized courses focusing on the content, form and type of knowledge capsule. Finally, the health teacher must above all be “connected to” the students, so that technical developments can be adapted to their needs
Albilani, Mohamad. « Neuro-symbolic deep reinforcement learning for safe urban driving using low-cost sensors ». Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAS008.
Texte intégralThe research conducted in this thesis is centered on the domain of safe urban driving, employing sensor fusion and reinforcement learning methodologies for the perception and control of autonomous vehicles (AV). The evolution and widespread integration of machine learning technologies have primarily propelled the proliferation of autonomous vehicles in recent years. However, substantial progress is requisite before achieving widespread adoption by the general populace. To accomplish its automation, autonomous vehicles necessitate the integration of an array of costly sensors, including cameras, radars, LiDARs, and ultrasonic sensors. In addition to their financial burden, these sensors exhibit susceptibility to environmental variables such as weather, a limitation not shared by human drivers who can navigate diverse conditions with a reliance on simple frontal vision. Moreover, the advent of decision-making neural network algorithms constitutes the core intelligence of autonomous vehicles. Deep Reinforcement Learning solutions, facilitating end-to-end driver policy learning, have found application in elementary driving scenarios, encompassing tasks like lane-keeping, steering control, and acceleration management. However, these algorithms demand substantial time and extensive datasets for effective training. In addition, safety must be considered throughout the development and deployment phases of autonomous vehicles.The first contribution of this thesis improves vehicle localization by fusing data from GPS and IMU sensors with an adaptation of a Kalman filter, ES-EKF, and a reduction of noise in IMU measurements.This method excels in urban environments marked by signal obstructions and elevated noise levels, effectively mitigating the adverse impact of noise in IMU sensor measurements, thereby maintaining localization accuracy and robustness. The algorithm is deployed and tested employing ground truth data on an embedded microcontroller. The second contribution introduces the DPPO-IL (Dynamic Proximal Policy Optimization with Imitation Learning) algorithm, designed to facilitate end-to-end automated parking while maintaining a steadfast focus on safety. This algorithm acquires proficiency in executing optimal parking maneuvers while navigating static and dynamic obstacles through exhaustive training incorporating simulated and real-world data.The third contribution is an end-to-end urban driving framework called GHRL. It incorporates vision and localization data and expert demonstrations expressed in the Answer Set Programming (ASP) rules to guide the hierarchical reinforcement learning (HRL) exploration policy and speed up the learning algorithm's convergence. When a critical situation occurs, the system relies on safety rules, which empower it to make prudent choices amidst unpredictable or hazardous conditions. GHRL is evaluated on the Carla NoCrash benchmark, and the results show that by incorporating logical rules, GHRL achieved better performance over state-of-the-art algorithms
Dsouza, Rodney Gracian. « Deep Learning Based Motion Forecasting for Autonomous Driving ». The Ohio State University, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1619139403696822.
Texte intégralGiuliani, Luca. « Extending the Moving Targets Method for Injecting Constraints in Machine Learning ». Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23885/.
Texte intégralAnsarnia, Masoomeh. « Development and Test of Computer Vision and Deep Learning Methods for Dynamic Management of Urban Lighting ». Electronic Thesis or Diss., Université de Lorraine, 2023. http://www.theses.fr/2023LORR0272.
