Semi-Supervised Learning for Diagnosing Faults in Electromechanical Systems

Safe and reliable operation of the systems relies on the use of online condition monitoring and diagnostic systems that aim to take immediate actions upon the occurrence of a fault. Machine learning techniques are widely used for designing data-driven diagnostic models. The training procedure of a data-driven model usually requires a large amount of labeled data, which may not be always practical. This problem can be untangled by resorting to semi-supervised learning approaches, which enables the decision making procedure using only a few numbers of labeled samples coupled with a large number of unlabeled samples. Thus, it is crucial to conduct a critical study on the use of semi-supervised learning for the purpose of fault diagnosis. Another issue of concern is fault diagnosis in non-stationary environments, where data streams evolve over time, and as a result, model-based and most of the data-driven models are impractical. In this work, this has been addressed by means of an adaptive data-driven diagnostic model

Event-guided Multi-patch Network with Self-supervision for Non-uniform Motion Deblurring

Contemporary deep learning multi-scale deblurring models suffer from many issues: 1) They perform poorly on non-uniformly blurred images/videos; 2) Simply increasing the model depth with finer-scale levels cannot improve deblurring; 3) Individual RGB frames contain a limited motion information for deblurring; 4) Previous models have a limited robustness to spatial transformations and noise. Below, we extend the DMPHN model by several mechanisms to address the above issues: I) We present a novel self-supervised event-guided deep hierarchical Multi-patch Network (MPN) to deal with blurry images and videos via fine-to-coarse hierarchical localized representations; II) We propose a novel stacked pipeline, StackMPN, to improve the deblurring performance under the increased network depth; III) We propose an event-guided architecture to exploit motion cues contained in videos to tackle complex blur in videos; IV) We propose a novel self-supervised step to expose the model to random transformations (rotations, scale changes), and make it robust to Gaussian noises. Our MPN achieves the state of the art on the GoPro and VideoDeblur datasets with a 40x faster runtime compared to current multi-scale methods. With 30ms to process an image at 1280x720 resolution, it is the first real-time deep motion deblurring model for 720p images at 30fps. For StackMPN, we obtain significant improvements over 1.2dB on the GoPro dataset by increasing the network depth. Utilizing the event information and self-supervision further boost results to 33.83dB.Comment: International Journal of Computer Vision. arXiv admin note: substantial text overlap with arXiv:1904.0346

An Industry Driven Genre Classification Application using Natural Language Processing

With the advent of digitized music, many online streaming companies such as Spotify have capitalized on a listener’s need for a common stream platform. An essential component of such a platform is the recommender systems that suggest to the constituent user base, related tracks, albums and artists. In order to sustain such a recommender system, labeling data to indicate which genre it belongs to is essential. Most recent academic publications that deal with music genre classification focus on the use of deep neural networks developed and applied within the music genre classification domain. This thesis attempts to use some of the highly sophisticated techniques, such as Hierarchical Attention Networks that exist within the text classification domain in order to classify tracks of different genres. In order to do this, the music is first separated into different tracks (drums, vocals, bass and accompaniment) and converted into symbolic text data. Due to the sophistication of the distributed machine learning system (over five computers, each possessing a graphical processing units greater than a GTX 1070) present in this thesis, it is capable of classifying contemporary genres with an impressive peak accuracy of over 93%, when comparing the results with that of competing classifiers. It is also argued that through the use text classification, the ex- pert domain knowledge which musicians and people involved with musicological techniques, can be attracted to improving reccomender systems within the music information retrieval research domain

Neuromorphic Engineering Editors' Pick 2021

This collection showcases well-received spontaneous articles from the past couple of years, which have been specially handpicked by our Chief Editors, Profs. André van Schaik and Bernabé Linares-Barranco. The work presented here highlights the broad diversity of research performed across the section and aims to put a spotlight on the main areas of interest. All research presented here displays strong advances in theory, experiment, and methodology with applications to compelling problems. This collection aims to further support Frontiers’ strong community by recognizing highly deserving authors

