Computing and Information Systems - Theses
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ItemTowards Robust Medical Machine Learning( 2022)Machine learning systems have been developed to address a number of problems in numerous domains, among which medical solutions have been facilitated by machine learning approaches for decades. These approaches play important roles in automated disease diagnosis, medical image processing, and auxiliary surgical operation, etc. Despite the highly efficient diagnosis benefited from machine learning approaches, these methods may not be robust to common challenges in practical scenarios, such as special while crucial characteristics of medical data, annotation variations from multiple experts, noisy annotations, and multi-source datasets. Such problems impede machine learning methods from being applied accurately and safely to medical tasks. In the thesis, we introduce special while important medical problems that were not brought into the spotlight before. We then provide corresponding robust machine learning solutions for each problem when existing machine learning methods degrade significantly in these tasks. Specifically, the first problem is the similarity analysis for time series with large discontinuities, which is common in surgical time series. We thus propose a robust distance measurement for time series with large discontinuities when they disable the accurate measurement of local characteristics using existing algorithms. Second, surgical policies provided by different surgeons for the same patient/surgery may not be exactly the same. We then propose the reward-penalty Dice loss (RPDL) to learn non-unique surgical segmentation regions for deep vision networks. RPDL is robust to varying annotations for the same input, which enables the comprehensive learning of models from multiple experts. Third, medical datasets might be composed of limited examples and noisy annotations, making it challenging to train deep learning models. To address this challenge, we propose alpha-IoU, a family of power Intersection over Union (IoU) losses for bounding box (bbox) regression. We show that alpha-IoU losses are more robust to small datasets and noisy bboxes in lesion detection. Fourth, large-scale medical datasets are often collected from different institutions cooperated by a number of experts. In this case, we build a one-stage framework SpineOne for detecting degenerative discs and vertebrae from spinal MRIs, which implements both the keypoint localization and classification tasks simultaneously. SpineOne is a robust detector to multi-source MRI slices with various scales, numbers and quality. All four proposed machine learning approaches outperform existing baselines by a noticeable margin in specific medical tasks. In summary, four medical issues are thoroughly investigated in the thesis, i.e., the distance measurement for surgical time series with large discontinuities, the surgical region segmentation with a variety of clinician annotations, the lesion detection with limited examples and noisy bboxes, and the anatomical keypoint detection with multi-source medical data. Towards more robust medical machine learning, we then propose one robust machine learning approach for each corresponding problem.
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ItemA multi-faceted approach to document quality assessment( 2020)Document quality assessment, due to its complexity and subjectivity, requires considering information from multiple sources and aspects, to capture quality indicators. Grammaticality, readability, stylistics, structure, correctness, and expertise depth reflect the quality of documents from different aspects, with varying importance across different domains. Automatic quality assessment has obvious benefits in terms of time saving and tractability in contexts where the volume of documents is large. In the case of dynamic documents (possibly with multiple authors), such as in the case of Wikipedia, it is particularly pertinent, as any edit potentially has implications for the quality label of that document. In this thesis, we focusing on improving the performance of document quality assessment systems and measure the uncertainty of document quality assessment systems. This thesis addresses four research questions: (1) How can we capture visual features not present in the document text, such as images and visual layout, to enhance representations learned from text content? (2) How can we make use of hand-crafted features widely adopted in traditional machine learning approaches in the context of neural networks, to generate a more accurate document quality assessment system? (3) How can we model the inherent subjectivity of quality assessment in evaluating the performance of quality assessment systems? and (4) Can a quality assessment system detect whether there are intruder sentences in documents and identify the span of any such intruder sentences, given that they interrupt the coherence of documents, thereby lowering their quality? To address the first research question, we propose to use Inception V3 (Szegedy et al., 2016), a widely used visual model in computer vision, to capture visual features from visual renderings of documents, based on the observation that visual renderings of documents can capture these visual features. Inception V3 compares favourably to textual-based models over the Wikipedia and academic paper reviewing datasets. We further propose a joint model to predict document quality by combining visual and textual features. We observe further improvements over both Wikipedia and academic paper reviewing datasets, indicating complementary between visual and textual features, and the general applicability of our proposed method. Next, we propose two methods to enhance the capacity of neural models in predicting the quality of documents by utilising hand-crafted features. In the first method, we propose to concatenate hand-crafted features with neural learned high-level