Randomness in neural networks: an overview

Neural networks, as powerful tools for data mining and knowledge engineering, can learn from data to build feature-based classifiers and nonlinear predictive models. Training neural networks involves the optimization of nonconvex objective functions, and usually, the learning process is costly and infeasible for applications associated with data streams. A possible, albeit counterintuitive, alternative is to randomly assign a subset of the networks’ weights so that the resulting optimization task can be formulated as a linear least-squares problem. This methodology can be applied to both feedforward and recurrent networks, and similar techniques can be used to approximate kernel functions. Many experimental results indicate that such randomized models can reach sound performance compared to fully adaptable ones, with a number of favorable benefits, including (1) simplicity of implementation, (2) faster learning with less intervention from human beings, and (3) possibility of leveraging overall linear regression and classification algorithms (e.g., ℓ 1 norm minimization for obtaining sparse formulations). This class of neural networks attractive and valuable to the data mining community, particularly for handling large scale data mining in real-time. However, the literature in the field is extremely vast and fragmented, with many results being reintroduced multiple times under different names. This overview aims to provide a self-contained, uniform introduction to the different ways in which randomization can be applied to the design of neural networks and kernel functions. A clear exposition of the basic framework underlying all these approaches helps to clarify innovative lines of research, open problems, and most importantly, foster the exchanges of well-known results throughout different communities. WIREs Data Mining Knowl Discov 2017, 7:e1200. doi: 10.1002/widm.1200

Teacher-Student Architecture for Knowledge Distillation: A Survey

Although Deep neural networks (DNNs) have shown a strong capacity to solve large-scale problems in many areas, such DNNs are hard to be deployed in real-world systems due to their voluminous parameters. To tackle this issue, Teacher-Student architectures were proposed, where simple student networks with a few parameters can achieve comparable performance to deep teacher networks with many parameters. Recently, Teacher-Student architectures have been effectively and widely embraced on various knowledge distillation (KD) objectives, including knowledge compression, knowledge expansion, knowledge adaptation, and knowledge enhancement. With the help of Teacher-Student architectures, current studies are able to achieve multiple distillation objectives through lightweight and generalized student networks. Different from existing KD surveys that primarily focus on knowledge compression, this survey first explores Teacher-Student architectures across multiple distillation objectives. This survey presents an introduction to various knowledge representations and their corresponding optimization objectives. Additionally, we provide a systematic overview of Teacher-Student architectures with representative learning algorithms and effective distillation schemes. This survey also summarizes recent applications of Teacher-Student architectures across multiple purposes, including classification, recognition, generation, ranking, and regression. Lastly, potential research directions in KD are investigated, focusing on architecture design, knowledge quality, and theoretical studies of regression-based learning, respectively. Through this comprehensive survey, industry practitioners and the academic community can gain valuable insights and guidelines for effectively designing, learning, and applying Teacher-Student architectures on various distillation objectives.Comment: 20 pages. arXiv admin note: substantial text overlap with arXiv:2210.1733

Sonar discrimination of cylinders from different angles using neural networks neural networks

This paper describes an underwater object discrimination system applied to recognize cylinders of various compositions from different angles. The system is based on a new combination of simulated dolphin clicks, simulated auditory filters and artificial neural networks. The model demonstrates its potential on real data collected from four different cylinders in an environment where the angles were controlled in order to evaluate the models capabilities to recognize cylinders independent of angles. 1. INTRODUCTION Dolphins possess an excellent sonar system for solving underwater target discrimination and recognition tasks in shallow water (see e.g., [2]). This has inspired research in new sonar systems based on biological knowledge, i.e. modeling the dolphins discrimination capabilities (see e.g., [4] and [5]). The fact that the inner ear of the dolphin has many similarities with the human inner ear makes it tempting to use knowledge from simulations of the human auditory system when t..

