Scalable Approximate Inference Methods for Bayesian Deep Learning PDF Download
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Author: Julian Hippolyt Ritter
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Author: Julian Hippolyt Ritter
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Author: Matt Benatan
Publisher: Packt Publishing Ltd
ISBN: 1803237252
Category : Computers
Languages : en
Pages : 386
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Book Description
Develop Bayesian Deep Learning models to help make your own applications more robust. Key Features Gain insights into the limitations of typical neural networks Acquire the skill to cultivate neural networks capable of estimating uncertainty Discover how to leverage uncertainty to develop more robust machine learning systems Book Description Deep learning has an increasingly significant impact on our lives, from suggesting content to playing a key role in mission- and safety-critical applications. As the influence of these algorithms grows, so does the concern for the safety and robustness of the systems which rely on them. Simply put, typical deep learning methods do not know when they don't know. The field of Bayesian Deep Learning contains a range of methods for approximate Bayesian inference with deep networks. These methods help to improve the robustness of deep learning systems as they tell us how confident they are in their predictions, allowing us to take more care in how we incorporate model predictions within our applications. Through this book, you will be introduced to the rapidly growing field of uncertainty-aware deep learning, developing an understanding of the importance of uncertainty estimation in robust machine learning systems. You will learn about a variety of popular Bayesian Deep Learning methods, and how to implement these through practical Python examples covering a range of application scenarios. By the end of the book, you will have a good understanding of Bayesian Deep Learning and its advantages, and you will be able to develop Bayesian Deep Learning models for safer, more robust deep learning systems. What you will learn Understand advantages and disadvantages of Bayesian inference and deep learning Understand the fundamentals of Bayesian Neural Networks Understand the differences between key BNN implementations/approximations Understand the advantages of probabilistic DNNs in production contexts How to implement a variety of BDL methods in Python code How to apply BDL methods to real-world problems Understand how to evaluate BDL methods and choose the best method for a given task Learn how to deal with unexpected data in real-world deep learning applications Who this book is for This book will cater to researchers and developers looking for ways to develop more robust deep learning models through probabilistic deep learning. You're expected to have a solid understanding of the fundamentals of machine learning and probability, along with prior experience working with machine learning and deep learning models.
Author: Christian Andersson Naesseth
Publisher: Linköping University Electronic Press
ISBN: 9176851613
Category :
Languages : en
Pages : 39
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Book Description
Automatic decision making and pattern recognition under uncertainty are difficult tasks that are ubiquitous in our everyday life. The systems we design, and technology we develop, requires us to coherently represent and work with uncertainty in data. Probabilistic models and probabilistic inference gives us a powerful framework for solving this problem. Using this framework, while enticing, results in difficult-to-compute integrals and probabilities when conditioning on the observed data. This means we have a need for approximate inference, methods that solves the problem approximately using a systematic approach. In this thesis we develop new methods for efficient approximate inference in probabilistic models. There are generally two approaches to approximate inference, variational methods and Monte Carlo methods. In Monte Carlo methods we use a large number of random samples to approximate the integral of interest. With variational methods, on the other hand, we turn the integration problem into that of an optimization problem. We develop algorithms of both types and bridge the gap between them. First, we present a self-contained tutorial to the popular sequential Monte Carlo (SMC) class of methods. Next, we propose new algorithms and applications based on SMC for approximate inference in probabilistic graphical models. We derive nested sequential Monte Carlo, a new algorithm particularly well suited for inference in a large class of high-dimensional probabilistic models. Then, inspired by similar ideas we derive interacting particle Markov chain Monte Carlo to make use of parallelization to speed up approximate inference for universal probabilistic programming languages. After that, we show how we can make use of the rejection sampling process when generating gamma distributed random variables to speed up variational inference. Finally, we bridge the gap between SMC and variational methods by developing variational sequential Monte Carlo, a new flexible family of variational approximations.
Author: Elaine Angelino
Publisher:
ISBN: 9781680832198
Category : Bayesian statistical decision theory
Languages : en
Pages : 128
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Book Description
Datasets are growing not just in size but in complexity, creating a demand for rich models and quantification of uncertainty. Bayesian methods are an excellent fit for this demand, but scaling Bayesian inference is a challenge. In response to this challenge, there has been considerable recent work based on varying assumptions about model structure, underlying computational resources, and the importance of asymptotic correctness. As a result, there is a zoo of ideas with a wide range of assumptions and applicability. In this paper, we seek to identify unifying principles, patterns, and intuitions for scaling Bayesian inference. We review existing work on utilizing modern computing resources with both MCMC and variational approximation techniques. From this taxonomy of ideas, we characterize the general principles that have proven successful for designing scalable inference procedures and comment on the path forward.
