Learning To Sample From Noise With Deep Generative Models

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Learning to Sample from Noise with Deep Generative Models

Author : Florian Bordes
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (12 download)

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Book Synopsis Learning to Sample from Noise with Deep Generative Models by : Florian Bordes

Download or read book Learning to Sample from Noise with Deep Generative Models written by Florian Bordes and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning and specifically deep learning has made significant breakthroughs in recent years concerning different tasks. One well known application of deep learning is computer vision. Tasks such as detection or classification are nearly considered solved by the community. However, training state-of-the-art models for such tasks requires to have labels associated to the data we want to classify. A more general goal is, similarly to animal brains, to be able to design algorithms that can extract meaningful features from data that aren't labeled. Unsupervised learning is one of the axes that try to solve this problem. In this thesis, I present a new way to train a neural network as a generative model capable of generating quality samples (a task akin to imagining). I explain how by starting from noise, it is possible to get samples which are close to the training data. This iterative procedure is called Infusion training and is a novel approach to learning the transition operator of a generative Markov chain. In the first chapter, I present some background about machine learning and probabilistic models. The second chapter presents generative models that inspired this work. The third and last chapter presents and investigates our novel approach to learn a generative model with Infusion training.

Deep Generative Modeling

Author : Jakub M. Tomczak
Publisher : Springer Nature
ISBN 13 : 303164087X
Total Pages : 325 pages
Book Rating : 4.0/5 (316 download)

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Book Synopsis Deep Generative Modeling by : Jakub M. Tomczak

Download or read book Deep Generative Modeling written by Jakub M. Tomczak and published by Springer Nature. This book was released on with total page 325 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Cooperative Learning of Deep Generative Models with Application in Sound Synthesis

Author : Ruiqi Zhong
Publisher :
ISBN 13 :
Total Pages : 37 pages
Book Rating : 4.:/5 (17 download)

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Book Synopsis Cooperative Learning of Deep Generative Models with Application in Sound Synthesis by : Ruiqi Zhong

Download or read book Cooperative Learning of Deep Generative Models with Application in Sound Synthesis written by Ruiqi Zhong and published by . This book was released on 2017 with total page 37 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fires, rainstorms or insect swarms produce natural sounds made up of rapidly occurring acoustic events. which we call "sound textures". This kind of phenomena have been studied by computational audio community [MS11] and neural science people for a long time. From previous studies, it has been verified that sound textures can be schematically synthesized from statistical models fairly well. Here we take a novel approach involving neural networks or deep learning methods. Specifically, we use cooperative training of a descriptor and a generator network, modeled as a convolutional neural network(ConvNet) and a deconvolu- tional neural network(DeconvNet) respectively. From several experiments, we proved that our framework can capture the essence of sound textures and synthesize identifiable natural sound

Deep Generative Models

Author : Anirban Mukhopadhyay
Publisher : Springer Nature
ISBN 13 : 303153767X
Total Pages : 256 pages
Book Rating : 4.0/5 (315 download)

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Book Synopsis Deep Generative Models by : Anirban Mukhopadhyay

Download or read book Deep Generative Models written by Anirban Mukhopadhyay and published by Springer Nature. This book was released on with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Deep Generative Modeling

Author : Jakub M. Tomczak
Publisher : Springer Nature
ISBN 13 : 3030931587
Total Pages : 210 pages
Book Rating : 4.0/5 (39 download)

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Book Synopsis Deep Generative Modeling by : Jakub M. Tomczak

