In this work, we leverage the recent developments in computer science to address several of the challenges in current literature-based discovery (LBD) solutions. First, LBD solutions cannot use semantics or are too computational complex. To solve the problems we propose a generative model OverlapLDA based on topic modeling, which has been shown both effective and efficient in extracting semantics from a corpus. We also introduce an inference method of OverlapLDA. We conduct extensive experiments to show the effectiveness and efficiency of OverlapLDA in LBD. Second, we expand LBD to a more complex and realistic setting. The settings are that there can be more than one concept connecting the input concepts, and the connectivity pattern between concepts can also be more complex than a chain. Current LBD solutions can hardly complete the LBD task in the new setting. We simplify the hypotheses as concept sets and propose LBDSetNet based on graph neural networks to solve this problem. We also introduce different training schemes based on self-supervised learning to train LBDSetNet without relying on comprehensive labeled hypotheses that are extremely costly to get. Our comprehensive experiments show that LBDSetNet outperforms strong baselines on simple hypotheses and addresses complex hypotheses.

Object detection is a fundamental task in computer vision, with significant applications in various domains. However, the reliance on large-scale annotated data and computational resource demands poses challenges in practical implementation. This thesis aims to address these complexities by exploring self-supervised training approaches for the detection transformer(DETR) family of object detectors. The project investigates the necessity of training the backbone under a semi-supervised setting and explores the benefits of initializing scene graph generation architectures with pretrained DETReg and DETR models for faster training convergence and reduced computational resource requirements. The significance of this research lies in the potential to mitigate the dependence on annotated data and make deep learning techniques more accessible to researchers and practitioners. By overcoming the limitations of data and computational resources, this thesis contributes to the accessibility of DETR and encourages a more sustainable and inclusive approach to deep learning research. / Objektigenkänning är en grundläggande uppgift inom datorseende, med betydande tillämpningar inom olika domäner. Dock skapar beroendet av storskaliga annoterade data och krav på datorkraft utmaningar i praktisk implementering. Denna avhandling syftar till att ta itu med dessa komplexiteter genom att utforska självövervakade utbildningsmetoder för detektions transformer (DETR) familjen av objektdetektorer. Projektet undersöker nödvändigheten av att träna ryggraden under en semi-övervakad inställning och utforskar fördelarna med att initiera scenegrafgenereringsarkitekturer med förtränade DETReg-modeller för snabbare konvergens av träning och minskade krav på datorkraft. Betydelsen av denna forskning ligger i potentialen att mildra beroendet av annoterade data och göra djupinlärningstekniker mer tillgängliga för forskare och utövare. Genom att övervinna begränsningarna av data och datorkraft, bidrar denna avhandling till tillgängligheten av DETR och uppmuntrar till en mer hållbar och inkluderande inställning till djupinlärning forskning.

In this Thesis, we leverage unsupervised raw data to develop more efficient pre-training objectives and self-supervised tasks that align well with downstream applications. In the first part, we present three alternative objectives to BERT’s Masked Language Modeling (MLM), namely Random Token Substitution (RTS), Cluster-based Random Token Substitution C-RTS, and Swapped Language Modeling (SLM). Unlike MLM, all of these proposals involve token swapping rather than replacing tokens with BERT’s [MASK]. RTS and C-RTS involve pre- dicting the originality of tokens, while SLM tasks the model at predicting the original token values. Each objective is applied to several models, which are trained using the same computational budget and corpora. Evaluation results reveal RTS and C-RTS require up to 45% less pre-training time while achieving performance on par with MLM. Notably, SLM outperforms MLM on several Answer Sentence Selection and GLUE tasks, despite utilizing the same computational budget for pre-training. In the second part of the Thesis, we propose self-supervised pre-training tasks that exhibit structural alignment with downstream applications, leading to improved performance and reduced reliance on labeled data to achieve comparable results. We exploit the weak supervision provided by large corpora like Wikipedia and CC-News, challenging the model to recognize whether spans of text originate from the same paragraph or document. To this end, we design (i) a pre-training objective that targets multi-sentence inference models by performing predictions over multiple spans of texts simultaneously, (ii) self-supervised objectives tailored to enhance performance in Answer Sentence Selection and its Contextual version, and (iii) a pre-training objective aimed at performance improvements in Summarization. Through continuous pre-training, starting from renowned checkpoints such as RoBERTa, ELEC- TRA, DeBERTa, BART, and T5, we demonstrate that our models achieve higher performance on Fact Verification, Answer Sentence Selection, and Summarization. We extensively evaluate our proposals on different benchmarks, revealing significant accuracy gains, particularly when annotation in the target dataset is limited. Notably, we achieve state-of-the-art results on the development set of the FEVER dataset and results close to state-of-the-art models using much more parameters on the test set. Furthermore, our objectives enable us to attain state-of-the-art results on ASNQ, WikiQA, and TREC-QA test sets, across all evaluation metrics (MAP, MRR, and P@1). For Summarization, our objective enhances summary quality, as measured by various metrics like ROUGE and BLEURT. We maintain that our proposals can be seamlessly combined with other techniques from recently proposed works, as they do not require alterations to the internal structure of Transformer models but only involve modifications to the training tasks.