A Comprehensive Survey on Knowledge Distillation of Diffusion Models

Diffusion Models (DMs), also referred to as score-based diffusion models, utilize neural networks to specify score functions. Unlike most other probabilistic models, DMs directly model the score functions, which makes them more flexible to parametrize and potentially highly expressive for probabilistic modeling. DMs can learn fine-grained knowledge, i.e., marginal score functions, of the underlying distribution. Therefore, a crucial research direction is to explore how to distill the knowledge of DMs and fully utilize their potential. Our objective is to provide a comprehensible overview of the modern approaches for distilling DMs, starting with an introduction to DMs and a discussion of the challenges involved in distilling them into neural vector fields. We also provide an overview of the existing works on distilling DMs into both stochastic and deterministic implicit generators. Finally, we review the accelerated diffusion sampling algorithms as a training-free method for distillation. Our tutorial is intended for individuals with a basic understanding of generative models who wish to apply DM's distillation or embark on a research project in this field

A survey of uncertainty in deep neural networks

Over the last decade, neural networks have reached almost every field of science and become a crucial part of various real world applications. Due to the increasing spread, confidence in neural network predictions has become more and more important. However, basic neural networks do not deliver certainty estimates or suffer from over- or under-confidence, i.e. are badly calibrated. To overcome this, many researchers have been working on understanding and quantifying uncertainty in a neural network's prediction. As a result, different types and sources of uncertainty have been identified and various approaches to measure and quantify uncertainty in neural networks have been proposed. This work gives a comprehensive overview of uncertainty estimation in neural networks, reviews recent advances in the field, highlights current challenges, and identifies potential research opportunities. It is intended to give anyone interested in uncertainty estimation in neural networks a broad overview and introduction, without presupposing prior knowledge in this field. For that, a comprehensive introduction to the most crucial sources of uncertainty is given and their separation into reducible model uncertainty and irreducible data uncertainty is presented. The modeling of these uncertainties based on deterministic neural networks, Bayesian neural networks (BNNs), ensemble of neural networks, and test-time data augmentation approaches is introduced and different branches of these fields as well as the latest developments are discussed. For a practical application, we discuss different measures of uncertainty, approaches for calibrating neural networks, and give an overview of existing baselines and available implementations. Different examples from the wide spectrum of challenges in the fields of medical image analysis, robotics, and earth observation give an idea of the needs and challenges regarding uncertainties in the practical applications of neural networks. Additionally, the practical limitations of uncertainty quantification methods in neural networks for mission- and safety-critical real world applications are discussed and an outlook on the next steps towards a broader usage of such methods is given

