Download or read book Deep Neural Networks with Contextual Probabilistic Units written by Xinjie Fan and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep neural networks (NNs) have become ubiquitous and achieved state-of-the-art results in a wide variety of research fields. Unlike the traditional machine learning techniques that require hand-crafted feature extractors to transform raw data, deep learning methods are able to automatically learn useful representations by exploiting the data. Despite the great success of deep learning methods, there are still many challenges in front of us. In this thesis, we propose new contextual probabilistic units to make progress along three directions in deep learning, including uncertainty estimation, generalization, and optimization. Unlike traditional probabilistic models that learn a distribution of predictions, deep learning models, composed of deterministic mappings, often only give us point estimates of predictions, lacking a sense of uncertainty. Dropout is an effective probabilistic unit to estimate uncertainty for neural networks. However, the quality of uncertainty estimation depends heavily on the dropout probabilities. Existing methods treat dropout probabilities as global parameters shared across all data samples. We introduce contextual dropout, a sample-dependent dropout, where we consider parameterizing dropout probabilities as a function of input covariates. This generalization could greatly enhance the neural network's capability of modeling uncertainty and bridge the gap between traditional probabilistic models and deep neural networks. To obtain uncertainty estimation for attention neural networks, we propose Bayesian attention modules where the attention weights are related to continuous latent alignment random variables dependent on the contextual information and learned in a probabilistic manner. The whole training process can be made differentiable via the reparameterization trick. Our method is able to capture complicated probabilistic dependencies as well as obtain better uncertainty estimation than previous methods while maintaining scalability. Deep NNs learn the representations from data in an implicit way, making them prone to learning features that do not generalize across domains. We study the impact on domain generalization from transferring the training-domain statistics to the testing domain in the normalization layer. We propose a novel normalization approach to learn both the standardization and rescaling statistics via neural networks, transforming input features to useful contextual statistics. This new form of normalization can be viewed as a generic form of the traditional normalizations. The statistics are learned to be adaptive to the data coming from different domains, and hence improve the model generalization performance across domains. Stochastic gradient descent has achieved great success in optimizing deterministic neural networks. However, standard backpropagation no longer applies to the training process of neural networks with stochastic latent variables and one often resorts to a REINFORCE gradient estimator, which has large variance. We address this issue on challenging contextual categorical sequence generation tasks, where the learning signal is noisy and/or sparse and the learning space is exponentially large. We adapt the ARSM estimator to our solution, using correlated Monte Carlo rollouts to reduce gradient variances. Our methods show significant reduction of gradient variance and consistently outperform related baselines

Download or read book Probabilistic Deep Learning written by Beate Sick and published by Simon and Schuster. This book was released on 2020-10-11 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probabilistic Deep Learning is a hands-on guide to the principles that support neural networks. Learn to improve network performance with the right distribution for different data types, and discover Bayesian variants that can state their own uncertainty to increase accuracy. This book provides easy-to-apply code and uses popular frameworks to keep you focused on practical applications. Summary Probabilistic Deep Learning: With Python, Keras and TensorFlow Probability teaches the increasingly popular probabilistic approach to deep learning that allows you to refine your results more quickly and accurately without much trial-and-error testing. Emphasizing practical techniques that use the Python-based Tensorflow Probability Framework, you’ll learn to build highly-performant deep learning applications that can reliably handle the noise and uncertainty of real-world data. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology The world is a noisy and uncertain place. Probabilistic deep learning models capture that noise and uncertainty, pulling it into real-world scenarios. Crucial for self-driving cars and scientific testing, these techniques help deep learning engineers assess the accuracy of their results, spot errors, and improve their understanding of how algorithms work. About the book Probabilistic Deep Learning is a hands-on guide to the principles that support neural networks. Learn to improve network performance with the right distribution for different data types, and discover Bayesian variants that can state their own uncertainty to increase accuracy. This book provides easy-to-apply code and uses popular frameworks to keep you focused on practical applications. What's inside Explore maximum likelihood and the statistical basis of deep learning Discover probabilistic models that can indicate possible outcomes Learn to use normalizing flows for modeling and generating complex distributions Use Bayesian neural networks to access the uncertainty in the model About the reader For experienced machine learning developers. About the author Oliver Dürr is a professor at the University of Applied Sciences in Konstanz, Germany. Beate Sick holds a chair for applied statistics at ZHAW and works as a researcher and lecturer at the University of Zurich. Elvis Murina is a data scientist. Table of Contents PART 1 - BASICS OF DEEP LEARNING 1 Introduction to probabilistic deep learning 2 Neural network architectures 3 Principles of curve fitting PART 2 - MAXIMUM LIKELIHOOD APPROACHES FOR PROBABILISTIC DL MODELS 4 Building loss functions with the likelihood approach 5 Probabilistic deep learning models with TensorFlow Probability 6 Probabilistic deep learning models in the wild PART 3 - BAYESIAN APPROACHES FOR PROBABILISTIC DL MODELS 7 Bayesian learning 8 Bayesian neural networks