Download or read book Deep Generative Modeling written by Jakub M. Tomczak and published by Springer Nature. This book was released on 2022-02-18 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook tackles the problem of formulating AI systems by combining probabilistic modeling and deep learning. Moreover, it goes beyond typical predictive modeling and brings together supervised learning and unsupervised learning. The resulting paradigm, called deep generative modeling, utilizes the generative perspective on perceiving the surrounding world. It assumes that each phenomenon is driven by an underlying generative process that defines a joint distribution over random variables and their stochastic interactions, i.e., how events occur and in what order. The adjective "deep" comes from the fact that the distribution is parameterized using deep neural networks. There are two distinct traits of deep generative modeling. First, the application of deep neural networks allows rich and flexible parameterization of distributions. Second, the principled manner of modeling stochastic dependencies using probability theory ensures rigorous formulation and prevents potential flaws in reasoning. Moreover, probability theory provides a unified framework where the likelihood function plays a crucial role in quantifying uncertainty and defining objective functions. Deep Generative Modeling is designed to appeal to curious students, engineers, and researchers with a modest mathematical background in undergraduate calculus, linear algebra, probability theory, and the basics in machine learning, deep learning, and programming in Python and PyTorch (or other deep learning libraries). It will appeal to students and researchers from a variety of backgrounds, including computer science, engineering, data science, physics, and bioinformatics, who wish to become familiar with deep generative modeling. To engage the reader, the book introduces fundamental concepts with specific examples and code snippets. The full code accompanying the book is available on github. The ultimate aim of the book is to outline the most important techniques in deep generative modeling and, eventually, enable readers to formulate new models and implement them.

Generative Deep Learning

Author : David Foster
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 109813415X
Total Pages : 456 pages
Book Rating : 4.0/5 (981 download)

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Book Synopsis Generative Deep Learning by : David Foster

Download or read book Generative Deep Learning written by David Foster and published by "O'Reilly Media, Inc.". This book was released on 2022-06-28 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: Generative AI is the hottest topic in tech. This practical book teaches machine learning engineers and data scientists how to use TensorFlow and Keras to create impressive generative deep learning models from scratch, including variational autoencoders (VAEs), generative adversarial networks (GANs), Transformers, normalizing flows, energy-based models, and denoising diffusion models. The book starts with the basics of deep learning and progresses to cutting-edge architectures. Through tips and tricks, you'll understand how to make your models learn more efficiently and become more creative. Discover how VAEs can change facial expressions in photos Train GANs to generate images based on your own dataset Build diffusion models to produce new varieties of flowers Train your own GPT for text generation Learn how large language models like ChatGPT are trained Explore state-of-the-art architectures such as StyleGAN2 and ViT-VQGAN Compose polyphonic music using Transformers and MuseGAN Understand how generative world models can solve reinforcement learning tasks Dive into multimodal models such as DALL.E 2, Imagen, and Stable Diffusion This book also explores the future of generative AI and how individuals and companies can proactively begin to leverage this remarkable new technology to create competitive advantage.

Understanding Expressivity and Trustworthy Aspects of Deep Generative Models

Author : Zhifeng Kong
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (138 download)

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Book Synopsis Understanding Expressivity and Trustworthy Aspects of Deep Generative Models by : Zhifeng Kong

Download or read book Understanding Expressivity and Trustworthy Aspects of Deep Generative Models written by Zhifeng Kong and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Generative Models are a kind of unsupervised deep learning methods that learn the data distribution from samples and then generate unseen, high-quality samples from the learned distributions. These models have achieved tremendous success in different domains and tasks. However, many questions are not well-understood for these models. In order to better understand these models, in this thesis, we investigate the following questions: (i) what is the representation power of deep generative models, and (ii) how to identify and mitigate trustworthy concerns in deep generative models. We study the representation power of deep generative models by looking at which distributions they can approximate arbitrarily well. we study normalizing flows and rigorously establish bounds on their expressive power. Our results indicate that some basic flows are highly expressive in one dimension, but in higher dimensions their representation power may be limited, especially when the flows have moderate depth. We then prove residual flows are universal approximators in maximum mean discrepancy and provide upper bounds on the depths under different assumptions. We next investigate three different trustworthy concerns. The first is how to explain the black box neural networks in these models. We introduce VAE-TracIn, a computationally efficient and theoretically sound interpretability solution, for VAEs. We evaluate VAE-TracIn on real world datasets with extensive quantitative and qualitative analysis. The second is how to mitigate privacy issues in learned generative models. We propose a density-ratio-based framework for efficient approximate data deletion in generative models, which avoids expensive re-training. We provide theoretical guarantees under various learner assumptions and empirically demonstrate our methods across a variety of generative methods. The third is how to prevent undesirable outputs from deep generative models. We take a compute-friendly approach and investigate how to post-edit a pre-trained model to redact certain samples. We consider several unconditional and conditional generative models and various types of descriptions of redacted samples. Extensive evaluations on real-world datasets show our algorithms outperform baseline methods in redaction quality as well as robustness while retaining high generation quality.