Adaptive Automated Machine Learning

The ever-growing demand for machine learning has led to the development of automated machine learning (AutoML) systems that can be used off the shelf by non-experts. Further, the demand for ML applications with high predictive performance exceeds the number of machine learning experts and makes the development of AutoML systems necessary. Automated Machine Learning tackles the problem of finding machine learning models with high predictive performance. Existing approaches incorporating deep learning techniques assume that all data is available at the beginning of the training process (offline learning). They configure and optimise a pipeline of preprocessing, feature engineering, and model selection by choosing suitable hyperparameters in each model pipeline step. Furthermore, they assume that the user is fully aware of the choice and, thus, the consequences of the underlying metric (such as precision, recall, or F1-measure). By variation of this metric, the search for suitable configurations and thus the adaptation of algorithms can be tailored to the user’s needs. With the creation of a vast amount of data from all kinds of sources every day, our capability to process and understand these data sets in a single batch is no longer viable. By training machine learning models incrementally (i.ex. online learning), the flood of data can be processed sequentially within data streams. However, if one assumes an online learning scenario, where an AutoML instance executes on evolving data streams, the question of the best model and its configuration remains open. In this work, we address the adaptation of AutoML in an offline learning scenario toward a certain utility an end-user might pursue as well as the adaptation of AutoML towards evolving data streams in an online learning scenario with three main contributions: 1. We propose a System that allows the adaptation of AutoML and the search for neural architectures towards a particular utility an end-user might pursue. 2. We introduce an online deep learning framework that fosters the research of deep learning models under the online learning assumption and enables the automated search for neural architectures. 3. We introduce an online AutoML framework that allows the incremental adaptation of ML models. We evaluate the contributions individually, in accordance with predefined requirements and to state-of-the- art evaluation setups. The outcomes lead us to conclude that (i) AutoML, as well as systems for neural architecture search, can be steered towards individual utilities by learning a designated ranking model from pairwise preferences and using the latter as the target function for the offline learning scenario; (ii) architectual small neural networks are in general suitable assuming an online learning scenario; (iii) the configuration of machine learning pipelines can be automatically be adapted to ever-evolving data streams and lead to better performances

Efficient Deep Learning for Real-time Classification of Astronomical Transients

A new golden age in astronomy is upon us, dominated by data. Large astronomical surveys are broadcasting unprecedented rates of information, demanding machine learning as a critical component in modern scientific pipelines to handle the deluge of data. The upcoming Legacy Survey of Space and Time (LSST) of the Vera C. Rubin Observatory will raise the big-data bar for time- domain astronomy, with an expected 10 million alerts per-night, and generating many petabytes of data over the lifetime of the survey. Fast and efficient classification algorithms that can operate in real-time, yet robustly and accurately, are needed for time-critical events where additional resources can be sought for follow-up analyses. In order to handle such data, state-of-the-art deep learning architectures coupled with tools that leverage modern hardware accelerators are essential. The work contained in this thesis seeks to address the big-data challenges of LSST by proposing novel efficient deep learning architectures for multivariate time-series classification that can provide state-of-the-art classification of astronomical transients at a fraction of the computational costs of other deep learning approaches. This thesis introduces the depthwise-separable convolution and the notion of convolutional embeddings to the task of time-series classification for gains in classification performance that are achieved with far fewer model parameters than similar methods. It also introduces the attention mechanism to time-series classification that improves performance even further still, with significant improvement in computational efficiency, as well as further reduction in model size. Finally, this thesis pioneers the use of modern model compression techniques to the field of photometric classification for efficient deep learning deployment. These insights informed the final architecture which was deployed in a live production machine learning system, demonstrating the capability to operate efficiently and robustly in real-time, at LSST scale and beyond, ready for the new era of data intensive astronomy