Extensions And Applications Of Deep Probabilistic Inference For Generative Models

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Extensions and Applications of Deep Probabilistic Inference for Generative Models

Author : Mike Wu
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (132 download)

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Book Synopsis Extensions and Applications of Deep Probabilistic Inference for Generative Models by : Mike Wu

Download or read book Extensions and Applications of Deep Probabilistic Inference for Generative Models written by Mike Wu and published by . This book was released on 2022 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Despite the growth of data size, many applications for which we would like to apply learning algorithms to are limited by data quantity and quality. Generative models propose a framework to naturally combine prior beliefs with real world data. Core to the generative approach is the challenge of probabilistic inference, or estimating latent variables given observations. This challenge has led to a rich field of research spanning many statistical techniques. More recently, deep learning methods have been used to solve inference queries, aptly named deep inference. In my dissertation I will explore extensions to deep inference in response to real world challenges of sparsity and efficiency. I will present case studies of practical applications where deep inference achieves considerable improvements upon prior work. This dissertation is centered around three parts. We present the background for generative models and deep inference with an emphasis on modern variational methods. The first part will present new algorithms for generalizing inference to be robust to different notions of sparsity, such as multimodal data, missing data, or computational constraints. Second, we study meta-amortized inference, or "inferring how to infer". A doubly-amortized inference algorithm would be cheaply able to solve inference queries for a novel generative model. We will show a new algorithm to re-purpose masked language modeling to do just this. Third, we present two real-world applications of deep inference in education: (a) estimating student abilities under item response theory and related psychometric models, and (b) inferring educational feedback for students learning to solve programming questions. Together, these contributions showcase the richness and utility of deep inference in education, and more broadly in real world contexts.

Variational Methods for Machine Learning with Applications to Deep Networks

Author : Lucas Pinheiro Cinelli
Publisher : Springer Nature
ISBN 13 : 3030706796
Total Pages : 173 pages
Book Rating : 4.0/5 (37 download)

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Book Synopsis Variational Methods for Machine Learning with Applications to Deep Networks by : Lucas Pinheiro Cinelli

Download or read book Variational Methods for Machine Learning with Applications to Deep Networks written by Lucas Pinheiro Cinelli and published by Springer Nature. This book was released on 2021-05-10 with total page 173 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Methods and Applications of Variational Inference Via Deep Generative Models

Author : Yu Wang
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (139 download)

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Book Synopsis Methods and Applications of Variational Inference Via Deep Generative Models by : Yu Wang

Download or read book Methods and Applications of Variational Inference Via Deep Generative Models written by Yu Wang and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

An Introduction to Lifted Probabilistic Inference

Author : Guy Van den Broeck
Publisher : MIT Press
ISBN 13 : 0262542595
Total Pages : 455 pages
Book Rating : 4.2/5 (625 download)

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Book Synopsis An Introduction to Lifted Probabilistic Inference by : Guy Van den Broeck

Download or read book An Introduction to Lifted Probabilistic Inference written by Guy Van den Broeck and published by MIT Press. This book was released on 2021-08-17 with total page 455 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent advances in the area of lifted inference, which exploits the structure inherent in relational probabilistic models. Statistical relational AI (StaRAI) studies the integration of reasoning under uncertainty with reasoning about individuals and relations. The representations used are often called relational probabilistic models. Lifted inference is about how to exploit the structure inherent in relational probabilistic models, either in the way they are expressed or by extracting structure from observations. This book covers recent significant advances in the area of lifted inference, providing a unifying introduction to this very active field. After providing necessary background on probabilistic graphical models, relational probabilistic models, and learning inside these models, the book turns to lifted inference, first covering exact inference and then approximate inference. In addition, the book considers the theory of liftability and acting in relational domains, which allows the connection of learning and reasoning in relational domains.