Predicting Structured Data

Author : Neural Information Processing Systems Foundation
Publisher : MIT Press
ISBN 13 : 0262026171
Total Pages : 361 pages
Book Rating : 4.2/5 (62 download)

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Book Synopsis Predicting Structured Data by : Neural Information Processing Systems Foundation

Download or read book Predicting Structured Data written by Neural Information Processing Systems Foundation and published by MIT Press. This book was released on 2007 with total page 361 pages. Available in PDF, EPUB and Kindle. Book excerpt: State-of-the-art algorithms and theory in a novel domain of machine learning, prediction when the output has structure.

Cognitive Machine Intelligence

Author : Inam Ullah Khan
Publisher : CRC Press
ISBN 13 : 1040097081
Total Pages : 373 pages
Book Rating : 4.0/5 (4 download)

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Book Synopsis Cognitive Machine Intelligence by : Inam Ullah Khan

Download or read book Cognitive Machine Intelligence written by Inam Ullah Khan and published by CRC Press. This book was released on 2024-08-28 with total page 373 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cognitive Machine Intelligence: Applications, Challenges, and Related Technologies offers a compelling exploration of the transformative landscape shaped by the convergence of machine intelligence, artificial intelligence, and cognitive computing. In this book, the authors navigate through the intricate realms of technology, unveiling the profound impact of cognitive machine intelligence on diverse fields such as communication, healthcare, cybersecurity, and smart city development. The chapters present study on robots and drones to the integration of machine learning with wireless communication networks, IoT, quantum computing, and beyond. The book explores the essential role of machine learning in healthcare, security, and manufacturing. With a keen focus on privacy, trust, and the improvement of human lifestyles, this book stands as a comprehensive guide to the novel techniques and applications driving the evolution of cognitive machine intelligence. The vision presented here extends to smart cities, where AI-enabled techniques contribute to optimal decision-making, and future computing systems address end-to-end delay issues with a central focus on Quality-of-Service metrics. Cognitive Machine Intelligence is an indispensable resource for researchers, practitioners, and enthusiasts seeking a deep understanding of the dynamic landscape at the intersection of artificial intelligence and cognitive computing. This book: Covers a comprehensive exploration of cognitive machine intelligence and its intersection with emerging technologies such as federated learning, blockchain, and 6G and beyond. Discusses the integration of machine learning with various technologies such as wireless communication networks, ad-hoc networks, software-defined networks, quantum computing, and big data. Examines the impact of machine learning on various fields such as healthcare, unmanned aerial vehicles, cybersecurity, and neural networks. Provides a detailed discussion on the challenges and solutions to future computer networks like end-to-end delay issues, Quality of Service (QoS) metrics, and security. Emphasizes the need to ensure privacy and trust while implementing the novel techniques of machine intelligence. It is primarily written for senior undergraduate and graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, and computer engineering.

Deep Learning for Computer Vision

Author : Jyotsnarani Tripathy
Publisher : Leilani Katie Publication
ISBN 13 : 9363484777
Total Pages : 201 pages
Book Rating : 4.3/5 (634 download)

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Book Synopsis Deep Learning for Computer Vision by : Jyotsnarani Tripathy

Download or read book Deep Learning for Computer Vision written by Jyotsnarani Tripathy and published by Leilani Katie Publication. This book was released on 2024-09-05 with total page 201 pages. Available in PDF, EPUB and Kindle. Book excerpt: Jyotsnarani Tripathy, Assistant Professor, Department of CSE-AIML & IoT, Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering & Technology (VNRVJIET), Hyderabad, Telangana, India. Dr.M.Kamal, Assistant Professor, Department of Computer Science, Jamal Mohamed College (Autonomous), Tiruchirappalli, Tamil Nadu, India. G.Ashalatha, Assistant Professor, Department of Artificial Intelligence & Data Science, CSE-Cyber Security, Data Science, Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering &Technology (VNR VJIET), Hyderabad, Telangana, India. Mrs.EMN.Sharmila, Research Scholar, Department of Computer Science, CIRD Research Centre (Approved by University of Mysore), Bengaluru, Karnataka, India.