[pt] O desenvolvimento de técnicas de aprendizado auto-supervisionado vem ganhando muita visibilidade na área de Visão Computacional pois possibilita o pré-treinamento de redes neurais profundas sem a necessidade de dados anotados. Em alguns domínios, as anotações são custosas, pois demandam muito trabalho especializado para a rotulação dos dados. Esse problema é muito comum no setor de Óleo e Gás, onde existe um vasto volume de dados não interpretados. O presente trabalho visa aplicar a técnica de aprendizado auto-supervisionado denominada Masked Autoencoders para pré-treinar modelos Vision Transformers com dados sísmicos. Para avaliar o pré-treino, foi aplicada a técnica de transfer learning para o problema de segmentação de fácies sísmicas. Na fase de pré-treinamento foram empregados quatro volumes sísmicos distintos. Já para a segmentação foi utilizado o dataset Facies-Mark e escolhido o modelo da literatura Segmentation Transformers. Para avaliação e comparação da performance da metodologia foram empregadas as métricas de segmentação utilizadas pelo trabalho de benchmarking de ALAUDAH (2019). As métricas obtidas no presente trabalho mostraram um resultado superior. Para a métrica frequency weighted intersection over union, por exemplo, obtivemos um ganho de 7.45 por cento em relação ao trabalho de referência. Os resultados indicam que a metodologia é promissora para melhorias de problemas de visão computacional em dados sísmicos. / [en] The development of self-supervised learning techniques has gained a lot of visibility in the field of Computer Vision as it allows the pre-training of deep neural networks without the need for annotated data. In some domains, annotations are costly, as they require a lot of specialized work to label the data. This problem is very common in the Oil and Gas sector, where there is a vast amount of uninterpreted data. The present work aims to apply the self-supervised learning technique called Masked Autoencoders to pre-train Vision Transformers models with seismic data. To evaluate the pre-training, transfer learning was applied to the seismic facies segmentation problem. In the pre-training phase, four different seismic volumes were used. For the segmentation, the Facies-Mark dataset was used and the Segmentation Transformers model was chosen from the literature. To evaluate and compare the performance of the methodology, the segmentation metrics used by the benchmarking work of ALAUDAH (2019) were used. The metrics obtained in the present work showed a superior result. For the frequency weighted intersection over union (FWIU) metric, for example, we obtained a gain of 7.45 percent in relation to the reference work. The results indicate that the methodology is promising for improving computer vision problems in seismic data.