representations, assuming that neural model-learned features may not have captured all the information carried by these hand-crafted features. The second method, on the other hand, utilises hand-crafted features to guide neural model learning by explicitly attending to feature indicators when learning the relationship between the input and target variables, rather than simply concatenating hand-crafted features. Experimental results demonstrate the superiority of our proposed methods over baselines. To imitate people’s disagreement over the inherently subjective task of document quality assessment, we propose to measure the uncertainty in document quality predictions. We investigate two methods: Gaussian processes (GPs) (Rasmussen and Williams, 2006) and random forests (RFs) (Breiman, 2001), which provide not only a prediction of the document quality but also the uncertainty over their predictions. We also propose an asymmetric cost, considering the prediction uncertainty, which is used to measure the performance of two methods in the scenario, where decision-making processes based on model predictions can lead to different costs. Lastly, we propose a new task of detecting whether there is an intruder sentence in a document, generated by replacing an original sentence with a similar sentence from a second document. Existing datasets in coherence detection are not suitable for our task as they are either too small for training current data-hungry models on or do not specify the span of incoherent text. To benchmark model performance over this task, we construct a large-scale dataset consisting of documents from English Wikipedia and CNN news articles. Experimental results show that pre-trained language models which incorporate larger document contexts in pretraining perform remarkably well in-domain, but experience a substantial drop cross-domain. In follow-up analysis based on human annotations, substantial divergences from human intuitions were observed, pointing to limitations in their ability to model document coherence. Further results over a linguistic probe dataset show that pre-trained models fail to identify some linguistic characteristics that affect document coherence, suggesting room to improve for them to truly capture document coherence, and motivating the construction of a dataset with intruder text at the intra-sentential level.
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ItemTowards Robust Representation of Natural Language Processing( 2019)There are many challenges in building robust natural language applications. Machine learning based methods require large volumes of annotated text data, and variations over text can lead to problems, namely: (1) language can be highly variable and expressed with different variations, such as lexical and syntactic. Robust models should be able to handle these variations. (2) A text corpus is heterogeneous, often making language systems domain-brittle. Solutions for domain adaptation and training with corpora comprised of multiple domains are required for language applications in the real world. (3) Many language applications tend to be biased to the demographic of the authors of documents the system is trained on, and lack model fairness. Demographic bias also causes privacy issues when a model is made available to others. In this thesis, I aim to build robust natural language models to tackle these problems, focusing on deep learning approaches which have shown great success in language processing via representation learning. I pose three basic research questions: how to learn representations that are robust to language variation, robust to domain variation, and robust to demographic variables. Each of these research questions is tackled using different approaches, including data augmentation, adversarial learning, and variational inference. For learning robust representations to language variation, I study lexical variation and syntactic variation. To be specific, a regularisation method is proposed to tackle lexical variation, and a data augmentation method is proposed to build robust models, using a range of language generation methods from both linguistic and machine learning perspectives. For domain robustness, I focus on multi-domain learning and investigate domain supervised and unsupervised learning, where domain labels may or may not be available. Two types of models are proposed, via adversarial learning and latent domain gating, to build robust models for heterogeneous text. For robustness to demographics, I show that demographic bias in the training corpus leads to model fairness problems with respect to the demographic of the authors, as well as privacy issues under inference attacks. Adversarial learning is adopted to mitigate bias in representation learning, to improve model fairness and privacy-preservation. To demonstrate the proposed approaches, a range of tasks are considered, including text classification and POS tagging. To evaluate the generalisation and robustness, both in-domain and out-of-domain experiments are conducted with two classes of language tasks: text classification and part-of-speech tagging. For multi-domain learning, multi-domain language identification and multi-domain sentiment classification are conducted, and I simulate domain supervised learning and domain unsupervised learning to evaluate domain robustness. I evaluate model fairness with different demographic attributes and apply inference attacks to test model privacy. The experiments show the advantages and the robustness of the proposed methods. Finally, I discuss the relations between the different forms of robustness, including their commonalities and differences. The limitations of this thesis are discussed in detail, including potential methods to address these shortcomings in future work, and potential opportunities to generalise the proposed methods to other language tasks. Above all, these methods of learning robust representations can contribute towards progress in natural language processing.