Deep Learning

Author : Shriram K Vasudevan
Publisher : CRC Press
ISBN 13 : 1000481875
Total Pages : 307 pages
Book Rating : 4.0/5 (4 download)

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Book Synopsis Deep Learning by : Shriram K Vasudevan

Download or read book Deep Learning written by Shriram K Vasudevan and published by CRC Press. This book was released on 2021-12-24 with total page 307 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning: A Comprehensive Guide provides comprehensive coverage of Deep Learning (DL) and Machine Learning (ML) concepts. DL and ML are the most sought-after domains, requiring a deep understanding – and this book gives no less than that. This book enables the reader to build innovative and useful applications based on ML and DL. Starting with the basics of neural networks, and continuing through the architecture of various types of CNNs, RNNs, LSTM, and more till the end of the book, each and every topic is given the utmost care and shaped professionally and comprehensively. Key Features Includes the smooth transition from ML concepts to DL concepts Line-by-line explanations have been provided for all the coding-based examples Includes a lot of real-time examples and interview questions that will prepare the reader to take up a job in ML/DL right away Even a person with a non-computer-science background can benefit from this book by following the theory, examples, case studies, and code snippets Every chapter starts with the objective and ends with a set of quiz questions to test the reader’s understanding Includes references to the related YouTube videos that provide additional guidance AI is a domain for everyone. This book is targeted toward everyone irrespective of their field of specialization. Graduates and researchers in deep learning will find this book useful.

Artificial Intelligence (AI)

Author : S. Kanimozhi Suguna
Publisher : CRC Press
ISBN 13 : 1000375528
Total Pages : 331 pages
Book Rating : 4.0/5 (3 download)

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Book Synopsis Artificial Intelligence (AI) by : S. Kanimozhi Suguna

Download or read book Artificial Intelligence (AI) written by S. Kanimozhi Suguna and published by CRC Press. This book was released on 2021-05-27 with total page 331 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book aims to bring together leading academic scientists, researchers, and research scholars to exchange and share their experiences and research results on all aspects of Artificial Intelligence. The book provides a premier interdisciplinary platform to present practical challenges and adopted solutions. The book addresses the complete functional framework workflow in Artificial Intelligence technology. It explores the basic and high-level concepts and can serve as a manual for the industry for beginners and the more advanced. It covers intelligent and automated systems and its implications to the real-world, and offers data acquisition and case studies related to data-intensive technologies in AI-based applications. The book will be of interest to researchers, professionals, scientists, professors, students of computer science engineering, electronics and communications, as well as information technology.

Robust Deep Learning for Computer Vision to Counteract Data Scarcity and Label Noise

Author : Duc Tam Nguyen
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (123 download)

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Book Synopsis Robust Deep Learning for Computer Vision to Counteract Data Scarcity and Label Noise by : Duc Tam Nguyen

Download or read book Robust Deep Learning for Computer Vision to Counteract Data Scarcity and Label Noise written by Duc Tam Nguyen and published by . This book was released on 2020* with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Interpretability in Deep Learning

Author : Ayush Somani
Publisher : Springer Nature
ISBN 13 : 3031206398
Total Pages : 483 pages
Book Rating : 4.0/5 (312 download)

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Book Synopsis Interpretability in Deep Learning by : Ayush Somani

Download or read book Interpretability in Deep Learning written by Ayush Somani and published by Springer Nature. This book was released on 2023-06-01 with total page 483 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a comprehensive curation, exposition and illustrative discussion of recent research tools for interpretability of deep learning models, with a focus on neural network architectures. In addition, it includes several case studies from application-oriented articles in the fields of computer vision, optics and machine learning related topic. The book can be used as a monograph on interpretability in deep learning covering the most recent topics as well as a textbook for graduate students. Scientists with research, development and application responsibilities benefit from its systematic exposition.

