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Adversarial Robustness For Machine Learning Models

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Robust Machine Learning in Adversarial Setting with Provable Guarantee

Robust Machine Learning in Adversarial Setting with Provable Guarantee Book
Author : Yizhen Wang
Publisher : Unknown
Release : 2020
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Over the last decade, machine learning systems have achieved state-of-the-art performance in many fields, and are now used in increasing number of applications. However, recent research work has revealed multiple attacks to machine learning systems that significantly reduce the performance by manipulating the training or test data. As machine learning is increasingly involved in high-stake decision making processes, the robustness of machine learning systems in adversarial environment becomes a major concern. This dissertation attempts to build machine learning systems robust to such adversarial manipulation with the emphasis on providing theoretical performance guarantees. We consider adversaries in both test and training time, and make the following contributions. First, we study the robustness of machine learning algorithms and model to test-time adversarial examples. We analyze the distributional and finite sample robustness of nearest neighbor classification, and propose a modified 1-Nearest-Neighbor classifier that both has theoretical guarantee and empirical improvement in robustness. Second, we examine the robustness of malware detectors to program transformation. We propose novel attacks that evade existing detectors using program transformation, and then show program normalization as a provably robust defense against such transformation. Finally, we investigate data poisoning attacks and defenses for online learning, in which models update and predict over data stream in real-time. We show efficient attacks for general adversarial objectives, analyze the conditions for which filtering based defenses are effective, and provide practical guidance on choosing defense mechanisms and parameters.

Adversarial Machine Learning

Adversarial Machine Learning Book
Author : Yevgeniy Vorobeychik,Murat Kantarcioglu
Publisher : Morgan & Claypool Publishers
Release : 2018-08-08
ISBN : 168173396X
Language : En, Es, Fr & De

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Book Description :

The increasing abundance of large high-quality datasets, combined with significant technical advances over the last several decades have made machine learning into a major tool employed across a broad array of tasks including vision, language, finance, and security. However, success has been accompanied with important new challenges: many applications of machine learning are adversarial in nature. Some are adversarial because they are safety critical, such as autonomous driving. An adversary in these applications can be a malicious party aimed at causing congestion or accidents, or may even model unusual situations that expose vulnerabilities in the prediction engine. Other applications are adversarial because their task and/or the data they use are. For example, an important class of problems in security involves detection, such as malware, spam, and intrusion detection. The use of machine learning for detecting malicious entities creates an incentive among adversaries to evade detection by changing their behavior or the content of malicius objects they develop. The field of adversarial machine learning has emerged to study vulnerabilities of machine learning approaches in adversarial settings and to develop techniques to make learning robust to adversarial manipulation. This book provides a technical overview of this field. After reviewing machine learning concepts and approaches, as well as common use cases of these in adversarial settings, we present a general categorization of attacks on machine learning. We then address two major categories of attacks and associated defenses: decision-time attacks, in which an adversary changes the nature of instances seen by a learned model at the time of prediction in order to cause errors, and poisoning or training time attacks, in which the actual training dataset is maliciously modified. In our final chapter devoted to technical content, we discuss recent techniques for attacks on deep learning, as well as approaches for improving robustness of deep neural networks. We conclude with a discussion of several important issues in the area of adversarial learning that in our view warrant further research. Given the increasing interest in the area of adversarial machine learning, we hope this book provides readers with the tools necessary to successfully engage in research and practice of machine learning in adversarial settings.

Strengthening Deep Neural Networks

Strengthening Deep Neural Networks Book
Author : Katy Warr
Publisher : O'Reilly Media
Release : 2019-07-03
ISBN : 149204492X
Language : En, Es, Fr & De

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Book Description :

As deep neural networks (DNNs) become increasingly common in real-world applications, the potential to deliberately "fool" them with data that wouldn’t trick a human presents a new attack vector. This practical book examines real-world scenarios where DNNs—the algorithms intrinsic to much of AI—are used daily to process image, audio, and video data. Author Katy Warr considers attack motivations, the risks posed by this adversarial input, and methods for increasing AI robustness to these attacks. If you’re a data scientist developing DNN algorithms, a security architect interested in how to make AI systems more resilient to attack, or someone fascinated by the differences between artificial and biological perception, this book is for you. Delve into DNNs and discover how they could be tricked by adversarial input Investigate methods used to generate adversarial input capable of fooling DNNs Explore real-world scenarios and model the adversarial threat Evaluate neural network robustness; learn methods to increase resilience of AI systems to adversarial data Examine some ways in which AI might become better at mimicking human perception in years to come