일반화 능력의 향상을 위한 깊은 신경망 양자화

학위논문(박사)--서울대학교 대학원 :공과대학 전기·컴퓨터공학부,2020. 2. 성원용.최근 깊은 신경망(deep neural network, DNN)은 영상, 음성 인식 및 합성 등 다양한 분야에서 좋은 성능을 보이고 있다. 하지만 대부분의 인공신경망은 많은 가중치(parameter) 수와 계산량을 요구하여 임베디드 시스템에서의 동작을 방해한다. 인공신경망은 낮은 정밀도에서도 잘 동작하는 인간의 신경세포를 모방하였기 떄문에 낮은 정밀도에서도 잘 동작할 가능성을 가지고 있다. 인공신경망의 양자화(quantization)는 이러한 특징을 이용한다. 일반적으로 깊은 신경망 고정소수점 양자화는 8-bit 이상의 단어길이에서 부동소수점과 유사한 성능을 얻을 수있지만, 그보다 낮은 1-, 2-bit에서는 성능이 떨어진다. 이러한 문제를 해결하기 위해 기존 연구들은 불균형 양자화기나 적응적 양자화 등의 더 정밀한 인공신경망 양자화 방법을 사용하였다. 본 논문은 기존의 연구와 매우 다른 방법을 제시한다. 본 연구는 고정 소수점 네트워크의 일반화능력을 향상시키는데 초점을 맞추었으며, 이를 위해 재훈련(retraining) 알고리즘에 기반하여 양자화된 인공신경망의 성능을 분석한다. 성능 분석은 레이어별 민감도 측정(layer-wise sensitivity analysis)에 기반한다. 또한 양자화 모델의 넓이와 깊이에 따른 성능도 분석한다. 분석된 결과를 바탕으로 양자화 스텝 적응 훈련법(quantization step size adaptation)과 점진적 양자화 훈련 방법(gradual quantization)을 제안한다. 양자화된 신경망 훈련시 양자화 노이즈를 적당히 조정하여 손실 평면(loss surface)상에 평평한 미니마(minima)에 도달 할 수 있는 양자화 훈련 방법 또한 제안한다. HLHLp (high-low-high-low-precision)로 명명된 훈련 방법은 양자화 정밀도를 훈련중에 높게-낮게-높게-낮게 바꾸면서 훈련한다. 훈련률(learning rate)도 양자화 스텝 사이즈를 고려하여 유동적으로 바뀐다. 제안하는 훈련방법은 일반적인 방법으로 훈련된 양자화 모델에 비해 상당히 좋은 성능을 보였다. 또한 선훈련된 선생 모델로 학생 모델을 훈련하는 지식 증류(knowledge distillation, KD) 기술을 이용하여 양자화의 성능을 높이는 방법을 제안한다. 특히 선생 모델을 선택하는 방법과 지식 증류의 하이퍼파라미터가 성능에 미치는 영향을 분석한다. 부동소수점 선생모델과 양자화 된 선생 모델을 사용하여 훈련 시킨 결과 선생 모델이 만들어내는 소프트맥스(softmax) 분포가 지식증류학습 결과에 크게 영향을 주는 것을 발견하였다. 소프트맥스 분포는 지식증류의 하이퍼파라미터들을 통해 조절될수 있으므로 지식증류 하이퍼파라미터들간의 연관관계 분석을 통해 높은 성능을 얻을 수 있었다. 또한 점진적으로 소프트 손실 함수 반영 비율을 훈련중에 줄여가는 점진적 소프트 손실 감소(gradual soft loss reducing)방법을 제안하였다. 뿐만 아니라 여러 양자화모델을 평균내어 높은 일반화 능력을 갖는 양자화 모델을 얻는 훈련 방법인 확률 양자화 가중치 평균(stochastic quantized weight averaging, SQWA) 훈련법을 제안한다. 제안하는 방법은 (1) 부동소수점 훈련, (2) 부동소수점 모델의 직접 양자화(direct quantization), (3) 재훈련(retraining)과정에서 진동 훈련율(cyclical learning rate)을 사용하여 휸련율이 진동내에서 가장 낮을 때 모델들을 저장, (4) 저장된 모델들을 평균, (5) 평균 된 모델을 낮은 훈련율로 재조정 하는 다중 단계 훈련법이다. 추가로 양자화 가중치 도메인에서 여러 양자화 모델들을 하나의 손실평면내에 동시에 나타낼 수 있는 심상(visualization) 방법을 제안한다. 제안하는 심상 방법을 통해 SQWA로 훈련된 양자화 모델은 손실평면의 가운데 부분에 있다는 것을 보였다.Deep neural networks (DNNs) achieve state-of-the-art performance for various applications such as image recognition and speech synthesis across different fields. However, their implementation in embedded systems is difficult owing to the large number of associated parameters and high computational costs. In general, DNNs operate well using low-precision parameters because they mimic the operation of human neurons; therefore, quantization of DNNs could further improve their operational performance. In many applications, word-length larger than 8 bits leads to DNN performance comparable to that of a full-precision model; however, shorter word-length such as those of 1 or 2 bits can result in significant performance degradation. To alleviate this problem, complex quantization methods implemented via asymmetric or adaptive quantizers have been employed in previous works. In contrast, in this study, we propose a different approach for quantization of DNNs. In particular, we focus on improving the generalization capability of quantized DNNs (QDNNs) instead of employing complex quantizers. To this end, first, we analyze the performance characteristics of quantized DNNs using a retraining algorithm; we employ layer-wise sensitivity analysis to investigate the quantization characteristics of each layer. In addition, we analyze the differences in QDNN performance for different quantized network sizes. Based on our analyses, two simple quantization training techniques, namely \textit{adaptive step size retraining} and \textit{gradual quantization} are proposed. Furthermore, a new training scheme for QDNNs is proposed, which is referred to as high-low-high-low-precision (HLHLp) training scheme, that allows the network to achieve flat minima on its loss surface with the aid of quantization noise. As the name suggests, the proposed training method employs high-low-high-low precision for network training in an alternating manner. Accordingly, the learning rate is also abruptly changed at each stage. Our obtained analysis results include that the proposed training technique leads to good performance improvement for QDNNs compared with previously reported fine tuning-based quantization schemes. Moreover, the knowledge distillation (KD) technique that utilizes a pre-trained teacher model for training a student network is exploited for the optimization of the QDNNs. We explore the effect of teacher network selection and investigate that of different hyperparameters on the quantization of DNNs using KD. In particular, we use several large floating-point and quantized models as teacher networks. Our experiments indicate that, for effective KD training, softmax distribution produced by a teacher network is more important than its performance. Furthermore, because softmax distribution of a teacher network can be controlled using KD hyperparameters, we analyze the interrelationship of each KD component for QDNN training. We show that even a small teacher model can achieve the same distillation performance as a larger teacher model. We also propose the gradual soft loss reducing (GSLR) technique for robust KD-based QDNN optimization, wherein the mixing ratio of hard and soft losses during training is controlled. In addition, we present a new QDNN optimization approach, namely \textit{stochastic quantized weight averaging} (SQWA), to design low-precision DNNs with good generalization capability using model averaging. The proposed approach includes (1) floating-point model training, (2) direct quantization of weights, (3) capture of multiple low-precision models during retraining with cyclical learning rate, (4) averaging of the captured models, and (5) re-quantization of the averaged model and its fine-tuning with low learning rate. Additionally, we present a loss-visualization technique for the quantized weight domain to elucidate the behavior of the proposed method. Our visualization results indicate that a QDNN optimized using our proposed approach is located near the center of the flat minimum on the loss surface.1.Introduction 1 1.1 Quantization of Deep Neural Networks 1 1.2 Generalization Capability of DNNs 3 1.3 Improved Generalization Capability of QDNNs 3 1.4 Outline of the Dissertation 5 2. Analysis of Fixedpoint Quantization of Deep Neural Networks 6 2.1 Introduction 6 2.2 Fixedpoint Performance Analysis of Deep Neural Networks 8 2.2.1 Model Design of Deep Neural Networks 8 2.2.2 Retrainbased Weight Quantization 10 2.2.3 Quantization Sensitivity Analysis 12 2.2.4 Empirical Analysis 13 2.3 Step Size Adaptation and Gradual Quantization for Retraining of DeepNeural Networks 22 2.3.1 Stepsize adaptation during retraining 22 2.3.2 Gradual quantization scheme 24 2.3.3 Experimental Results 24 2.4 Concluding remarks 30 3. HLHLp:Quantized Neural Networks Training for Reaching Flat Minimain Loss Surface 32 3.1 Introduction 32 3.2 Related Works 33 3.2.1 Quantization of Deep Neural Networks 33 3.2.2 Flat Minima in Loss Surfaces 34 3.3 Training QDNN for IMproved Generalization Capability 35 3.3.1 Analysis of Training with Quantized Weights 35 3.3.2 Highlowhighlowprecision Training 38 3.4 Experimental Results 40 3.4.1 Image Classification with CNNs 41 3.4.2 Language Modeling on PTB and WikiText2 44 3.4.3 Speech Recognition on WSJ Corpus 48 3.4.4 Discussion 49 3.5 Concluding Remarks 55 4 Knowledge Distillation for Optimization of Quantized Deep Neural Networks 56 4.1 Introduction 56 4.2 Quantized Deep Neural Netowrk Training Using Knowledge Distillation 57 4.2.1 Quantization of deep neural networks and knowledge distillation 58 4.2.2 Teacher model selection for KD 59 4.2.3 Discussion on hyperparameters of KD 62 4.3 Experimental Results 62 4.3.1 Experimental setup 62 4.3.2 Results on CIFAR10 and CIFAR100 64 4.3.3 Model size and temperature 66 4.3.4 Gradual Soft Loss Reducing 68 4.4 Concluding Remarks 68 5 SQWA: Stochastic Quantized Weight Averaging for Improving the Generalization Capability of LowPrecision Deep Neural Networks 70 5.1 Introduction 70 5.2 Related works 71 5.2.1 Quantization of deep neural networks for efficient implementations 71 5.2.2 Stochastic weight averaging and losssurface visualization 72 5.3 Quantization of DNN and loss surface visualization 73 5.3.1 Quantization of deep neural networks 73 5.3.2 Loss surface visualization for QDNNs 75 5.4 SQWA algorithm 76 5.5 Experimental results 80 5.5.1 CIFAR100 80 5.5.2 ImageNet 87 5.6 Concluding remarks 90 6 Conclusion 92 Abstract (In Korean) 110Docto