Download or read book Deep Learning written by Dr. Om Prakash C and published by Archers & Elevators Publishing House. This book was released on with total page 556 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book ARTIFICIAL NEURAL NETWORKS written by Dr. N.N. Praboo and published by Archers & Elevators Publishing House. This book was released on with total page 556 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Investigation of Connection Between Deep Learning and Probabilistic Graphical Models written by Paul Andrew Hager and published by . This book was released on 2018 with total page 21 pages. Available in PDF, EPUB and Kindle. Book excerpt: The field of machine learning (ML) has benefitted greatly from its relationship with the field of classical statistics. In support of that continued expansion, the following proposes an alternative perspective at the link between these fields. The link focuses on probabilistic graphical models in the context of reinforcement learning. Viewing certain algorithms as reinforcement learning gives one an ability to map ML concepts to statistics problems. Training a multi-layer nonlinear perceptron algorithm is equivalent to structure learning problems in probabilistic graphical models (PGMs). The technique of boosting weak rules into an ensemble is weighted sampling. Finally regularizing neural networks using the dropout technique is conditioning on certain observations in PGMs.

Download or read book Multivariate Statistical Machine Learning Methods for Genomic Prediction written by Osval Antonio Montesinos López and published by Springer Nature. This book was released on 2022-02-14 with total page 707 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is open access under a CC BY 4.0 license This open access book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists. It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool. To do so, for each tool the book provides background theory, some elements of the R statistical software for its implementation, the conceptual underpinnings, and at least two illustrative examples with data from real-world genomic selection experiments. Lastly, worked-out examples help readers check their own comprehension.The book will greatly appeal to readers in plant (and animal) breeding, geneticists and statisticians, as it provides in a very accessible way the necessary theory, the appropriate R code, and illustrative examples for a complete understanding of each statistical learning tool. In addition, it weighs the advantages and disadvantages of each tool.

Download or read book Efficient Processing of Deep Neural Networks written by Vivienne Sze and published by Springer Nature. This book was released on 2022-05-31 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are currently widely used for many artificial intelligence (AI) applications, including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Therefore, techniques that enable efficient processing of deep neural networks to improve key metrics—such as energy-efficiency, throughput, and latency—without sacrificing accuracy or increasing hardware costs are critical to enabling the wide deployment of DNNs in AI systems. The book includes background on DNN processing; a description and taxonomy of hardware architectural approaches for designing DNN accelerators; key metrics for evaluating and comparing different designs; features of DNN processing that are amenable to hardware/algorithm co-design to improve energy efficiency and throughput; and opportunities for applying new technologies. Readers will find a structured introduction to the field as well as formalization and organization of key concepts from contemporary work that provide insights that may spark new ideas.

Download or read book Graph Neural Networks for Multimodal Learning and Representation written by Mahmoud Khademi and published by . This book was released on 2019 with total page 97 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recently, several deep learning models are proposed that operate on graph-structured data. These models, which are known as graph neural networks, emphasize on methods for reasoning about non-Euclidean data. By combining end-to-end and handcrafted learning, graph neural networks can supply both relational reasoning and compositionality which are extremely important in many emerging tasks. This new paradigm is also consistent with the attributes of human intelligence: a human represents complicated systems as compositions of simple units and their interactions. Another important feature of graph neural networks is that they can often support complex attention mechanisms, and learn rich contextual representations by sending messages across different components of the input data. The main focus of this thesis is to solve some multimodal learning tasks by either introducing new graph neural network architectures or extending the existing graph neural network models and applying them to solve the tasks. I address three tasks: visual question answering (VQA), scene graph generation, and automatic image caption generation. I show that graph neural networks are effective tools to achieve better performance on these tasks. Despite all the hype and excitements about the future influence of graph neural networks, an open question about graph neural networks remains: how can we obtain the (structure of) the graphs that graph neural networks perform on? That is, how can we transform sensory input data such as images and text into graphs. A second main emphasis of this thesis is, therefore, to introduce new techniques and algorithms to address this issue. We introduce a generative graph neural network model based on reinforcement learning and recurrent neural networks (RNNs) to extract a structured representation from sensory data. The specific contributions are the following: We introduce a new neural network architecture, Multimodal Neural Graph Memory Networks (MN-GMN), for the VQA task. A key issue for VQA is how to reason about information from different image regions that is relevant for answering the question. Our novel approach uses graph structure with different region features as node attributes and applies a recently proposed powerful graph neural network model, Graph Network (GN), to reason about objects and their interactions in the scene context. The flexibility of GNs allows us to integrate bimodal sources of local information, text and visual, both within and across each modality. Experiments show MN-GMN outperforms the state-of-the-art on Visual7W and VQA v2.0 datasets and achieves comparable to the state-of-the-art results on CLEVR dataset. We propose a new algorithm, called Deep Generative Probabilistic Graph Neural Networks (DG-PGNN), to generate a scene graph for an image. The input to DG-PGNN is an image, together with a set of region-grounded captions (RGCs) and object bounding-box proposals for the image. To generate the scene graph, DG-PGNN constructs and updates a new model, called a Probabilistic Graph Network (PGN). A PGN can be thought of as a scene graph with uncertainty: it represents each node and each edge by a CNN feature vector and defines a probability mass function (PMF) for node-type (object category) of each node and edge-type (predicate class) of each edge. The DG-PGNN sequentially adds a new node to the current PGN by learning the optimal ordering in a Deep Q-learning framework, where states are partial PGNs, actions choose a new node, and rewards are defined based on the ground-truth. After adding a node, DG-PGNN uses message passing to update the feature vectors of the current PGN by leveraging contextual relationship information, object co-occurrences, and language priors from captions. The updated features are then used to fine-tune the PMFs. Our experiments show that the proposed algorithm significantly outperforms the state-of-the-art results on the Visual Genome dataset for the scene graph generation. We present a novel context-aware attention-based deep architecture for image caption generation. Our architecture employs a Bidirectional Grid LSTM, which takes visual features of an image as input and learns complex spatial patterns based on a two-dimensional context, by selecting or ignoring its input. The Grid LSTM can be seen as a graph neural network model with a grid structure. The Grid LSTM has not been applied to the image caption generation task before. Another novel aspect is that we leverage a set of local RGCs obtained by transfer learning. The RGCs often describe the properties of the objects and their relationships in an image. To generate a global caption for the image, we integrate the spatial features from the Grid LSTM with the local region-grounded texts, using a two-layer Bidirectional LSTM. The first layer models the global scene context such as object presence. The second layer utilizes a novel dynamic spatial attention mechanism, based on another Grid LSTM, to generate the global caption word-by-word while considering the caption context around a word in both directions. Unlike recent models that use a soft attention mechanism, our dynamic spatial attention mechanism considers the spatial context of the image regions. Experimental results on the MS-COCO dataset show that our architecture outperforms the state-of-the-art.