Intelligent Systems and Applications

Intelligent Systems and Applications Book
Author : Kohei Arai
Publisher : Springer Nature
Release : 2021-05-06
ISBN : 3030551873
Language : En, Es, Fr & De

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Book Description :

Download Intelligent Systems and Applications book written by Kohei Arai, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Machine Learning and Knowledge Discovery in Databases

Machine Learning and Knowledge Discovery in Databases Book
Author : Peggy Cellier,Kurt Driessens
Publisher : Springer Nature
Release : 2020-03-27
ISBN : 3030438236
Language : En, Es, Fr & De

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Book Description :

This two-volume set constitutes the refereed proceedings of the workshops which complemented the 19th Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD, held in Würzburg, Germany, in September 2019. The 70 full papers and 46 short papers presented in the two-volume set were carefully reviewed and selected from 200 submissions. The two volumes (CCIS 1167 and CCIS 1168) present the papers that have been accepted for the following workshops: Workshop on Automating Data Science, ADS 2019; Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence and eXplainable Knowledge Discovery in Data Mining, AIMLAI-XKDD 2019; Workshop on Decentralized Machine Learning at the Edge, DMLE 2019; Workshop on Advances in Managing and Mining Large Evolving Graphs, LEG 2019; Workshop on Data and Machine Learning Advances with Multiple Views; Workshop on New Trends in Representation Learning with Knowledge Graphs; Workshop on Data Science for Social Good, SoGood 2019; Workshop on Knowledge Discovery and User Modelling for Smart Cities, UMCIT 2019; Workshop on Data Integration and Applications Workshop, DINA 2019; Workshop on Machine Learning for Cybersecurity, MLCS 2019; Workshop on Sports Analytics: Machine Learning and Data Mining for Sports Analytics, MLSA 2019; Workshop on Categorising Different Types of Online Harassment Languages in Social Media; Workshop on IoT Stream for Data Driven Predictive Maintenance, IoTStream 2019; Workshop on Machine Learning and Music, MML 2019; Workshop on Large-Scale Biomedical Semantic Indexing and Question Answering, BioASQ 2019.

Science of Cyber Security

Science of Cyber Security Book
Author : Feng Liu,Jia Xu,Shouhuai Xu,Moti Yung
Publisher : Springer Nature
Release : 2020-01-11
ISBN : 3030346374
Language : En, Es, Fr & De

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Book Description :

This book constitutes the proceedings of the Second International Conference on Science of Cyber Security, SciSec 2019, held in Nanjing, China, in August 2019. The 20 full papers and 8 short papers presented in this volume were carefully reviewed and selected from 62 submissions. These papers cover the following subjects: Artificial Intelligence for Cybersecurity, Machine Learning for Cybersecurity, and Mechanisms for Solving Actual Cybersecurity Problems (e.g., Blockchain, Attack and Defense; Encryptions with Cybersecurity Applications).

Towards Robust Deep Neural Networks

Towards Robust Deep Neural Networks Book
Author : Andras Rozsa
Publisher : Unknown
Release : 2018
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

One of the greatest technological advancements of the 21st century has been the rise of machine learning. This thriving field of research already has a great impact on our lives and, considering research topics and the latest advancements, will continue to rapidly grow. In the last few years, the most powerful machine learning models have managed to reach or even surpass human level performance on various challenging tasks, including object or face recognition in photographs. Although we are capable of designing and training machine learning models that perform extremely well, the intriguing discovery of adversarial examples challenges our understanding of these models and raises questions about their real-world applications. That is, vulnerable machine learning models misclassify examples that are indistinguishable from correctly classified examples by human observers. Furthermore, in many cases a variety of machine learning models having different architectures and/or trained on different subsets of training data misclassify the same adversarial example formed by an imperceptibly small perturbation. In this dissertation, we mainly focus on adversarial examples and closely related research areas such as quantifying the quality of adversarial examples in terms of human perception, proposing algorithms for generating adversarial examples, and analyzing the cross-model generalization properties of such examples. We further explore the robustness of facial attribute recognition and biometric face recognition systems to adversarial perturbations, and also investigate how to alleviate the intriguing properties of machine learning models.