Knowledge graphs (KGs) store relational information in a flexible triplet schema and have become ubiquitous for information storage in domains such as web search, e-commerce, social networks, and biology. Retrieval of information from KGs is generally achieved through logical reasoning, but this process can be computationally expensive and has limited performance due to the large size and complexity of relationships within the KGs. Furthermore, to extend the usage of KGs to non-expert users, retrieval over them cannot solely rely on logical reasoning but also needs to consider text-based search. This creates a need for multi-modal representations that capture both the semantic and structural features from the KGs. The primary objective of the proposed work is to extend the accessibility of KGs to non-expert users/institutions by enabling them to utilize non-technical textual queries to search over the vast amount of information stored in KGs. To achieve this objective, the research aims to solve four limitations: (i) develop a framework for logical reasoning over KGs that can learn representations to capture hierarchical dependencies between entities, (ii) design an architecture that can effectively learn the logic flow of queries from natural language text, (iii) create a multi-modal architecture that can capture inherent semantic and structural features from the entities and KGs, respectively, and (iv) introduce a novel hyperbolic learning framework to enable the scalability of hyperbolic neural networks over large graphs using meta-learning. The proposed work is distinct from current research because it models the logical flow of textual queries in hyperbolic space and uses it to perform complex reasoning over large KGs. The models developed in this work are evaluated on both the standard research setting of logical reasoning, as well as, real-world scenarios of query matching and search, specifically, in the e-commerce domain. In summary, the proposed work aims to extend the accessibility of KGs to non-expert users by enabling them to use non-technical textual queries to search vast amounts of information stored in KGs. To achieve this objective, the work proposes the use of multi-modal representations that capture both semantic and structural features from the KGs, and a novel hyperbolic learning framework to enable scalability of hyperbolic neural networks over large graphs. The work also models the logical flow of textual queries in hyperbolic space to perform complex reasoning over large KGs. The models developed in this work are evaluated on both the standard research setting of logical reasoning and real-world scenarios in the e-commerce domain. / Doctor of Philosophy / Knowledge graphs (KGs) are databases that store information in a way that allows computers to easily identify relationships between different pieces of data. They are widely used in domains such as web search, e-commerce, social networks, and biology. However, retrieving information from KGs can be computationally expensive, and relying solely on logical reasoning can limit their accessibility to non-expert users. This is where the proposed work comes in. The primary objective is to make KGs more accessible to non-experts by enabling them to use natural language queries to search the vast amounts of information stored in KGs. To achieve this objective, the research aims to address four limitations. Firstly, a framework for logical reasoning over KGs that can learn representations to capture hierarchical dependencies between entities is developed. Secondly, an architecture is designed that can effectively learn the logic flow of queries from natural language text. Thirdly, a multi-modal architecture is created that can capture inherent semantic and structural features from the entities and KGs, respectively. Finally, a novel hyperbolic learning framework is introduced to enable the scalability of hyperbolic neural networks over large graphs using meta-learning. The proposed work is unique because it models the logical flow of textual queries in hyperbolic space and uses it to perform complex reasoning over large KGs. The models developed in this work are evaluated on both the standard research setting of logical reasoning, as well as, real-world scenarios of query matching and search, specifically, in the e-commerce domain. In summary, the proposed work aims to make KGs more accessible to non-experts by enabling them to use natural language queries to search vast amounts of information stored in KGs. To achieve this objective, the work proposes the use of multi-modal representations that capture both semantic and structural features from the KGs, and a novel hyperbolic learning framework to enable scalability of hyperbolic neural networks over large graphs. The work also models the logical flow of textual queries in hyperbolic space to perform complex reasoning over large KGs. The results of this work have significant implications for the field of information retrieval, as it provides a more efficient and accessible way to retrieve information from KGs. Additionally, the multi-modal approach taken in this work has potential applications in other areas of machine learning, such as image recognition and natural language processing. The work also contributes to the development of hyperbolic geometry as a tool for modeling complex networks, which has implications for fields such as network science and social network analysis. Overall, this work represents an important step towards making the vast amounts of information stored in KGs more accessible and useful to a wider audience.

Made available in DSpace on 2016-06-02T19:05:51Z (GMT). No. of bitstreams: 1 3777.pdf: 3225691 bytes, checksum: 38e3ba8f3c842f4e05d42710339e897a (MD5) Previous issue date: 2011-02-17 / Machine Learning (ML) can be seen as research area within the Artificial Intelligence (AI) that aims to develop computer programs that can evolve with new experiences. The main ML purpose is the search for methods and techniques that enable the computer system improve its performance autonomously using information learned through its use. This feature can be considered the fundamental mechanisms of the processes of automatic learning. The main goal in this research project was to investigate, propose and implement methods and algorithms to allow the construction of a continuous learning system capable of extracting knowledge from the Web in Portuguese, throughout the creation of a knowledge base which can be constantly updated as new knowledge is extracted. / O Aprendizado de Máquina (AM) pode ser visto como uma área de pesquisa dentro da Inteligência Artificial (IA) que busca o desenvolvimento de programas de computador que possam evoluir à medida que vão sendo expostos a novas experiências. O principal objetivo de AM é a busca por métodos e técnicas que permitem a concepção de sistemas computacionais capazes de melhorar seu desempenho, de maneira autônoma, usando informações obtidas ao longo de seu uso; tal característica pode, de certa forma, ser considerada como um dos mecanismos fundamentais que regem os processos de aprendizado automático. O principal objetivo da pesquisa descrita neste documento foi investigar, propor e implementar métodos e algoritmos que permitissem a construção de um sistema computacional de aprendizado contínuo capaz de realizar a extração de conhecimento a partir da Web em português, por meio da criação de uma base de conhecimento atualizada constantemente à medida que novos conhecimentos vão sendo extraídos.