Log-Linear Models, Extensions, and Applications

Author : Aleksandr Aravkin
Publisher : MIT Press
ISBN 13 : 0262351617
Total Pages : 215 pages
Book Rating : 4.2/5 (623 download)

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Book Synopsis Log-Linear Models, Extensions, and Applications by : Aleksandr Aravkin

Download or read book Log-Linear Models, Extensions, and Applications written by Aleksandr Aravkin and published by MIT Press. This book was released on 2018-12-25 with total page 215 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in training models with log-linear structures, with topics including variable selection, the geometry of neural nets, and applications. Log-linear models play a key role in modern big data and machine learning applications. From simple binary classification models through partition functions, conditional random fields, and neural nets, log-linear structure is closely related to performance in certain applications and influences fitting techniques used to train models. This volume covers recent advances in training models with log-linear structures, covering the underlying geometry, optimization techniques, and multiple applications. The first chapter shows readers the inner workings of machine learning, providing insights into the geometry of log-linear and neural net models. The other chapters range from introductory material to optimization techniques to involved use cases. The book, which grew out of a NIPS workshop, is suitable for graduate students doing research in machine learning, in particular deep learning, variable selection, and applications to speech recognition. The contributors come from academia and industry, allowing readers to view the field from both perspectives. Contributors Aleksandr Aravkin, Avishy Carmi, Guillermo A. Cecchi, Anna Choromanska, Li Deng, Xinwei Deng, Jean Honorio, Tony Jebara, Huijing Jiang, Dimitri Kanevsky, Brian Kingsbury, Fabrice Lambert, Aurélie C. Lozano, Daniel Moskovich, Yuriy S. Polyakov, Bhuvana Ramabhadran, Irina Rish, Dimitris Samaras, Tara N. Sainath, Hagen Soltau, Serge F. Timashev, Ewout van den Berg

Compression, Generation, and Inference Via Supervised Learning

Author : Jiaming Song
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (126 download)

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Book Synopsis Compression, Generation, and Inference Via Supervised Learning by : Jiaming Song

Download or read book Compression, Generation, and Inference Via Supervised Learning written by Jiaming Song and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence and machine learning methods have seen tremendous advances in the past decade, thanks to deep neural networks. Supervised learning methods enables neural networks to effectively approximate low-level functions of human intelligence, such as identifying an object within an image. However, many complex functions of human intelligence are difficult to solve with supervised learning directly: humans can build concise representations of the world (compression), generate works of art based on creative imaginations (generation), and infer how others will act from personal experiences (inference). In this dissertation, we focus on machine learning approaches that reduce these complex functions of human intelligence into simpler ones that can be readily solved with supervised learning and thus enabling us to leverage the developments in deep learning. This dissertation comprises of three parts, namely compression, generation, and inference. The first part discusses how we can apply supervised learning to unsupervised representation learning. We develop algorithms that can learn informative representations from large unlabeled datasets while protecting certain sensitive attributes. The second part extends these ideas to learning high-dimensional probabilistic models of unlabeled data. Combined with the insights from the first part, we introduce a generative model suitable for conditional generation under limited supervision. In the third and final part, we present two applications of supervised learning in probabilistic inference methods: (a) optimizing for efficient Bayesian inference algorithms and (b) inferring the agents' intent under complex, multi-agent environments. These contributions enable machines to overcome existing limitations of supervised learning in real-world compression, generation, and inference problems.

Emerging Biometrics: Deep Inference and Other Computational Intelligence

Author : S. Yanushkevich
Publisher : Infinite Study
ISBN 13 :
Total Pages : 16 pages
Book Rating : 4./5 ( download)

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Book Synopsis Emerging Biometrics: Deep Inference and Other Computational Intelligence by : S. Yanushkevich

Download or read book Emerging Biometrics: Deep Inference and Other Computational Intelligence written by S. Yanushkevich and published by Infinite Study. This book was released on with total page 16 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper aims at identifying emerging computational intelligence trends for the design and modeling of complex biometric-enabled infrastructure and systems. Biometric-enabled systems are evolving towards deep learning and deep inference using the principles of adaptive computing, – the front tides of the modern computational intelligence domain.