Engineering Dependable and Secure Machine Learning Systems

Engineering Dependable and Secure Machine Learning Systems Book
Author : Onn Shehory,Eitan Farchi,Guy Barash
Publisher : Springer Nature
Release : 2020-11-07
ISBN : 3030621448
Language : En, Es, Fr & De

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Book Description :

This book constitutes the revised selected papers of the Third International Workshop on Engineering Dependable and Secure Machine Learning Systems, EDSMLS 2020, held in New York City, NY, USA, in February 2020. The 7 full papers and 3 short papers were thoroughly reviewed and selected from 16 submissions. The volume presents original research on dependability and quality assurance of ML software systems, adversarial attacks on ML software systems, adversarial ML and software engineering, etc.

Artificial Neural Networks and Machine Learning ICANN 2020

Artificial Neural Networks and Machine Learning     ICANN 2020 Book
Author : Igor Farkaš
Publisher : Springer Nature
Release : 2021-05-06
ISBN : 3030616096
Language : En, Es, Fr & De

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Book Description :

Download Artificial Neural Networks and Machine Learning ICANN 2020 book written by Igor Farkaš, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Secure and Private Machine Learning for Smart Devices

Secure and Private Machine Learning for Smart Devices Book
Author : MOUSTAFA FARID TAHA MOHAMMED ALZANTOT
Publisher : Unknown
Release : 2019
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Nowadays, machine learning models and especially deep neural networks are achieving outstanding levels of accuracy in different tasks such as image understanding and speech recognition. Therefore, they are widely used in the pervasive smart connected devices, e.g., smartphones, security cameras, and digital personal assistants, to make intelligent inferences from sensor data. However, despite their high level of accuracy, researchers have recently found that malicious attackers can easily fool machine learning models. Therefore, this brings into question the robustness of machine learning models under attacks, especially in the context of privacy-sensitive and safety-critical applications. In this dissertation, we investigate the security and privacy of machine learning models. First, we consider the problem of adversarial attacks that fool machine learning models under the practical setting where the attacker has limited information about the victim model and also restricted access to it. We introduce, GenAttack, an efficient method to generate adversarial examples against black-box machine learning. GenAttack requires 235 times fewer model queries than previous state-of-the-art methods while achieving a higher success rate in targeted attacks against the large scale Inception-v3 image classification models. We also show how GenAttack can be used to overcome a set of different recently proposed methods of model defenses. Furthermore, while prior research on adversarial attacks against machine learning models has focused only on image recognition models due to the challenges of attacking models of other data modalities such as text and speech, we show GenAttack can be extended to attack both speech recognition and text understanding models with a high success rate. We achieve 87% success rate against a speech command recognition model and 97% success rate against a natural language sentiment classification model. In the second part of this dissertation, we focus on methods for improving the robustness of machine learning models against security and privacy threats. A significant limitation of deep neural networks is their lack of explanation for their predictions. Therefore, we present NeuroMask, an algorithm for generating accurate explanations of the neural network prediction results. Another serious threat against the voice-controlled devices is the audio spoofing attacks. We present a deep residual convolutional network for detecting two different kinds of attacks: the logical access attack and the physical access attack. Our model achieves 6.02% and 2.78% equal error rate (EER) on the evaluation datasets of the ASVSpoof2019 competition for the detection of the logical access, and physical access attacks, respectively. To alleviate the privacy concerns of unwanted inferences while sharing private sensor data measurements, we introduce, PhysioGAN, a novel model architecture for generating high-quality synthetic datasets of physiological sensor readings. Using evaluation experiments on two different datasets: ECG classification dataset and motion sensors for human activity recognition dataset, we show that compared to previous methods of training sensor data generative models PhysioGAN is capable of producing synthetic datasets that are both more accurate and more diverse. Therefore, synthetic datasets generated by PhysioGAN are a good replacement to be shared instead of the real private datasets with a moderate loss in their utility. Finally, we show how we apply the differential privacy techniques to extend the training of the generative adversarial networks to produce synthetic datasets with formal privacy guarantees.

Machine Learning in Adversarial Settings

Machine Learning in Adversarial Settings Book
Author : Hossein Hosseini
Publisher : Unknown
Release : 2019
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Deep neural networks have achieved remarkable success over the last decade in a variety of tasks. Such models are, however, typically designed and developed with the implicit assumption that they will be deployed in benign settings. With the increasing use of learning systems in security-sensitive and safety-critical application, such as banking, medical diagnosis, and autonomous cars, it is important to study and evaluate their performance in adversarial settings. The security of machine learning systems has been studied from different perspectives. Learning models are subject to attacks at both training and test phases. The main threat at test time is evasion attack, in which the attacker subtly modifies input data such that a human observer would perceive the original content, but the model generates different outputs. Such inputs, known as adversarial examples, has been used to attack voice interfaces, face-recognition systems and text classifiers. The goal of this dissertation is to investigate the test-time vulnerabilities of machine learning systems in adversarial settings and develop robust defensive mechanisms. The dissertation covers two classes of models, 1) commercial ML products developed by Google, namely Perspective, Cloud Vision, and Cloud Video Intelligence APIs, and 2) state-of-the-art image classification algorithms. In both cases, we propose novel test-time attack algorithms and also present defense methods against such attacks.

