Neural architecture search for 1D CNNs - Different approaches tests and measurements

In the field of sensors, in areas such as industrial, clinical, or environment, it is common to find one dimensional (1D) formatted data (e.g., electrocardiogram, temperature, power consumption). A very promising technique for modelling this information is the use of One Dimensional Convolutional Neural Networks (1D CNN), which introduces a new challenge, namely how to define the best architecture for a 1D CNN. This manuscript addresses the concept of One Dimensional Neural Architecture Search (1D NAS), an approach that automates the search for the best combination of Neuronal Networks hyperparameters (model architecture), including both structural and training hyperparameters, for optimising 1D CNNs. This work includes the implementation of search processes for 1D CNN architectures based on five strategies: greedy, random, Bayesian, hyperband, and genetic approaches to perform, collect, and analyse the results obtained by each strategy scenario. For the analysis, we conducted 125 experiments, followed by a thorough evaluation from multiple perspectives, including the best-performing model in terms of accuracy, consistency, variability, total running time, and computational resource consumption. Finally, by presenting the optimised 1D CNN architecture, the results for the manuscript’s research question (a real-life clinical case) were provided.info:eu-repo/semantics/publishedVersio

End-to-end deep reinforcement learning in computer systems

Abstract The growing complexity of data processing systems has long led systems designers to imagine systems (e.g. databases, schedulers) which can self-configure and adapt based on environmental cues. In this context, reinforcement learning (RL) methods have since their inception appealed to systems developers. They promise to acquire complex decision policies from raw feedback signals. Despite their conceptual popularity, RL methods are scarcely found in real-world data processing systems. Recently, RL has seen explosive growth in interest due to high profile successes when utilising large neural networks (deep reinforcement learning). Newly emerging machine learning frameworks and powerful hardware accelerators have given rise to a plethora of new potential applications. In this dissertation, I first argue that in order to design and execute deep RL algorithms efficiently, novel software abstractions are required which can accommodate the distinct computational patterns of communication-intensive and fast-evolving algorithms. I propose an architecture which decouples logical algorithm construction from local and distributed execution semantics. I further present RLgraph, my proof-of-concept implementation of this architecture. In RLgraph, algorithm developers can explore novel designs by constructing a high-level data flow graph through combination of logical components. This dataflow graph is independent of specific backend frameworks or notions of execution, and is only later mapped to execution semantics via a staged build process. RLgraph enables high-performing algorithm implementations while maintaining flexibility for rapid prototyping. Second, I investigate reasons for the scarcity of RL applications in systems themselves. I argue that progress in applied RL is hindered by a lack of tools for task model design which bridge the gap between systems and algorithms, and also by missing shared standards for evaluation of model capabilities. I introduce Wield, a first-of-its-kind tool for incremental model design in applied RL. Wield provides a small set of primitives which decouple systems interfaces and deployment-specific configuration from representation. Core to Wield is a novel instructive experiment protocol called progressive randomisation which helps practitioners to incrementally evaluate different dimensions of non-determinism. I demonstrate how Wield and progressive randomisation can be used to reproduce and assess prior work, and to guide implementation of novel RL applications

On supporting K-anonymisation and L-diversity of crime databases with genetic algorithms in a resource constrained environment

The social benefits derived from analysing crime data need to be weighed against issues relating to privacy loss. To facilitate such analysis of crime data Burke and Kayem [7] proposed a framework (MCRF) to enable mobile crime reporting in a developing country. Here crimes are reported via mobile phones and stored in a database owned by a law enforcement agency. The expertise required to perform analysis on the crime data is however unlikely to be available within the law enforcement agency. Burke and Kayem [7] proposed anonymising the data(using manual input parameters) at the law enforcement agency before sending it to a third party for analysis. Whilst analysis of the crime data requires expertise, adequate skill to appropriately anonymise the data is also required. What is lacking in the original MCRF is therefore an automated scheme for the law enforcement agency to adequately anonymise the data before sending it to the third party. This should, however, be done whilst maximising information utility of the anonymised data from the perspective of the third party. In this thesis we introduce a crime severity scale to facilitate the automation of data anonymisation within the MCRF. We consider a modified loss metric to capture information loss incurred during the anonymisation process. This modified loss metric also gives third party users the flexibility to specify attributes of the anonymised data when requesting data from the law enforcement agency. We employ a genetic algorithm(GA) approach called "Crime Genes"(CG) to optimise utility of the anonymised data based on our modified loss metric whilst adhering to notions of privacy denned by k-anonymity and l-diversity. Our CG implementation is modular and can therefore be easily integrated with the original MCRF. We also show how our CG approach is designed to be suitable for implementation in a developing country where particular resource constraints exist