Texte intégralThis doctoral thesis has been conducted within the framework of a research contract between the French urban lighting design and manufacturing company, Eclatec, and the Jean Lamour Institute in Nancy. The overarching goal of this research is to enhance nighttime urban lighting while simultaneously reducing electrical consumption and light pollution. To achieve this, an RGB camera is integrated into the streetlamp's light source, serving as the primary data collection point. This choice necessitated the use of a wide-angle lens with a slight vertical tilt in its axis. Although this configuration allows for the observation of a significant portion of the illuminated area, it results in highly distorted images. From this system, four major research challenges were investigated:1. The first challenge concerns video detection of individuals in close proximity to the luminaire under very low lighting conditions, with the aim of achieving dynamic lighting adjustment. This detection relies on deep learning models from the Yolo family, which were fine-tuned through transfer learning using a specific collection of images. These images were captured at various locations in the Nancy metropolitan area, at heights ranging from 6 to 8 meters. Under conditions of 10 lux illumination, an aperture of f/3.5, and a fixed sensitivity of 3200 ISO, the detection rate for pedestrians and vehicles exceeds 97%. The model, implemented on the embedded NVidia Jetson Nano GPU, achieves a frame rate of approximately 10 FPS, which proves adequate for our application. 2. The second research direction explores the recognition of the environment surrounding the luminaire through semantic segmentation of images. This segmentation will subsequently be employed to adapt the light distribution of the LED matrix to the encountered urban scenario. To accomplish this, we employed the OCR-HRNet neural network, which enhances high-resolution segmentation by incorporating contextual representation that considers pixel aggregation. This architecture is well-suited to images of non-uniform surfaces, characteristic of the ground beneath the luminaire. The results demonstrate excellent identification of structures and vegetated areas. However, the distinction between sidewalk and road remains challenging, particularly when road surfaces exhibit similar reflectance and textures. A post-image virtual marking solution significantly improves segmentation accuracy, especially in sunny scenes with numerous shadowed areas. 3. In a third phase, we modeled the optical system to enable the estimation of the real-world positions of ground points based on their images. A simple Cam To World transformation is proposed, accounting for extrinsic parameters of the viewpoint (height, pitch, and resolution), and the lens distortion function, approximated as an equidistant projection law. Given that stringent precision is not critical, a rigorous system calibration was not conducted. For an effective observation zone of 20 m × 50 m, the localization error is on the order of meters. 4. Finally, we propose an avenue for utilizing the lighting infrastructure to analyze traffic flow fluidity. The proposed method analyzes apparent motion of users by estimating the mean optical flow within each bounding box detected by Yolo. Currently, optical flow determination is performed offline using the deep learning algorithm FlowNet2. In the range of 0 to 15 m/s, the estimated speed of the moving object exhibits an error of less than 1 m/s
Rabe, Erik, et Zacharias Sundlöf. « Bidragande faktorer till attityder gentemot implementering av AI-styrda fordon ». Thesis, Uppsala universitet, Institutionen för informatik och media, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-417547.
Texte intégralArtificial intelligence is a form of technology that is becoming increasingly more common within society. As the technology evolves, the discussion within the subject is also increasing which has made information about eventual problems and possibilities more public. There is a shortage of thoughts and expectations from the private individual’s point of view regarding this topic which can be a negative thing due to this group being expected to make up the majority of the technology’s user base. Because this type of technology is predicted to take on a larger responsibility of human tasks it is important to clarify different approaches and development perspectives in order to create a healthy and well-functioning AI-system within respective areas. The study intends to highlight contributing factors to attitudes and opinions specifically related to AI-controlled vehicles from the public's view as well as how these can affect an eventual implementation and is carried out with a qualitative method. The data that is used is gathered through semi-structured interviews with people that expressed interest in participating in the study. The analysis is based on the diffusion of innovations theory (IDT) and relevant earlier research in order to examine what influences users to adopt the technology or not. The factors that were identified to be affecting this process were worry that the technology would not work in a compatible way with human values, a demand for extensive testing as well as the possibility to reduce accidents or the affect on climate related to traffic. Several suggestions for implementation were derived from these factors which consisted of continuous expanded testing within public transport regulated by the state, clear structural rules and limitations as well as a promotion of the positive factors made possible by AI-controlled vehicles. This promotion can be done through effective communication which takes advantage of our flawed rational decision making and uses strong emotional impressions.
Zaiem, Mohamed Salah. « Informed Speech Self-supervised Representation Learning ». Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAT009.
Texte intégralFeature learning has been driving machine learning advancement with the recently proposed methods getting progressively rid of handcrafted parts within the transformations from inputs to desired labels. Self-supervised learning has emerged within this context, allowing the processing of unlabeled data towards better performance on low-labeled tasks. The first part of my doctoral work is aimed towards motivating the choices in the speech selfsupervised pipelines learning the unsupervised representations. In this thesis, I first show how conditional-independence-based scoring can be used to efficiently and optimally select pretraining tasks tailored for the best performance on a target task. The second part of my doctoral work studies the evaluation and usage of pretrained self-supervised representations. I explore, first, the robustness of current speech self-supervision benchmarks to changes in the downstream modeling choices. I propose, second, fine-tuning approaches for better efficicency and generalization