Reliable Machine Learning Via Distributional Robustness

Reliable Machine Learning Via Distributional Robustness Book
Author : Hongseok Namkoong
Publisher : Unknown
Release : 2019
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

As machine learning systems increasingly get applied in high-stake domains such as autonomous vehicles and medical diagnosis, it is imperative that they maintain good performance when deployed. Modeling assumptions rarely hold due to noisy inputs, shifts in environment, unmeasured confounders, and even adversarial attacks to the system. The standard machine learning paradigm that optimize average performance is brittle to even small amounts of noise, and exhibit poor performance on underrepresented minority groups. We study \emph{distributionally robust} learning procedures that explicitly protect against potential shifts in the data-generating distribution. Instead of doing well just on average, distributionally robust methods learn models that can do well on a range of scenarios that are different to the training distribution. In the first part of thesis, we show that robustness to small perturbations in the data allows better generalization by optimally trading between approximation and estimation error. We show that robust solutions provide asymptotically exact confidence intervals and finite-sample guarantees for stochastic optimization problems. In the second part of the thesis, we focus on notions of distributional robustness that correspond to uniform performance across different subpopulations. We build procedures that balance tail-performance alongside classical notions of average performance. To trade these multiple goals \emph{optimally}, we show fundamental trade-offs (lower bounds), and develop efficient procedures that achieve these limits (upper bounds). Then, we extend our formulation to study partial covariate shifts, where we are interested in marginal distributional shifts on a subset of the feature vector. We provide convex procedures for these robust formulations, and characterize their non-asymptotic convergence properties. In the final part of the thesis, we develop and analyze distributionally robust approaches using Wasserstein distances, which allows models to generalize to distributions that have different support than the training distribution. We show that for smooth neural networks, our robust procedure guarantees performance under imperceptible adversarial perturbations. Extending such notions to protect against distributions defined on learned feature spaces, we show these models can also improve performance across unseen domains.

Machine Learning and Knowledge Discovery in Databases

Machine Learning and Knowledge Discovery in Databases Book
Author : Frank Hutter
Publisher : Springer Nature
Release : 2021-05-06
ISBN : 3030676617
Language : En, Es, Fr & De

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Book Description :

Download Machine Learning and Knowledge Discovery in Databases book written by Frank Hutter, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Interpretable Machine Learning with Python

Interpretable Machine Learning with Python Book
Author : Serg Masís
Publisher : Packt Publishing Ltd
Release : 2021-03-26
ISBN : 1800206577
Language : En, Es, Fr & De

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Book Description :

This hands-on book will help you make your machine learning models fairer, safer, and more reliable and in turn improve business outcomes. Every chapter introduces a new mission where you learn how to apply interpretation methods to realistic use cases with methods that work for any model type as well as methods specific for deep neural networks.

Artificial Neural Networks and Machine Learning ICANN 2019 Image Processing

Artificial Neural Networks and Machine Learning     ICANN 2019  Image Processing Book
Author : Igor V. Tetko,Věra Kůrková,Pavel Karpov,Fabian Theis
Publisher : Springer Nature
Release : 2019-11-03
ISBN : 3030305082
Language : En, Es, Fr & De

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Book Description :

The proceedings set LNCS 11727, 11728, 11729, 11730, and 11731 constitute the proceedings of the 28th International Conference on Artificial Neural Networks, ICANN 2019, held in Munich, Germany, in September 2019. The total of 277 full papers and 43 short papers presented in these proceedings was carefully reviewed and selected from 494 submissions. They were organized in 5 volumes focusing on theoretical neural computation; deep learning; image processing; text and time series; and workshop and special sessions.