ECAI 2020

Author : G. De Giacomo
Publisher : IOS Press
ISBN 13 : 164368101X
Total Pages : 3122 pages
Book Rating : 4.6/5 (436 download)

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Book Synopsis ECAI 2020 by : G. De Giacomo

Download or read book ECAI 2020 written by G. De Giacomo and published by IOS Press. This book was released on 2020-09-11 with total page 3122 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the proceedings of the 24th European Conference on Artificial Intelligence (ECAI 2020), held in Santiago de Compostela, Spain, from 29 August to 8 September 2020. The conference was postponed from June, and much of it conducted online due to the COVID-19 restrictions. The conference is one of the principal occasions for researchers and practitioners of AI to meet and discuss the latest trends and challenges in all fields of AI and to demonstrate innovative applications and uses of advanced AI technology. The book also includes the proceedings of the 10th Conference on Prestigious Applications of Artificial Intelligence (PAIS 2020) held at the same time. A record number of more than 1,700 submissions was received for ECAI 2020, of which 1,443 were reviewed. Of these, 361 full-papers and 36 highlight papers were accepted (an acceptance rate of 25% for full-papers and 45% for highlight papers). The book is divided into three sections: ECAI full papers; ECAI highlight papers; and PAIS papers. The topics of these papers cover all aspects of AI, including Agent-based and Multi-agent Systems; Computational Intelligence; Constraints and Satisfiability; Games and Virtual Environments; Heuristic Search; Human Aspects in AI; Information Retrieval and Filtering; Knowledge Representation and Reasoning; Machine Learning; Multidisciplinary Topics and Applications; Natural Language Processing; Planning and Scheduling; Robotics; Safe, Explainable, and Trustworthy AI; Semantic Technologies; Uncertainty in AI; and Vision. The book will be of interest to all those whose work involves the use of AI technology.

Proceedings of 4th International Conference on Frontiers in Computing and Systems

Author : Dipak Kumar Kole
Publisher : Springer Nature
ISBN 13 : 9819726115
Total Pages : 728 pages
Book Rating : 4.8/5 (197 download)

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Book Synopsis Proceedings of 4th International Conference on Frontiers in Computing and Systems by : Dipak Kumar Kole

Download or read book Proceedings of 4th International Conference on Frontiers in Computing and Systems written by Dipak Kumar Kole and published by Springer Nature. This book was released on with total page 728 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Scaling Up Probabilistic Circuits for Inference Demanding Applications

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

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Book Synopsis Scaling Up Probabilistic Circuits for Inference Demanding Applications by : Meihua Dang

Download or read book Scaling Up Probabilistic Circuits for Inference Demanding Applications written by Meihua Dang and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: There is a trade-off between expressiveness and tractability in generative modeling. On the one hand, while neural-based deep generative models are extremely expressive, the ways we can query them are limited; on the other hand, while tractable probabilistic models support efficient computation of various probabilistic queries, scaling them up is a major challenge. Probabilistic circuits are a tractable representation of probability distributions allowing for exact and efficient computation of likelihoods and marginals. We study the task of scaling up the learning of probabilistic circuits and then applying them to various applications. On the learning front, we propose a new algorithm for learning the sparse structures of probabilistic circuits that can significantly improve their capacity. On the application front, we further demonstrate the expressiveness and tractability of probabilistic circuits in two downstream applications: genetic sequence modeling and controllable language generation.