An Introduction to Variational Autoencoders

Author : Diederik P. Kingma
Publisher :
ISBN 13 : 9781680836226
Total Pages : 102 pages
Book Rating : 4.8/5 (362 download)

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Book Synopsis An Introduction to Variational Autoencoders by : Diederik P. Kingma

Download or read book An Introduction to Variational Autoencoders written by Diederik P. Kingma and published by . This book was released on 2019-11-12 with total page 102 pages. Available in PDF, EPUB and Kindle. Book excerpt: An Introduction to Variational Autoencoders provides a quick summary for the of a topic that has become an important tool in modern-day deep learning techniques.

Large-scale Kernel Machines

Author : Léon Bottou
Publisher : MIT Press
ISBN 13 : 0262026252
Total Pages : 409 pages
Book Rating : 4.2/5 (62 download)

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Book Synopsis Large-scale Kernel Machines by : Léon Bottou

Download or read book Large-scale Kernel Machines written by Léon Bottou and published by MIT Press. This book was released on 2007 with total page 409 pages. Available in PDF, EPUB and Kindle. Book excerpt: Solutions for learning from large scale datasets, including kernel learning algorithms that scale linearly with the volume of the data and experiments carried out on realistically large datasets. Pervasive and networked computers have dramatically reduced the cost of collecting and distributing large datasets. In this context, machine learning algorithms that scale poorly could simply become irrelevant. We need learning algorithms that scale linearly with the volume of the data while maintaining enough statistical efficiency to outperform algorithms that simply process a random subset of the data. This volume offers researchers and engineers practical solutions for learning from large scale datasets, with detailed descriptions of algorithms and experiments carried out on realistically large datasets. At the same time it offers researchers information that can address the relative lack of theoretical grounding for many useful algorithms. After a detailed description of state-of-the-art support vector machine technology, an introduction of the essential concepts discussed in the volume, and a comparison of primal and dual optimization techniques, the book progresses from well-understood techniques to more novel and controversial approaches. Many contributors have made their code and data available online for further experimentation. Topics covered include fast implementations of known algorithms, approximations that are amenable to theoretical guarantees, and algorithms that perform well in practice but are difficult to analyze theoretically. Contributors Léon Bottou, Yoshua Bengio, Stéphane Canu, Eric Cosatto, Olivier Chapelle, Ronan Collobert, Dennis DeCoste, Ramani Duraiswami, Igor Durdanovic, Hans-Peter Graf, Arthur Gretton, Patrick Haffner, Stefanie Jegelka, Stephan Kanthak, S. Sathiya Keerthi, Yann LeCun, Chih-Jen Lin, Gaëlle Loosli, Joaquin Quiñonero-Candela, Carl Edward Rasmussen, Gunnar Rätsch, Vikas Chandrakant Raykar, Konrad Rieck, Vikas Sindhwani, Fabian Sinz, Sören Sonnenburg, Jason Weston, Christopher K. I. Williams, Elad Yom-Tov

Deep Learning For Physics Research

Author : Martin Erdmann
Publisher : World Scientific
ISBN 13 : 9811237476
Total Pages : 340 pages
Book Rating : 4.8/5 (112 download)

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Book Synopsis Deep Learning For Physics Research by : Martin Erdmann

Download or read book Deep Learning For Physics Research written by Martin Erdmann and published by World Scientific. This book was released on 2021-06-25 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: A core principle of physics is knowledge gained from data. Thus, deep learning has instantly entered physics and may become a new paradigm in basic and applied research.This textbook addresses physics students and physicists who want to understand what deep learning actually means, and what is the potential for their own scientific projects. Being familiar with linear algebra and parameter optimization is sufficient to jump-start deep learning. Adopting a pragmatic approach, basic and advanced applications in physics research are described. Also offered are simple hands-on exercises for implementing deep networks for which python code and training data can be downloaded.