Author: Jonathan Hunter Huggins
Publisher:
ISBN:
Category :
Languages : en
Pages : 140
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Book Description
Bayesian statistical modeling and inference allow scientists, engineers, and companies to learn from data while incorporating prior knowledge, sharing power across experiments via hierarchical models, quantifying their uncertainty about what they have learned, and making predictions about an uncertain future. While Bayesian inference is conceptually straightforward, in practice calculating expectations with respect to the posterior can rarely be done in closed form. Hence, users of Bayesian models must turn to approximate inference methods. But modern statistical applications create many challenges: the latent parameter is often high-dimensional, the models can be complex, and there are large amounts of data that may only be available as a stream or distributed across many computers. Existing algorithm have so far remained unsatisfactory because they either (1) fail to scale to large data sets, (2) provide limited approximation quality, or (3) fail to provide guarantees on the quality of inference. To simultaneously overcome these three possible limitations, I leverage the critical insight that in the large-scale setting, much of the data is redundant. Therefore, it is possible to compress data into a form that admits more efficient inference. I develop two approaches to compressing data for improved scalability. The first is to construct a coreset: a small, weighted subset of our data that is representative of the complete dataset. The second, which I call PASS-GLM, is to construct an exponential family model that approximates the original model. The data is compressed by calculating the finite-dimensional sufficient statistics of the data under the exponential family. An advantage of the compression approach to approximate inference is that an approximate likelihood substitutes for the original likelihood. I show how such approximate likelihoods lend them themselves to a priori analysis and develop general tools for proving when an approximate likelihood will lead to a high-quality approximate posterior. I apply these tools to obtain a priori guarantees on the approximate posteriors produced by PASS-GLM. Finally, for cases when users must rely on algorithms that do not have a priori accuracy guarantees, I develop a method for comparing the quality of the inferences produced by competing algorithms. The method comes equipped with provable guarantees while also being computationally efficient.
Author: Elaine Angelino
Publisher:
ISBN: 9781680832181
Category : Computers
Languages : en
Pages : 148
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Book Description
Identifies unifying principles, patterns, and intuitions for scaling Bayesian inference. Reviews existing work on utilizing modern computing resources with both MCMC and variational approximation techniques. From this taxonomy of ideas, it characterizes the general principles that have proven successful for designing scalable inference procedures.
Author: Lucas Pinheiro Cinelli
Publisher: Springer Nature
ISBN: 3030706796
Category : Technology & Engineering
Languages : en
Pages : 173
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Book Description
This book provides a straightforward look at the concepts, algorithms and advantages of Bayesian Deep Learning and Deep Generative Models. Starting from the model-based approach to Machine Learning, the authors motivate Probabilistic Graphical Models and show how Bayesian inference naturally lends itself to this framework. The authors present detailed explanations of the main modern algorithms on variational approximations for Bayesian inference in neural networks. Each algorithm of this selected set develops a distinct aspect of the theory. The book builds from the ground-up well-known deep generative models, such as Variational Autoencoder and subsequent theoretical developments. By also exposing the main issues of the algorithms together with different methods to mitigate such issues, the book supplies the necessary knowledge on generative models for the reader to handle a wide range of data types: sequential or not, continuous or not, labelled or not. The book is self-contained, promptly covering all necessary theory so that the reader does not have to search for additional information elsewhere. Offers a concise self-contained resource, covering the basic concepts to the algorithms for Bayesian Deep Learning; Presents Statistical Inference concepts, offering a set of elucidative examples, practical aspects, and pseudo-codes; Every chapter includes hands-on examples and exercises and a website features lecture slides, additional examples, and other support material.
Author: Martin J. Wainwright
Publisher: Now Publishers Inc
ISBN: 1601981848
Category : Computers
Languages : en
Pages : 324
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Book Description
The core of this paper is a general set of variational principles for the problems of computing marginal probabilities and modes, applicable to multivariate statistical models in the exponential family.
Author: Ruqi Zhang
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Probabilistic modeling, as known as probabilistic machine learning, provides a principled framework for learning from data, with the key advantage of offering rigorous solutions for uncertainty quantification. In the era of big and complex data, there is an urgent need for new inference methods in probabilistic modeling to extract information from data effectively and efficiently. This thesis shows how to do theoretically-guaranteed scalable and reliable inference for modern machine learning. Considering both theory and practice, we provide foundational understanding of scalable and reliable inference methods and practical algorithms of new inference methods, as well as extensive empirical evaluation on common machine learning and deep learning tasks. Classical inference algorithms, such as Markov chain Monte Carlo, have enabled probabilistic modeling to achieve gold standard results on many machine learning tasks. However, these algorithms are rarely used in modern machine learning due to the difficulty of scaling up to large datasets. Existing work suggests that there is an inherent trade-off between scalability and reliability, forcing practitioners to choose between expensive exact methods and biased scalable ones. To overcome the current trade-off, we introduce general and theoretically grounded frameworks to enable fast and asymptotically correct inference, with applications to Gibbs sampling, Metropolis-Hastings and Langevin dynamics. Deep neural networks (DNNs) have achieved impressive success on a variety of learning problems in recent years. However, DNNs have been criticized for being unable to estimate uncertainty accurately. Probabilistic modeling provides a principled alternative that can mitigate this issue; they are able to account for model uncertainty and achieve automatic complexity control. In this thesis, we analyze the key challenges of probabilistic inference in deep learning, and present novel approaches for fast posterior inference of neural network weights.
Author: Ninon Burgos
Publisher: Academic Press
ISBN: 0128243503
Category : Computers
Languages : en
Pages : 676
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Book Description
Biomedical Image Synthesis and Simulation: Methods and Applications presents the basic concepts and applications in image-based simulation and synthesis used in medical and biomedical imaging. The first part of the book introduces and describes the simulation and synthesis methods that were developed and successfully used within the last twenty years, from parametric to deep generative models. The second part gives examples of successful applications of these methods. Both parts together form a book that gives the reader insight into the technical background of image synthesis and how it is used, in the particular disciplines of medical and biomedical imaging. The book ends with several perspectives on the best practices to adopt when validating image synthesis approaches, the crucial role that uncertainty quantification plays in medical image synthesis, and research directions that should be worth exploring in the future. - Gives state-of-the-art methods in (bio)medical image synthesis - Explains the principles (background) of image synthesis methods - Presents the main applications of biomedical image synthesis methods