MACHINE LEARNING MASTERY: ALGORITHMS, APPLICATIONS AND INSIGHTS

Author : Dr. Pramod Kumar
Publisher : Xoffencerpublication
ISBN 13 : 811953414X
Total Pages : 245 pages
Book Rating : 4.1/5 (195 download)

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Book Synopsis MACHINE LEARNING MASTERY: ALGORITHMS, APPLICATIONS AND INSIGHTS by : Dr. Pramod Kumar

Download or read book MACHINE LEARNING MASTERY: ALGORITHMS, APPLICATIONS AND INSIGHTS written by Dr. Pramod Kumar and published by Xoffencerpublication. This book was released on 2023-08-14 with total page 245 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning is an area of artificial intelligence (AI) that focuses on the development of algorithms and models that allow computers to learn and make predictions or judgments without being explicitly programmed. This is accomplished by teaching the computer to learn from its own experiences. The creation and development of computer systems that are able to automatically analyze and understand complicated data in order to enhance their performance over time is the focus of this field. The foundation of machine learning is the construction of mathematical models that are capable of gaining knowledge from data. These models are educated using a collection of instances that have been labeled. This collection of examples is referred to as the training data, and it includes input features as well as output labels or goal values. Adjusting the model's internal parameters or weights in accordance with the patterns and relationships discovered in the data is what the training process entails. This is done with the intention of achieving a gap that is as narrow as possible between the anticipated outputs and the actual values. Reinforcement learning is a paradigm that entails an agent interacting with an environment and learning to make a series of choices or actions in order to maximize a cumulative reward. This paradigm was developed by Edward de Bono. The agent is provided with feedback in the form of incentives or penalties according to its actions, which teaches it the optimum behavior via the process of trial and error. The methodologies of machine learning are becoming more prevalent in a broad variety of fields and applications. Image and audio recognition, natural language processing, recommendation systems, fraud detection, autonomous cars, and medical diagnostics are just few of the numerous applications that may benefit from AI. Programming languages such as Python and R, in addition to libraries and frameworks such as scikit-learn, TensorFlow, and PyTorch, are often used when it comes to the implementation of machine learning algorithms. These tools offer a comprehensive array of functions and utilities for the preparation of data, as well as for the training, assessment, and deployment of models. Learning via machines is an active topic that is developing at a quick pace because to continuing research and technological breakthroughs. The potential for employing machine learning to tackle difficult issues and promote innovation is continuing to develop as more data becomes accessible and as computer power grows.

Learning Deep Generative Models

Author : Ruslan Salakhutdinov
Publisher :
ISBN 13 : 9780494610800
Total Pages : 196 pages
Book Rating : 4.6/5 (18 download)

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Book Synopsis Learning Deep Generative Models by : Ruslan Salakhutdinov

Download or read book Learning Deep Generative Models written by Ruslan Salakhutdinov and published by . This book was released on 2009 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: Building intelligent systems that are capable of extracting high-level representations from high-dimensional sensory data lies at the core of solving many AI related tasks, including object recognition, speech perception, and language understanding. Theoretical and biological arguments strongly suggest that building such systems requires models with deep architectures that involve many layers of nonlinear processing. The aim of the thesis is to demonstrate that deep generative models that contain many layers of latent variables and millions of parameters can be learned efficiently, and that the learned high-level feature representations can be successfully applied in a wide spectrum of application domains, including visual object recognition, information retrieval, and classification and regression tasks. In addition, similar methods can be used for nonlinear dimensionality reduction. The first part of the thesis focuses on analysis and applications of probabilistic generative models called Deep Belief Networks. We show that these deep hierarchical models can learn useful feature representations from a large supply of unlabeled sensory inputs. The learned high-level representations capture a lot of structure in the input data, which is useful for subsequent problem-specific tasks, such as classification, regression or information retrieval, even though these tasks are unknown when the generative model is being trained. In the second part of the thesis, we introduce a new learning algorithm for a different type of hierarchical probabilistic model, which we call a Deep Boltzmann Machine. Like Deep Belief Networks, Deep Boltzmann Machines have the potential of learning internal representations that become increasingly complex at higher layers, which is a promising way of solving object and speech recognition problems. Unlike Deep Belief Networks and many existing models with deep architectures, the approximate inference procedure, in addition to a fast bottom-up pass, can incorporate top-down feedback. This allows Deep Boltzmann Machines to better propagate uncertainty about ambiguous inputs.