Characterizing the Limits and Defenses of Machine Learning in Adversarial Settings

Characterizing the Limits and Defenses of Machine Learning in Adversarial Settings Book
Author : Nicolas Papernot
Publisher : Unknown
Release : 2018
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Advances in machine learning (ML) in recent years have enabled a dizzying array of applications such as object recognition, autonomous systems, security diagnostics, and playing the game of Go. Machine learning is not only a new paradigm for building software and systems, it is bringing social disruption at scale. There is growing recognition that ML exposes new vulnerabilities in software systems, yet the technical communitys understanding of the nature and extent of these vulnerabilities remains limited. In this thesis, I focus my study on the integrity of ML models. Integrity refers here to the faithfulness of model predictions with respect to an expected outcome. This property is at the core of traditional machine learning evaluation, as demonstrated by the pervasiveness of metrics such as accuracy among practitioners. A large fraction of ML techniques were designed for benign execution environments. Yet, the presence of adversaries may invalidate some of these underlying assumptions by forcing a mismatch between the distributions on which the model is trained and tested. As ML is increasingly applied and being relied on for decision-making in critical applications like transportation or energy, the models produced are becoming a target for adversaries who have a strong incentive to force ML to mispredict. I explore the space of attacks against ML integrity at test time. Given full or limited access to a trained model, I devise strategies that modify the test data to create a worst-case drift between the training and test distributions. The implications of this part of my research is that an adversary with very weak access to a system, and little knowledge about the ML techniques it deploys, can nevertheless mount powerful attacks against such systems as long as she has the capability of interacting with it as an oracle: i.e., send inputs of the adversarys choice and observe the ML prediction. This systematic exposition of the poor generalization of ML models indicates the lack of reliable confidence estimates when the model is making predictions far from its training data. Hence, my efforts to increase the robustness of models to these adversarial manipulations strive to decrease the confidence of predictions made far from the training distribution. Informed by my progress on attacks operating in the black-box threat model, I first identify limitations to two defenses: defensive distillation and adversarial training. I then describe recent defensive efforts addressing these shortcomings. To this end, I introduce the Deep k-Nearest Neighbors classifier, which augments deep neural networks with an integrity check at test time. The approach compares internal representations produced by the deep neural network on test data with the ones learned on its training points. Using the labels of training points whose representations neighbor the test input across the deep neural networks layers, I estimate the nonconformity of the prediction with respect to the models training data. An application of conformal prediction methodology then paves the way for more reliable estimates of the models prediction credibility, i.e., how well the prediction is supported by training data. In turn, we distinguish legitimate test data with high credibility from adversarial data with low credibility. This research calls for future efforts to investigate the robustness of individual layers of deep neural networks rather than treating the model as a black-box. This aligns well with the modular nature of deep neural networks, which orchestrate simple computations to model complex functions. This also allows us to draw connections to other areas like interpretability in ML, which seeks to answer the question of: How can we provide an explanation for the model prediction to a human? Another by-product of this research direction is that I better distinguish vulnerabilities of ML models that are a consequence of the ML algorithms from those that can be explained by artifacts in the data.

Advanced Deep Learning with Python

Advanced Deep Learning with Python Book
Author : Ivan Vasilev
Publisher : Packt Publishing Ltd
Release : 2019-12-12
ISBN : 1789952719
Language : En, Es, Fr & De

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Book Description :

Gain expertise in advanced deep learning domains such as neural networks, meta-learning, graph neural networks, and memory augmented neural networks using the Python ecosystem Key Features Get to grips with building faster and more robust deep learning architectures Investigate and train convolutional neural network (CNN) models with GPU-accelerated libraries such as TensorFlow and PyTorch Apply deep neural networks (DNNs) to computer vision problems, NLP, and GANs Book Description In order to build robust deep learning systems, you’ll need to understand everything from how neural networks work to training CNN models. In this book, you’ll discover newly developed deep learning models, methodologies used in the domain, and their implementation based on areas of application. You’ll start by understanding the building blocks and the math behind neural networks, and then move on to CNNs and their advanced applications in computer vision. You'll also learn to apply the most popular CNN architectures in object detection and image segmentation. Further on, you’ll focus on variational autoencoders and GANs. You’ll then use neural networks to extract sophisticated vector representations of words, before going on to cover various types of recurrent networks, such as LSTM and GRU. You’ll even explore the attention mechanism to process sequential data without the help of recurrent neural networks (RNNs). Later, you’ll use graph neural networks for processing structured data, along with covering meta-learning, which allows you to train neural networks with fewer training samples. Finally, you’ll understand how to apply deep learning to autonomous vehicles. By the end of this book, you’ll have mastered key deep learning concepts and the different applications of deep learning models in the real world. What you will learn Cover advanced and state-of-the-art neural network architectures Understand the theory and math behind neural networks Train DNNs and apply them to modern deep learning problems Use CNNs for object detection and image segmentation Implement generative adversarial networks (GANs) and variational autoencoders to generate new images Solve natural language processing (NLP) tasks, such as machine translation, using sequence-to-sequence models Understand DL techniques, such as meta-learning and graph neural networks Who this book is for This book is for data scientists, deep learning engineers and researchers, and AI developers who want to further their knowledge of deep learning and build innovative and unique deep learning projects. Anyone looking to get to grips with advanced use cases and methodologies adopted in the deep learning domain using real-world examples will also find this book useful. Basic understanding of deep learning concepts and working knowledge of the Python programming language is assumed.

