Advances in quantum machine learning

Here we discuss advances in the field of quantum machine learning. The following document offers a hybrid discussion; both reviewing the field as it is currently, and suggesting directions for further research. We include both algorithms and experimental implementations in the discussion. The field's outlook is generally positive, showing significant promise. However, we believe there are appreciable hurdles to overcome before one can claim that it is a primary application of quantum computation.Comment: 38 pages, 17 Figure

Identification of handloom and powerloom fabrics using proximal support vector machines

This study endeavors to recognize handloom and powerloom products by means of proximal support vector machine (PSVM) using the features extracted from gray level images of both fabrics. A k-fold cross validation technique has been applied to assess the accuracy. The robustness, speed of execution, proven accuracy coupled with simplicity in algorithm hold the PSVM as a foremost classifier to recognize handloom and powerloom fabrics.

An Auto-tuner for Quantizing Deep Neural Networks

학위논문 (석사)-- 서울대학교 대학원 : 공과대학 컴퓨터공학부, 2019. 2. 이재욱.AI 기반 응용 프로그램 및 서비스의 확산으로 심층 신경망 (DNN)의 효율적인 처리에 대한 수요가 크게 증가하고 있다. DNN은 많은 계산량과 메모리 공간을 필요로 하기 때문에 컴퓨팅 및 메모리 집약적인 것으로 알려져 있다. 양자화는 적 은 비트 수로 숫자를 표현하여 컴퓨팅 성능과 메모리 공간을 모두 줄이는데 널리 사용되는 방법이다. 그러나 계층별 최적화로 인해 악화되는 다양한 비트 폭을 가진 가능한 숫자 표현의 조합이 수천만가지가 있다, 따라서 DNN에 대한 최적의 숫자 표현을 찾는 것은 어려운 작업이다. 이를 해결하기 위해 본 논문는 DNN 양자화를 위한 자동 튜너를 제안한다. 여기서 자동 튜너는 정확도 제약 조건을 만족시키면서 사용자의 목적 함수를 최소화하여 숫자의 콤팩트한 표현 (숫자유형, 비트 및 바이어스)을 찾아 준다. FPGA 플랫폼과 bit-serial 하드웨어을 응용대상으로 각각 두 DNN 프레임 워크에서 11 개의 DNN 모델을 사용하여 평가 했다. 상대 정확도 최대 7% (1%) 손실이 허용되는 상황에 32 비트 floating-point를 사용하는 baseline 과 비교할 때에 변수 크기가 평균적으로 8배 (7배) 감소되고, 최대로는 16배까지 감소되었다.With the proliferation of AI-based applications and services, there are strong demands for efficient processing of deep neural networks (DNNs). DNNs are known to be both compute- and memory-intensive as they require a tremen- dous amount of computation and large memory space. Quantization is a popu- lar technique to boost efficiency of DNNs by representing a number with fewer bits, hence reducing both computational strength and memory footprint. How- ever, it is a difficult task to find an optimal number representation for a DNN due to a combinatorial explosion in feasible number representations with vary- ing bit widths, which is only exacerbated by layer-wise optimization. To address this, an automatic tuner is proposed in this work for DNN quantization. Here, the auto-tuner can efficiently find a compact representation (type, bit width, and bias) for the number that minimizes the user-supplied objective function, while satisfying the accuracy constraint. The evaluation using eleven DNN models on two DNN frameworks targeting an FPGA platform and a bit-serial hardware, demonstrates over 8× (7×) reduction in the parameter size on aver- age when up to 7% (1%) loss of relative accuracy is tolerable, with a maximum reduction of 16×, compared to the baseline using 32-bit floating-point numbers.Abstract i Contents iv List of Tables v List of Figures vii Chapter 1 Introduction 1 Chapter 2 Motivation 4 2.1 Redundancy in Deep Neural Networks . . . . . . . . . . . . . . 4 2.2 OptimizingNumberRepresentations . . . . . . . . . . . . . . . 6 Chapter 3 Overview 9 Chapter 4 Auto-tuner 12 4.1 ConfiguringtheAuto-tuner .................... 12 4.2 TuningAlgorithm ......................... 13 Chapter 5 Evaluation 19 5.1 Methodology ............................ 19 5.2 Results................................ 20 Chapter 6 Related Work 25 Chapter 7 Conclusion 27 Bibliography 28 국문초록 35 Acknowledgements 36Maste

