Learning Structured Prediction Models
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Structured Learning And Prediction In Computer Vision
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Author : Sebastian Nowozin
language : en
Publisher: Now Publishers Inc
Release Date : 2011
Structured Learning And Prediction In Computer Vision written by Sebastian Nowozin and has been published by Now Publishers Inc this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with Computers categories.
Structured Learning and Prediction in Computer Vision introduces the reader to the most popular classes of structured models in computer vision.
Learning Structured Prediction Models
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Author : Ben Taskar
language : en
Publisher:
Release Date : 2004
Learning Structured Prediction Models written by Ben Taskar and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with categories.
Linguistic Structure Prediction
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Author : Noah A. Smith
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Linguistic Structure Prediction written by Noah A. Smith and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Computers categories.
A major part of natural language processing now depends on the use of text data to build linguistic analyzers. We consider statistical, computational approaches to modeling linguistic structure. We seek to unify across many approaches and many kinds of linguistic structures. Assuming a basic understanding of natural language processing and/or machine learning, we seek to bridge the gap between the two fields. Approaches to decoding (i.e., carrying out linguistic structure prediction) and supervised and unsupervised learning of models that predict discrete structures as outputs are the focus. We also survey natural language processing problems to which these methods are being applied, and we address related topics in probabilistic inference, optimization, and experimental methodology. Table of Contents: Representations and Linguistic Data / Decoding: Making Predictions / Learning Structure from Annotated Data / Learning Structure from Incomplete Data / Beyond Decoding: Inference
Advanced Structured Prediction
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Author : Sebastian Nowozin
language : en
Publisher: MIT Press
Release Date : 2014-12-05
Advanced Structured Prediction written by Sebastian Nowozin and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-12-05 with Computers categories.
An overview of recent work in the field of structured prediction, the building of predictive machine learning models for interrelated and dependent outputs. The goal of structured prediction is to build machine learning models that predict relational information that itself has structure, such as being composed of multiple interrelated parts. These models, which reflect prior knowledge, task-specific relations, and constraints, are used in fields including computer vision, speech recognition, natural language processing, and computational biology. They can carry out such tasks as predicting a natural language sentence, or segmenting an image into meaningful components. These models are expressive and powerful, but exact computation is often intractable. A broad research effort in recent years has aimed at designing structured prediction models and approximate inference and learning procedures that are computationally efficient. This volume offers an overview of this recent research in order to make the work accessible to a broader research community. The chapters, by leading researchers in the field, cover a range of topics, including research trends, the linear programming relaxation approach, innovations in probabilistic modeling, recent theoretical progress, and resource-aware learning. Contributors Jonas Behr, Yutian Chen, Fernando De La Torre, Justin Domke, Peter V. Gehler, Andrew E. Gelfand, Sébastien Giguère, Amir Globerson, Fred A. Hamprecht, Minh Hoai, Tommi Jaakkola, Jeremy Jancsary, Joseph Keshet, Marius Kloft, Vladimir Kolmogorov, Christoph H. Lampert, François Laviolette, Xinghua Lou, Mario Marchand, André F. T. Martins, Ofer Meshi, Sebastian Nowozin, George Papandreou, Daniel Průša, Gunnar Rätsch, Amélie Rolland, Bogdan Savchynskyy, Stefan Schmidt, Thomas Schoenemann, Gabriele Schweikert, Ben Taskar, Sinisa Todorovic, Max Welling, David Weiss, Thomáš Werner, Alan Yuille, Stanislav Živný
Interpretable Machine Learning
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Author : Christoph Molnar
language : en
Publisher: Lulu.com
Release Date : 2020
Interpretable Machine Learning written by Christoph Molnar and has been published by Lulu.com this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Computers categories.
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Introduction To Protein Structure Prediction
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Author : Huzefa Rangwala
language : en
Publisher: John Wiley & Sons
Release Date : 2011-03-16
Introduction To Protein Structure Prediction written by Huzefa Rangwala and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-03-16 with Science categories.
A look at the methods and algorithms used to predict protein structure A thorough knowledge of the function and structure of proteins is critical for the advancement of biology and the life sciences as well as the development of better drugs, higher-yield crops, and even synthetic bio-fuels. To that end, this reference sheds light on the methods used for protein structure prediction and reveals the key applications of modeled structures. This indispensable book covers the applications of modeled protein structures and unravels the relationship between pure sequence information and three-dimensional structure, which continues to be one of the greatest challenges in molecular biology. With this resource, readers will find an all-encompassing examination of the problems, methods, tools, servers, databases, and applications of protein structure prediction and they will acquire unique insight into the future applications of the modeled protein structures. The book begins with a thorough introduction to the protein structure prediction problem and is divided into four themes: a background on structure prediction, the prediction of structural elements, tertiary structure prediction, and functional insights. Within those four sections, the following topics are covered: Databases and resources that are commonly used for protein structure prediction The structure prediction flagship assessment (CASP) and the protein structure initiative (PSI) Definitions of recurring substructures and the computational approaches used for solving sequence problems Difficulties with contact map prediction and how sophisticated machine learning methods can solve those problems Structure prediction methods that rely on homology modeling, threading, and fragment assembly Hybrid methods that achieve high-resolution protein structures Parts of the protein structure that may be conserved and used to interact with other biomolecules How the loop prediction problem can be used for refinement of the modeled structures The computational model that detects the differences between protein structure and its modeled mutant Whether working in the field of bioinformatics or molecular biology research or taking courses in protein modeling, readers will find the content in this book invaluable.
Computational Methods For Protein Structure Prediction And Modeling
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Author : Ying Xu
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-08-24
Computational Methods For Protein Structure Prediction And Modeling written by Ying Xu and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-08-24 with Science categories.
