Author: Sharon Zhou
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Evaluation drives and tracks progress in every field. Metrics of evaluation are designed to assess important criteria in an area, and aid us in understanding the quantitative differences between one breakthrough and another. In machine learning, evaluation metrics have historically acted as north stars towards which researchers have optimized and organized their methods and findings. While evaluation metrics have been straightforward to construct and implement in some subfields of machine learning, they have been notoriously difficult to design in generative models. Several reasons emerge to explain this: (1) there are no gold standard outputs to compare against, unlike held-out test sets, (2) because of their diverse training methods and formulations, inherent model properties are difficult to measure consistently, and sampled outputs are often used for evaluation instead, (3) dependence on external (pretrained) models that add another layer of bias and uncertainty, and (4) inconsistent results without a large number of samples. As a result, generative models have suffered from noisy assessments that occupy a changing evaluation landscape, in contrast to the relative stability of their discriminative counterparts. In this manuscript, we examine several important criteria for generative models and introduce evaluation metrics to address each one while discussing the aforementioned issues in generative model evaluation. In particular, we examine the challenge of measuring the perceptual realism of generated outputs and introduce a human-in-the-loop evaluation system that leverages psychophysics theory to ground the method in human perception literature and crowdsourcing techniques to construct an efficient, reliable, and consistent method for comparing different models. In addition to this, we analyze disentanglement, an increasingly important property for assessing learned representations, by measuring an intrinsic property of a generative model's data manifold using persistent homology. The final work in this manuscript takes a step towards assessing a generative model and its different modes with a key application in mind, specifically the stylistic fidelity across different generated modes in a multimodal setting.
Author: Sandy Engelhardt
Publisher: Springer Nature
ISBN: 3030882101
Category : Computers
Languages : en
Pages : 278
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Book Description
This book constitutes the refereed proceedings of the First MICCAI Workshop on Deep Generative Models, DG4MICCAI 2021, and the First MICCAI Workshop on Data Augmentation, Labelling, and Imperfections, DALI 2021, held in conjunction with MICCAI 2021, in October 2021. The workshops were planned to take place in Strasbourg, France, but were held virtually due to the COVID-19 pandemic. DG4MICCAI 2021 accepted 12 papers from the 17 submissions received. The workshop focusses on recent algorithmic developments, new results, and promising future directions in Deep Generative Models. Deep generative models such as Generative Adversarial Network (GAN) and Variational Auto-Encoder (VAE) are currently receiving widespread attention from not only the computer vision and machine learning communities, but also in the MIC and CAI community. For DALI 2021, 15 papers from 32 submissions were accepted for publication. They focus on rigorous study of medical data related to machine learning systems.
Author: Jakub M. Tomczak
Publisher: Springer Nature
ISBN: 3030931587
Category : Computers
Languages : en
Pages : 210
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Book Description
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.
Author: Hazrat Ali
Publisher: Springer Nature
ISBN: 3031463412
Category : Technology & Engineering
Languages : en
Pages : 259
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Book Description
Generative Artificial Intelligence is rapidly advancing with many state-of-the-art performances on computer vision, speech processing, and natural language processing tasks. Generative adversarial networks and neural diffusion models can generate high-quality synthetic images of human faces, artworks, and coherent essays on different topics. Generative models are also transforming Medical Artificial Intelligence, given their potential to learn complex features from medical imaging and healthcare data. Hence, computer-aided diagnosis and healthcare are benefiting from Medical Artificial Intelligence and Generative Artificial Intelligence. This book presents the recent advances in generative models for Medical Artificial Intelligence. It covers many applications of generative models for medical image data, including volumetric medical image segmentation, data augmentation, MRI reconstruction, and modeling of spatiotemporal medical data. This book highlights the recent advancements in Generative Artificial Intelligence for medical and healthcare applications, using medical imaging and clinical and electronic health records data. Furthermore, the book comprehensively presents the concepts and applications of deep learning-based artificial intelligence methods, such as generative adversarial networks, convolutional neural networks, and vision transformers. It also presents a quantitative and qualitative analysis of data augmentation and synthesis performances of Generative Artificial Intelligence models. This book is the result of the collaborative efforts and hard work of many minds who contributed to it and illuminated the vast landscape of Medical Artificial Intelligence. The book is suitable for reading by computer science researchers, medical professionals, healthcare informatics, and medical imaging researchers interested in understanding the potential of artificial intelligence in healthcare. It serves as a compass for navigating the artificial intelligence-driven healthcare landscape.
Author: Zhifeng Kong
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
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.
Author: Anirban Mukhopadhyay
Publisher: Springer Nature
ISBN: 303153767X
Category :
Languages : en
Pages : 256
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Book Description
Author: Gisbert Schneider
Publisher: Wiley-VCH
ISBN: 9783527334612
Category : Medical
Languages : en
Pages : 0
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Book Description
Systematically examining current methods and strategies, this ready reference covers a wide range of molecular structures, from organic-chemical drugs to peptides, Proteins and nucleic acids, in line with emerging new drug classes derived from biomacromolecules. A leader in the field and one of the pioneers of this young discipline has assembled here the most prominent experts from across the world to provide first-hand knowledge. While most of their methods and examples come from the area of pharmaceutical discovery and development, the approaches are equally applicable for chemical probes and diagnostics, pesticides, and any other molecule designed to interact with a biological system. Numerous images and screenshots illustrate the many examples and method descriptions. With its broad and balanced coverage, this will be the firststop resource not only for medicinal chemists, biochemists and biotechnologists, but equally for bioinformaticians and molecular designers for many years to come. From the content: * Reaction-driven de novo design * Adaptive methods in molecular design * Design of ligands against multitarget profiles * Free energy methods in ligand design * Fragment-based de novo design * Automated design of focused and target family-oriented compound libraries * Molecular de novo design by nature-inspired computing * 3D QSAR approaches to de novo drug design * Bioisosteres in de novo design * De novo design of peptides, proteins and nucleic acid structures, including RNA aptamers and many more.
Author: Jakub M. Tomczak
Publisher: Springer Nature
ISBN: 303164087X
Category :
Languages : en
Pages : 325
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Book Description
Author: Herbert Edelsbrunner
Publisher: American Mathematical Society
ISBN: 1470467690
Category : Mathematics
Languages : en
Pages : 241
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Book Description
Combining concepts from topology and algorithms, this book delivers what its title promises: an introduction to the field of computational topology. Starting with motivating problems in both mathematics and computer science and building up from classic topics in geometric and algebraic topology, the third part of the text advances to persistent homology. This point of view is critically important in turning a mostly theoretical field of mathematics into one that is relevant to a multitude of disciplines in the sciences and engineering. The main approach is the discovery of topology through algorithms. The book is ideal for teaching a graduate or advanced undergraduate course in computational topology, as it develops all the background of both the mathematical and algorithmic aspects of the subject from first principles. Thus the text could serve equally well in a course taught in a mathematics department or computer science department.
Author: Jason Brownlee
Publisher: Machine Learning Mastery
ISBN:
Category : Computers
Languages : en
Pages : 655
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Book Description
Step-by-step tutorials on generative adversarial networks in python for image synthesis and image translation.