Hyperspectral imaging is an expanding topic within the field of computer vision, that uses images of high spectral granularity. Contrastive learning is a discrim- inative approach to self-supervised learning, a form of unsupervised learning where the network is trained using self-created pseudo-labels. This work com- bines these two research areas and investigates how a pretrained network based on contrastive learning can be used for hyperspectral images. The hyperspectral images used in this work are generated from simulated RGB images and spec- tra from a spectral library. The network is trained with a pretext task based on data augmentations, and is evaluated through transfer learning and fine-tuning for a downstream task. The goal is to determine the impact of the pretext task on the downstream task and to determine the required amount of labelled data. The results show that the downstream task (a classifier) based on the pretrained network barely performs better than a classifier without a pretrained network. In the end, more research needs to be done to confirm or reject the benefit of a pretrained network based on contrastive learning for hyperspectral images. Also, the pretrained network should be tested on real-world hyperspectral data and trained with a pretext task designed for hyperspectral images.

Deep learning works best with vast andd well-distributed data collections. However, collecting and annotating large data sets can be very time-consuming and expensive. Moreover, deep learning is specific to domain knowledge, even with data and computation. E.g., models trained to classify animals would probably underperform when they classify vehicles. Although techniques such as domain adaptation and transfer learning have been popularised recently, tasks in cross-domain knowledge transfer have also taken off. However, most of these works are limited to computer vision. In the domain of time series, this is relatively underexplored. This thesis explores methods to use time series data from one domain to classify data generated from another domain via transfer learning. It focuses on using accelerometer data from running recordings to improve the classification performance on jumping data based on the apparent similarity of individual recordings. Thus, transfer learning and domain adaptation techniques were used to use the learning acquired through deep model training on running sequences. This thesis has performed four experiments to test this domain similarity. The first one consists of transforming time series with the continuous wavelet transform to get both time and frequency information. The model is then pre-trained within a contrastive learning framework. However, the continuous wavelet transformation (CWT) did not improve the classification results. The following two experiments consisted of pre-training the models with self-supervised learning. The first one with a contrastive pretext-task improved the classification results, and the resilience to data decrease. The second one with a forward forecasting pretext-task improved the results when all the data was available but was very sensitive to data decrease. Finally, the domain adaptation was tested and showed interesting performances on the classification task. Although some of the employed techniques did not show improvement, pre-training using contrastive learning on the running dataset has shown great improvement to classify the jumping dataset. / Djupinlärning fungerar bäst med stora och väl distribuerade datasamlingar. Det kan dock vara mycket tidskrävande och dyrt att samla in och kommentera stora datamängder. Även med alla data och beräkningar är djupinlärning specifik för domänkunskap. Exempelvis skulle modeller som tränats för att klassificera djur förmodligen underprestera när de klassificerar fordon. Även om tekniker som domänanpassning och överföringsinlärning har populariserats på senare tid, har även uppgifter inom kunskapsöverföring mellan olika domäner tagit fart. De flesta av dessa arbeten är dock begränsade till datorseende. Inom tidsseriernas område är detta relativt outforskat. I den här avhandlingen undersöks metoder för att använda tidsseriedata från en domän för att klassificera data från en annan domän med hjälp av djupinlärning. Fokus ligger på att använda accelerometerdata från löpning för att förbättra klassificeringen av hoppdata, baserat på den uppenbara likheten mellan löpning och hoppning. Således användes tekniker för överföringsinlärning och domänanpassning för att använda den inlärning som förvärvats genom träning av djupa modeller på löpsekvenser. I den här avhandlingen har fyra experiment utförts för att testa denna domänlikhet. Det första består av att omvandla tidsserier med den kontinuerliga wavelettransformen för att få fram både tids- och frekvensinformation. Modellen förtränas sedan inom en ram för kontrastiv inlärning. Användningen av CWT förbättrade dock inte klassificeringsresultaten. De följande två experimenten bestod av att förträna modellerna med självövervakad inlärning. Det första försöket med en kontrasterande förtextuppgift förbättrade klassificeringsresultaten och motståndskraften mot dataförlust. Det andra försöket med en prognostiserande förtextuppgift förbättrade resultaten när alla data var tillgängliga, men var mycket känslig för dataförlust. Slutligen testades domänanpassningen och visade intressanta resultat i klassificeringsuppgiften. Även om några av de använda teknikerna inte visade någon förbättring, har förträning med hjälp av kontrastinlärning på löpande dataset visat sig ge stora förbättringar när det gäller klassificering av hoppdata.