Machine learning and its applications in reliability analysis systems

In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs). We begin by investigating some ML paradigms and their- techniques, go on to discuss the possible applications of ML in improving RAs performance, and lastly give guidelines of the architecture of learning RAs. Our survey of ML covers both levels of Neural Network learning and Symbolic learning. In symbolic process learning, five types of learning and their applications are discussed: rote learning, learning from instruction, learning from analogy, learning from examples, and learning from observation and discovery. The Reliability Analysis systems (RAs) presented in this thesis are mainly designed for maintaining plant safety supported by two functions: risk analysis function, i.e., failure mode effect analysis (FMEA) ; and diagnosis function, i.e., real-time fault location (RTFL). Three approaches have been discussed in creating the RAs. According to the result of our survey, we suggest currently the best design of RAs is to embed model-based RAs, i.e., MORA (as software) in a neural network based computer system (as hardware). However, there are still some improvement which can be made through the applications of Machine Learning. By implanting the 'learning element', the MORA will become learning MORA (La MORA) system, a learning Reliability Analysis system with the power of automatic knowledge acquisition and inconsistency checking, and more. To conclude our thesis, we propose an architecture of La MORA

A Large Imaging Database and Novel Deep Neural Architecture for Covid-19 Diagnosis

Deep learning methodologies constitute nowadays the main approach for medical image analysis and disease prediction. Large annotated databases are necessary for developing these methodologies; such databases are difficult to obtain and to make publicly available for use by researchers and medical experts. In this paper, we focus on diagnosis of Covid-19 based on chest 3-D CT scans and develop a dual knowledge framework, including a large imaging database and a novel deep neural architecture. We introduce COV19-CT-DB, a very large database annotated for COVID-19 that consists of 7,750 3-D CT scans, 1,650 of which refer to COVID-19 cases and 6,100 to non-COVID19 cases. We use this database to train and develop the RACNet architecture. This architecture performs 3-D analysis based on a CNN-RNN network and handles input CT scans of different lengths, through the introduction of dynamic routing, feature alignment and a mask layer. We conduct a large experimental study that illustrates that the RACNet network has the best performance compared to other deep neural networks i) when trained and tested on COV19-CT-DB; ii) when tested, or when applied, through transfer learning, to other public databases