Volume One of this two-volume sequence focuses on the basic characterization of known protein structures, and structure prediction from protein sequence information. Eleven chapters survey of the field, covering key topics in modeling, force fields, classification, computational methods, and structure prediction. Each chapter is a self contained review covering definition of the problem and historical perspective; mathematical formulation; computational methods and algorithms; performance results; existing software; strengths, pitfalls, challenges, and future research.
Learning Structured Prediction Models In Computer Vision
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Author : Fayao Liu
language : en
Publisher:
Release Date : 2015
Learning Structured Prediction Models In Computer Vision written by Fayao Liu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with Computer vision categories.
Most of the real world applications can be formulated as structured learning problems, in which the output domain can be arbitrary, e.g., a sequence or a graph. By modelling the structures (constraints and correlations) of the output variables, structured learning provides a more general learning scheme than simple binary classification or regression models. This thesis is dedicated to learning such structured prediction models, i.e., conditional random fields (CRFs) and their applications in computer vision. CRFs are popular probabilistic graphical models, which model the conditional distribution of the output variables given the observations. They play an essential role in the computer vision community and have found wide applications in various vision tasks-semantic labelling, object detection, pose estimation, to name a few. Specifically, we here focus on two challenging tasks in this thesis: image segmentation (also referred as semantic labelling) and depth estimation from single monocular images, which represent two types of CRFs models-discrete and continuous. In summary, we made three contributions in this thesis. First, we present a new approach to exploit tree potentials in CRFs for the task of image segmentation. This method combines the advantages of both CRFs and decision trees. Different from traditional methods, in which the potential functions of CRFs are defined as a linear combination of some pre-defined parametric models, we formulate the unary and the pairwise potentials as nonparametric forests-ensembles of decision trees, and learn the ensemble parameters and the trees in a unified optimization problem within the large-margin framework. In this fashion, we easily achieve nonlinear learning of potential functions on both unary and pairwise terms in CRFs. Moreover, we learn class-wise decision trees for each object that appears in the image. We further show that this challenging optimization can be efficiently solved by combining a modified column generation and cutting-planes techniques. Experimental results on both binary and multi-class segmentation datasets demonstrate the power of the learned nonlinear nonparametric potentials. Second, we propose to model the unary potentials of the CRFs using a convolutional neural network (CNN). The deep CNN is trained on the large-scale ImageNet dataset and transferred to image segmentation here for constructing unary potentials of super-pixels. The CRFs parameters are then learned within the max-margin framework using structured support vector machines (SSVM). To fully exploit context information in inference, we construct spatially related co-occurrence pairwise potentials and incorporate them into the energy function. This prefers labellings of object pairs that frequently co-occur in a certain spatial layout and at the same time avoids implausible labellings during the inference. Extensive experiments on binary and multi-class segmentation benchmarks demonstrate the potentials of the proposed method. Third, different from the previous two works, we address the problem of continuous CRFs learning, applied to the task of depth estimation from single images. Specifically, we formulate and learn the unary and pairwise potentials of a continuous CRFs model with CNN networks in a unified framework. We term this new method as deep convolutional neural fields, abbreviated as DCNF. It jointly explores the capacity of deep CNN and continuous CRFs. The proposed method can be used for depth estimation of general scenes with no geometric priors nor any extra information injected. Specifically, in our case, the integral of the partition function can be calculated in a closed form such that we can exactly solve the log-likelihood maximization. Moreover, solving the inference problem for predicting depths of a test image is highly efficient as closed-form solutions exist. We then further propose an equally effective model based on fully convolutional networks and a novel superpixel pooling method, which is ~ 10 times faster, to speedup the patch-wise convolutions in the deep model. With this more efficient model, we are able to design very deep networks to pursue further performance gain. Experiments on both indoor and outdoor scene datasets demonstrate that the proposed method significantly outperforms state-of-the-art depth estimation approaches. We also show experimentally that the proposed method generalizes well to depth estimations of images unrelated to the training data. This indicates the potential of our method for benefiting other vision tasks.
Algorithmic Learning Theory
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Author : Jyriki Kivinen
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-09-23
Algorithmic Learning Theory written by Jyriki Kivinen and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-09-23 with Computers categories.
This book constitutes the refereed proceedings of the 22nd International Conference on Algorithmic Learning Theory, ALT 2011, held in Espoo, Finland, in October 2011, co-located with the 14th International Conference on Discovery Science, DS 2011. The 28 revised full papers presented together with the abstracts of 5 invited talks were carefully reviewed and selected from numerous submissions. The papers are divided into topical sections of papers on inductive inference, regression, bandit problems, online learning, kernel and margin-based methods, intelligent agents and other learning models.
Protein Supersecondary Structures
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Author : Alexander E. Kister
language : en
Publisher: Springer Nature
Release Date : 2024-11-14
Protein Supersecondary Structures written by Alexander E. Kister and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-14 with Science categories.
This new edition delves into the latest developments in the field and new techniques used to study secondary and supersecondary structures (SSS) in proteins. Beyond the tremendous advances in the field from the AI-based AlphaFold algorithm, researchers continue to untangle how specific structures and protein folds come to be, and these chapters contain numerous techniques to further pursue this study. Written for the highly successful Methods in Molecular Biology series, chapters contain the kind of detailed implementation advice needed to ensure effective results in the lab. Authoritative and practical, Protein Supersecondary Structures: Methods and Protocols, Third Edition serves as a valuable resource for researchers exploring the relationship between amino acids sequences and protein structures, the evolution of proteins, and the dynamics of protein formation.