Gen

Author : Marco Francis Cusumano-Towner
Publisher :
ISBN 13 :
Total Pages : 231 pages
Book Rating : 4.:/5 (122 download)

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Book Synopsis Gen by : Marco Francis Cusumano-Towner

Download or read book Gen written by Marco Francis Cusumano-Towner and published by . This book was released on 2020 with total page 231 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probabilistic inference provides a powerful theoretical framework for engineering intelligent systems. However, diverse modeling approaches and inference algorithms are needed to navigate engineering tradeoffs between robustness, adaptability, accuracy, safety, interpretability, data efficiency, and computational efficiency. Structured generative models represented as symbolic programs provide interpretability. Structure learning of these models provides data-efficient adaptability. Uncertainty quantification is needed for safety. Bottom-up, discriminative inference provides computational efficiency. Iterative “model-in-the-loop” algorithms can improve accuracy by fine-tuning inferences and improve robustness to out-of- distribution data. Recent probabilistic programming systems fully or partially automate inference, but are too restrictive for many applications. Differentiable programming systems are also inadequate: they do not support structure learning of generative models or hybrids of “model-in-the-loop” and discriminative inference. Therefore, probabilistic inference is still often implemented by translating tedious mathematical derivations into low-level numerical programs, which are error-prone and difficult to modify and maintain. This thesis presents the design and implementation of the Gen programming platform for probabilistic inference. Gen automates the low-level implementation of probabilistic inference algorithms while remaining flexible enough to support heterogeneous algorithmic approaches and extensible enough for practical inference engineering. Gen users define their models explicitly using probabilistic programs, but instead of compiling the model directly into an inference algorithm implementation, Gen compiles the model into data types that encapsulate low-level inference operations whose semantics are derived from the model, like sampling, density evaluation, and gradients. Users write their inference application in a general-purpose programming language using Gen’s abstract data types as primitives. This thesis defines Gen’s data types and shows that they can be used to compose a variety of inference techniques including sophisticated Monte Carlo algorithms and hybrids of Monte Carlo, variational, and discriminative techniques. The same data types can be generated from multiple probabilistic programming languages that strike different expressiveness and performance tradeoffs. By decoupling probabilistic programming language implementations from inference algorithm design, Gen enables more flexible specialization of both, leading to performance improvements over existing probabilistic programming systems.

Scalable and Reliable Inference for Probabilistic Modeling

Author : Ruqi Zhang
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (14 download)

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Book Synopsis Scalable and Reliable Inference for Probabilistic Modeling by : Ruqi Zhang

Download or read book Scalable and Reliable Inference for Probabilistic Modeling written by Ruqi Zhang and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Deep Learning

Author : Ian Goodfellow
Publisher : MIT Press
ISBN 13 : 0262337371
Total Pages : 801 pages
Book Rating : 4.2/5 (623 download)

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Book Synopsis Deep Learning by : Ian Goodfellow

Download or read book Deep Learning written by Ian Goodfellow and published by MIT Press. This book was released on 2016-11-10 with total page 801 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

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.

The Cambridge Handbook of Computational Cognitive Sciences

Author : Ron Sun
Publisher : Cambridge University Press
ISBN 13 : 1108617433
Total Pages : 1804 pages
Book Rating : 4.1/5 (86 download)

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Book Synopsis The Cambridge Handbook of Computational Cognitive Sciences by : Ron Sun

Download or read book The Cambridge Handbook of Computational Cognitive Sciences written by Ron Sun and published by Cambridge University Press. This book was released on 2023-04-30 with total page 1804 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Cambridge Handbook of Computational Cognitive Sciences is a comprehensive reference for this rapidly developing and highly interdisciplinary field. Written with both newcomers and experts in mind, it provides an accessible introduction of paradigms, methodologies, approaches, and models, with ample detail and illustrated by examples. It should appeal to researchers and students working within the computational cognitive sciences, as well as those working in adjacent fields including philosophy, psychology, linguistics, anthropology, education, neuroscience, artificial intelligence, computer science, and more.