A Public Fabric Database for Defect Detection Methods and Results

[EN] The use of image processing for the detection and classification of defects has been a reality for some time in science and industry. New methods are continually being presented to improve every aspect of this process. However, these new approaches are applied to a small, private collection of images, which makes a real comparative study of these methods very difficult. The objective of this paper was to compile a public annotated benchmark, that is, an extensive set of images with and without defects, and make these public, to enable the direct comparison of detection and classification methods. Moreover, different methods are reviewed and one of these is applied to the set of images; the results of which are also presented in this paper.The authors thank for the financial support provided by IVACE (Institut Valencia de Competitivitat Empresarial, Spain) and FEDER (Fondo Europeo de Desarrollo Regional, Europe), throughout the projects: AUTOVIMOTION and INTELITEX.Silvestre-Blanes, J.; Albero Albero, T.; Miralles, I.; Pérez-Llorens, R.; Moreno, J. (2019). A Public Fabric Database for Defect Detection Methods and Results. AUTEX Research Journal. 19(4):363-374. https://doi.org/10.2478/aut-2019-0035S36337419

MOD: A novel machine-learning optimal-filtering method for accurate and efficient detection of subthreshold synaptic events in vivo

Background: To understand information coding in single neurons, it is necessary to analyze subthreshold synaptic events, action potentials (APs), and their interrelation in different behavioral states. However, detecting excitatory postsynaptic potentials (EPSPs) or currents (EPSCs) in behaving animals remains challenging, because of unfavorable signal-to-noise ratio, high frequency, fluctuating amplitude, and variable time course of synaptic events. New method: We developed a method for synaptic event detection, termed MOD (Machine-learning Optimal-filtering Detection-procedure), which combines concepts of supervised machine learning and optimal Wiener filtering. Experts were asked to manually score short epochs of data. The algorithm was trained to obtain the optimal filter coefficients of a Wiener filter and the optimal detection threshold. Scored and unscored data were then processed with the optimal filter, and events were detected as peaks above threshold. Results: We challenged MOD with EPSP traces in vivo in mice during spatial navigation and EPSC traces in vitro in slices under conditions of enhanced transmitter release. The area under the curve (AUC) of the receiver operating characteristics (ROC) curve was, on average, 0.894 for in vivo and 0.969 for in vitro data sets, indicating high detection accuracy and efficiency. Comparison with existing methods: When benchmarked using a (1 − AUC)−1 metric, MOD outperformed previous methods (template-fit, deconvolution, and Bayesian methods) by an average factor of 3.13 for in vivo data sets, but showed comparable (template-fit, deconvolution) or higher (Bayesian) computational efficacy. Conclusions: MOD may become an important new tool for large-scale, real-time analysis of synaptic activity

Developing an advanced collision risk model for autonomous vehicles

Aiming at improving road safety, car manufacturers and researchers are verging upon autonomous vehicles. In recent years, collision prediction methods of autonomous vehicles have begun incorporating contextual information such as information about the traffic environment and the relative motion of other traffic participants but still fail to anticipate traffic scenarios of high complexity. During the past two decades, the problem of real-time collision prediction has also been investigated by traffic engineers. In the traffic engineering approach, a collision occurrence can potentially be predicted in real-time based on available data on traffic dynamics such as the average speed and flow of vehicles on a road segment. This thesis attempts to integrate vehicle-level collision prediction approaches for autonomous vehicles with network-level collision prediction, as studied by traffic engineers. [Continues.

A VISION-BASED QUALITY INSPECTION SYSTEM FOR FABRIC DEFECT DETECTION AND CLASSIFICATION

Published ThesisQuality inspection of textile products is an important issue for fabric manufacturers. It is desirable to produce the highest quality goods in the shortest amount of time possible. Fabric faults or defects are responsible for nearly 85% of the defects found by the garment industry. Manufacturers recover only 45 to 65% of their profits from second or off-quality goods. There is a need for reliable automated woven fabric inspection methods in the textile industry. Numerous methods have been proposed for detecting defects in textile. The methods are generally grouped into three main categories according to the techniques they use for texture feature extraction, namely statistical approaches, spectral approaches and model-based approaches. In this thesis, we study one method from each category and propose their combinations in order to get improved fabric defect detection and classification accuracy. The three chosen methods are the grey level co-occurrence matrix (GLCM) from the statistical category, the wavelet transform from the spectral category and the Markov random field (MRF) from the model-based category. We identify the most effective texture features for each of those methods and for different fabric types in order to combine them. Using GLCM, we identify the optimal number of features, the optimal quantisation level of the original image and the optimal intersample distance to use. We identify the optimal GLCM features for different types of fabrics and for three different classifiers. Using the wavelet transform, we compare the defect detection and classification performance of features derived from the undecimated discrete wavelet and those derived from the dual-tree complex wavelet transform. We identify the best features for different types of fabrics. Using the Markov random field, we study the performance for fabric defect detection and classification of features derived from different models of Gaussian Markov random fields of order from 1 through 9. For each fabric type we identify the best model order. Finally, we propose three combination schemes of the best features identified from the three methods and study their fabric detection and classification performance. They lead generally to improved performance as compared to the individual methods, but two of them need further improvement