Neural Information Processing

Neural Information Processing Book
Author : Tom Gedeon,Kok Wai Wong,Minho Lee
Publisher : Springer Nature
Release : 2019-12-06
ISBN : 3030368084
Language : En, Es, Fr & De

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Book Description :

The two-volume set CCIS 1142 and 1143 constitutes thoroughly refereed contributions presented at the 26th International Conference on Neural Information Processing, ICONIP 2019, held in Sydney, Australia, in December 2019. For ICONIP 2019 a total of 345 papers was carefully reviewed and selected for publication out of 645 submissions. The 168 papers included in this volume set were organized in topical sections as follows: adversarial networks and learning; convolutional neural networks; deep neural networks; embeddings and feature fusion; human centred computing; human centred computing and medicine; human centred computing for emotion; hybrid models; image processing by neural techniques; learning from incomplete data; model compression and optimization; neural network applications; neural network models; semantic and graph based approaches; social network computing; spiking neuron and related models; text computing using neural techniques; time-series and related models; and unsupervised neural models.

Dataset Shift in Machine Learning

Dataset Shift in Machine Learning Book
Author : Joaquin Quiñonero-Candela,Masashi Sugiyama,Neil D. Lawrence,Anton Schwaighofer
Publisher : Mit Press
Release : 2009
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

An overview of recent efforts in the machine learning community to deal with dataset and covariate shift, which occurs when test and training inputs and outputs have different distributions. Dataset shift is a common problem in predictive modeling that occurs when the joint distribution of inputs and outputs differs between training and test stages. Covariate shift, a particular case of dataset shift, occurs when only the input distribution changes. Dataset shift is present in most practical applications, for reasons ranging from the bias introduced by experimental design to the irreproducibility of the testing conditions at training time. (An example is -email spam filtering, which may fail to recognize spam that differs in form from the spam the automatic filter has been built on.) Despite this, and despite the attention given to the apparently similar problems of semi-supervised learning and active learning, dataset shift has received relatively little attention in the machine learning community until recently. This volume offers an overview of current efforts to deal with dataset and covariate shift. The chapters offer a mathematical and philosophical introduction to the problem, place dataset shift in relationship to transfer learning, transduction, local learning, active learning, and semi-supervised learning, provide theoretical views of dataset and covariate shift (including decision theoretic and Bayesian perspectives), and present algorithms for covariate shift. Contributors Shai Ben-David, Steffen Bickel, Karsten Borgwardt, Michael Brückner, David Corfield, Amir Globerson, Arthur Gretton, Lars Kai Hansen, Matthias Hein, Jiayuan Huang, Choon Hui Teo, Takafumi Kanamori, Klaus-Robert Müller, Sam Roweis, Neil Rubens, Tobias Scheffer, Marcel Schmittfull, Bernhard Schölkopf Hidetoshi Shimodaira, Alex Smola, Amos Storkey, Masashi Sugiyama

Advances in Artificial Intelligence

Advances in Artificial Intelligence Book
Author : Cyril Goutte,Xiaodan Zhu
Publisher : Springer Nature
Release : 2020-05-05
ISBN : 3030473589
Language : En, Es, Fr & De

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Book Description :

This book constitutes the refereed proceedings of the 33rd Canadian Conference on Artificial Intelligence, Canadian AI 2020, which was planned to take place in Ottawa, ON, Canada. Due to the COVID-19 pandemic, however, it was held virtually during May 13–15, 2020. The 31 regular papers and 24 short papers presented together with 4 Graduate Student Symposium papers were carefully reviewed and selected from a total of 175 submissions. The selected papers cover a wide range of topics, including machine learning, pattern recognition, natural language processing, knowledge representation, cognitive aspects of AI, ethics of AI, and other important aspects of AI research.