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ItemCompositional morphology through deep learning( 2018)Most human languages have sophisticated morphological systems. In order to build successful models of language processing, we need to focus on morphology, the internal structure of words. In this thesis, we study two morphological processes: inflection (word change rules, e.g. run -- runs) and derivation (word formation rules, e.g. run -- runner). We first evaluate the ability of contemporary models that are trained using the distributional hypothesis, which states that a word's meaning can be expressed by the context in which it appears, to capture these types of morphology. Our study reveals that inflections are predicted at high accuracy whereas derivations are more challenging due to irregularity of meaning change. We then demonstrate that supplying the model with character-level information improves predictions and makes usage of language resources more efficient, especially in morphologically rich languages. We then address the question of to what extent and which information about word properties (such as gender, case, number) can be predicted entirely from a word's sentential content. To this end, we introduce a novel task of contextual inflection prediction. Our experiments on prediction of morphological features and a corresponding word form from sentential context show that the task is challenging, and as morphological complexity increases, performance significantly drops. We found that some morphological categories (e.g., verbal tense) are inherent and typically cannot be predicted from context while others (e.g., adjective number and gender) are contextual and inferred from agreement. Compared to morphological inflection tasks, where morphological features are explicitly provided, and the system has to predict only the form, accuracy on this task is much lower. Finally, we turn to word formation, derivation. Experiments with derivations show that they are less regular and systematic. We study how much a sentential context is indicative of a meaning change type. Our results suggest that even though inflections are more productive and regular than derivations, the latter also present cases of high regularity of meaning and form change, but often require extra information such as etymology, word frequency, and more fine-grained annotation in order to be predicted at high accuracy.
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ItemAutoregressive generative models and multi-task learning with convolutional neural networks( 2018)At a high level, sequence modelling problems are of the form where the model aims to predict the next element of a sequence based on neighbouring items. Common types of applications include time-series forecasting, language modelling, machine translation and more recently, adversarial learning. One main characteristic of such models is that they assume that there is an underlying learnable structure behind the data generation process, such as it is for language. Therefore, the models used have to go beyond traditional linear or discrete hidden state models. Convolutional Neural Networks (CNNs) are the de facto state of the art in computer vision. Conversely, for sequence modelling and multi-task learning (MTL) problems, the most common choice are Recurrent Neural Networks (RNNs). In this thesis I show that causal CNNs can be successfully and efficiently used for a broad range of sequence modelling and multi-task learning problems. This is supported by applying CNNs to two very different domains, which highlight their flexibility and performance: 1) traffic forecasting in the context of highly dynamic road conditions with non-stationary data and normal granularity (sampling rate) and a high spatial volume of related tasks; 2) learning musical instrument synthesisers with stationary data and a very high granularity (high sampling rate raw waveforms) and thus a high temporal volume, and conditional side information. In the first case, the challenge is to leverage the complex interactions between tasks while keeping the streaming (online) forecasting process tractable and robust to faults and changes (adding or removing tasks). In the second case, the problem is highly related to language modelling, although much more difficult since, unlike words, multiple musical notes can be played at the same time, therefore making the task much more challenging. With the ascent of the Internet of Things (IoT) and Big Data becoming more common, new challenges arise. The four V‘s of Big Data (Volume, Velocity, Variety and Veracity) are studied in the context of multi-task learning for spatio-temporal (ST) prediction problems. These aspects are studied in the first part of this thesis. Traditionally such problems are addressed with static, non-modular linear models that do not leverage Big Data. I discuss what the four V‘s imply for multi-task ST problems and finally show how CNNs can be set up as efficient classifiers for such problems, if the quantization is properly set up for non-stationary data. While the first part is predominantly data-centric, focused on aspects such as Volume (is it useful?) and Veracity (how to deal with missing data?) the second part of the thesis addresses the Velocity and Variety challenges. I also show that even for prediction problems set up as regression, causal CNNs are still the best performing model as compared to state of the art algorithms such as SVRS and more traditional methods such as ARIMA. I introduce TRU-VAR (Topologically Regularized Universal Vector AutoRegression) which, as I show, is a robust, versatile real-time multi-task forecasting framework which leverages domain-specific knowledge (task topology), the Variety (task diversity) and Velocity (online training). Finally, the last part of this thesis is focused on generative CNN models. The main contribution is the SynthNet architecture which is the first capable of learning musical instrument synthesisers end-to-end. The architecture is derived by following a parsimonious approach (reducing complexity) and via an in-depth analysis of the learned representations of the baseline architectures. I show that the 2D projection of each layer gram activations can correspond to resonating frequencies (which gives each musical instrument it‘s timbre). SynthNet trains much faster and it’s generation accuracy is much higher than the baselines. The generated waveforms are almost identical to the ground truth. This has implications in other domains where the the goal is to generate data with similar properties as the data generation process (i.e. adversarial examples). In summary, this thesis makes contributions towards multi-task spatio-temporal time series problems with causal CNNs (set up as both classification and regression) and generative CNN models. The achievements of this thesis are supported by publications which contain an extensive set of experiments and theoretical foundations.