Types of cost in inductive concept learning

Inductive concept learning is the task of learning to assign cases to a discrete set of classes. In real-world applications of concept learning, there are many different types of cost involved. The majority of the machine learning literature ignores all types of cost (unless accuracy is interpreted as a type of cost measure). A few papers have investigated the cost of misclassification errors. Very few papers have examined the many other types of cost. In this paper, we attempt to create a taxonomy of the different types of cost that are involved in inductive concept learning. This taxonomy may help to organize the literature on cost-sensitive learning. We hope that it will inspire researchers to investigate all types of cost in inductive concept learning in more depth

Structure Learning in Coupled Dynamical Systems and Dynamic Causal Modelling

Identifying a coupled dynamical system out of many plausible candidates, each of which could serve as the underlying generator of some observed measurements, is a profoundly ill posed problem that commonly arises when modelling real world phenomena. In this review, we detail a set of statistical procedures for inferring the structure of nonlinear coupled dynamical systems (structure learning), which has proved useful in neuroscience research. A key focus here is the comparison of competing models of (ie, hypotheses about) network architectures and implicit coupling functions in terms of their Bayesian model evidence. These methods are collectively referred to as dynamical casual modelling (DCM). We focus on a relatively new approach that is proving remarkably useful; namely, Bayesian model reduction (BMR), which enables rapid evaluation and comparison of models that differ in their network architecture. We illustrate the usefulness of these techniques through modelling neurovascular coupling (cellular pathways linking neuronal and vascular systems), whose function is an active focus of research in neurobiology and the imaging of coupled neuronal systems

Predictive Modelling Approach to Data-Driven Computational Preventive Medicine

This thesis contributes novel predictive modelling approaches to data-driven computational preventive medicine and offers an alternative framework to statistical analysis in preventive medicine research. In the early parts of this research, this thesis presents research by proposing a synergy of machine learning methods for detecting patterns and developing inexpensive predictive models from healthcare data to classify the potential occurrence of adverse health events. In particular, the data-driven methodology is founded upon a heuristic-systematic assessment of several machine-learning methods, data preprocessing techniques, models’ training estimation and optimisation, and performance evaluation, yielding a novel computational data-driven framework, Octopus. Midway through this research, this thesis advances research in preventive medicine and data mining by proposing several new extensions in data preparation and preprocessing. It offers new recommendations for data quality assessment checks, a novel multimethod imputation (MMI) process for missing data mitigation, a novel imbalanced resampling approach, and minority pattern reconstruction (MPR) led by information theory. This thesis also extends the area of model performance evaluation with a novel classification performance ranking metric called XDistance. In particular, the experimental results show that building predictive models with the methods guided by our new framework (Octopus) yields domain experts' approval of the new reliable models’ performance. Also, performing the data quality checks and applying the MMI process led healthcare practitioners to outweigh predictive reliability over interpretability. The application of MPR and its hybrid resampling strategies led to better performances in line with experts' success criteria than the traditional imbalanced data resampling techniques. Finally, the use of the XDistance performance ranking metric was found to be more effective in ranking several classifiers' performances while offering an indication of class bias, unlike existing performance metrics The overall contributions of this thesis can be summarised as follow. First, several data mining techniques were thoroughly assessed to formulate the new Octopus framework to produce new reliable classifiers. In addition, we offer a further understanding of the impact of newly engineered features, the physical activity index (PAI) and biological effective dose (BED). Second, the newly developed methods within the new framework. Finally, the newly accepted developed predictive models help detect adverse health events, namely, visceral fat-associated diseases and advanced breast cancer radiotherapy toxicity side effects. These contributions could be used to guide future theories, experiments and healthcare interventions in preventive medicine and data mining

On Differentiable Interpreters

Neural networks have transformed the fields of Machine Learning and Artificial Intelligence with the ability to model complex features and behaviours from raw data. They quickly became instrumental models, achieving numerous state-of-the-art performances across many tasks and domains. Yet the successes of these models often rely on large amounts of data. When data is scarce, resourceful ways of using background knowledge often help. However, though different types of background knowledge can be used to bias the model, it is not clear how one can use algorithmic knowledge to that extent. In this thesis, we present differentiable interpreters as an effective framework for utilising algorithmic background knowledge as architectural inductive biases of neural networks. By continuously approximating discrete elements of traditional program interpreters, we create differentiable interpreters that, due to the continuous nature of their execution, are amenable to optimisation with gradient descent methods. This enables us to write code mixed with parametric functions, where the code strongly biases the behaviour of the model while enabling the training of parameters and/or input representations from data. We investigate two such differentiable interpreters and their use cases in this thesis. First, we present a detailed construction of ∂4, a differentiable interpreter for the programming language FORTH. We demonstrate the ability of ∂4 to strongly bias neural models with incomplete programs of variable complexity while learning missing pieces of the program with parametrised neural networks. Such models can learn to solve tasks and strongly generalise to out-of-distribution data from small datasets. Second, we present greedy Neural Theorem Provers (gNTPs), a significant improvement of a differentiable Datalog interpreter NTP. gNTPs ameliorate the large computational cost of recursive differentiable interpretation, achieving drastic time and memory speedups while introducing soft reasoning over logic knowledge and natural language