<p>Deep neural networks have made significant advances in computer vision. However, several challenges limit their real-world applications. For example, domain shifts in vision data degrade model performance; visual appearance variances affect model robustness; it is also non-trivial to extend a model trained on one task to novel tasks; and in many applications, large-scale labeled data are not even available for learning powerful deep models from scratch. This research focuses on improving the transferability of deep features and the efficiency of deep vision model adaptation, leading to enhanced generalization and new capabilities on computer vision tasks. Specifically, we approach these problems from the following two directions: architectural adaptation and label-efficient transferable feature learning. From an architectural perspective, we investigate various schemes that permit network adaptation to be parametrized by multiple copies of sub-structures, distributions of parameter subspaces, or functions that infer parameters from data. We also explore how model adaptation can bring new capabilities, such as continuous and stochastic image modeling, fast transfer to new tasks, and dynamic computation allocation based on sample complexity. From the perspective of feature learning, we show how transferable features emerge from generative modeling with massive unlabeled or weakly labeled data. Such features enable both image generation under complex conditions and downstream applications like image recognition and segmentation. By combining both perspectives, we achieve improved performance on computer vision tasks with limited labeled data, enhanced transferability of deep features, and novel capabilities beyond standard deep learning models.</p>

<p>Graph representation learning (GRL) has been increasing used to model and understand data from a wide variety of complex systems spanning social, technological, bio-chemical and physical domains. GRL consists of two main components (1) a parametrized encoder that provides representations of graph data and (2) a learning process to train the encoder parameters. Designing flexible encoders that capture the underlying invariances and characteristics of graph data are crucial to the success of GRL. On the other hand, the learning process drives the quality of the encoder representations and developing principled learning mechanisms are vital for a number of growing applications in self-supervised, transfer and federated learning settings. To this end, we propose a suite of models and learning algorithms for GRL which form the two main thrusts of this dissertation.</p> <p><br></p> <p>In Thrust I, we propose two novel encoders which build upon on a widely popular GRL encoder class called graph neural networks (GNNs). First, we empirically study the prediction performance of current GNN based encoders when applied to graphs with heterogeneous node mixing patterns using our proposed notion of local assortativity. We find that GNN performance in node prediction tasks strongly correlates with our local assortativity metric---thereby introducing a limit. We propose to transform the input graph into a computation graph with proximity and structural information as distinct types of edges. We then propose a novel GNN based encoder that operates on this computation graph and adaptively chooses between structure and proximity information. Empirically, adopting our transformation and encoder framework leads to improved node classification performance compared to baselines in real-world graphs that exhibit diverse mixing.</p> <p>Secondly, we study the trade-off between expressivity and efficiency of GNNs when applied to temporal graphs for the task of link ranking. We develop an encoder that incorporates a labeling approach designed to allow for efficient inference over the candidate set jointly, while provably boosting expressivity. We also propose to optimize a list-wise loss for improved ranking. With extensive evaluation on real-world temporal graphs, we demonstrate its improved performance and efficiency compared to baselines.</p> <p><br></p> <p>In Thrust II, we propose two principled encoder learning mechanisms for challenging and realistic graph data settings. First, we consider a scenario where only limited or even no labelled data is available for GRL. Recent research has converged on graph contrastive learning (GCL), where GNNs are trained to maximize the correspondence between representations of the same graph in its different augmented forms. However, we find that GNNs trained by traditional GCL often risk capturing redundant graph features and thus may be brittle and provide sub-par performance in downstream tasks. We then propose a novel principle, termed adversarial-GCL (AD-GCL), which enables GNNs to avoid capturing redundant information during the training by optimizing adversarial graph augmentation strategies used in GCL. We pair AD-GCL with theoretical explanations and design a practical instantiation based on trainable edge-dropping graph augmentation. We experimentally validate AD-GCL by comparing with state-of-the-art GCL methods and achieve performance gains in semi-supervised, unsupervised and transfer learning settings using benchmark chemical and biological molecule datasets. </p> <p>Secondly, we consider a scenario where graph data is silo-ed across clients for GRL. We focus on two unique challenges encountered when applying distributed training to GRL: (i) client task heterogeneity and (ii) label scarcity. We propose a novel learning framework called federated self-supervised graph learning (FedSGL), which first utilizes a self-supervised objective to train GNNs in a federated fashion across clients and then, each client fine-tunes the obtained GNNs based on its local task and available labels. Our framework enables the federated GNN model to extract patterns from the common feature (attribute and graph topology) space without the need of labels or being biased by heterogeneous local tasks. Extensive empirical study of FedSGL on both node and graph classification tasks yields fruitful insights into how the level of feature / task heterogeneity, the adopted federated algorithm and the level of label scarcity affects the clients’ performance in their tasks.</p>