Download or read book Engineering Applications of Artificial Intelligence written by Aziza Chakir and published by Springer Nature. This book was released on with total page 443 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Proceedings of the Twenty first Annual Conference of the Cognitive Science Society written by Martin Hahn and published by Psychology Press. This book was released on 2020-12-22 with total page 847 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the complete collection of peer-reviewed presentations at the 1999 Cognitive Science Society meeting, including papers, poster abstracts, and descriptions of conference symposia. For students and researchers in all areas of cognitive science.

Download or read book Neural Network for Beginners written by Sebastian Klaas and published by BPB Publications. This book was released on 2021-08-24 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: KEY FEATURES ● Understand applications like reinforcement learning, automatic driving and image generation. ● Understand neural networks accompanied with figures and charts. ● Learn about determining coefficients and initial values of weights. DESCRIPTION Deep learning helps you solve issues related to data problems as it has a vast array of mathematical algorithms and has capacity to detect patterns. This book starts with a quick view of deep learning in Python which would include definition, features and applications. You would be learning about perceptron, neural networks, Backpropagation. This book would also give you a clear insight of how to use Numpy and Matplotlin in deep learning models. By the end of the book, you’ll have the knowledge to apply the relevant technologies in deep learning. WHAT YOU WILL LEARN ● To develop deep learning applications, use Python with few outside inputs. ● Study several ideas of profound learning and neural networks ● Learn how to determine coefficients of learning and weight values ● Explore applications such as automation, image generation and reinforcement learning ● Implement trends like batch Normalisation, dropout, and Adam WHO THIS BOOK IS FOR Deep Learning from the Basics is for data scientists, data analysts and developers who wish to build efficient solutions by applying deep learning techniques. Individuals who would want a better grasp of technology and an overview. You should have a workable Python knowledge is a required. NumPy knowledge and pandas will be an advantage, but that’s completely optional. TABLE OF CONTENTS 1. Python Introduction 2. Perceptron in Depth 3. Neural Networks 4. Training Neural Network 5. Backpropagation 6. Neural Network Training Techniques 7. CNN 8. Deep Learning

Download or read book Deep and Shallow written by Shlomo Dubnov and published by CRC Press. This book was released on 2023-12-08 with total page 430 pages. Available in PDF, EPUB and Kindle. Book excerpt: Providing an essential and unique bridge between the theories of signal processing, machine learning, and artificial intelligence (AI) in music, this book provides a holistic overview of foundational ideas in music, from the physical and mathematical properties of sound to symbolic representations. Combining signals and language models in one place, this book explores how sound may be represented and manipulated by computer systems, and how our devices may come to recognize particular sonic patterns as musically meaningful or creative through the lens of information theory. Introducing popular fundamental ideas in AI at a comfortable pace, more complex discussions around implementations and implications in musical creativity are gradually incorporated as the book progresses. Each chapter is accompanied by guided programming activities designed to familiarize readers with practical implications of discussed theory, without the frustrations of free-form coding. Surveying state-of-the art methods in applications of deep neural networks to audio and sound computing, as well as offering a research perspective that suggests future challenges in music and AI research, this book appeals to both students of AI and music, as well as industry professionals in the fields of machine learning, music, and AI.

Download or read book Efficient Learning Machines written by Mariette Awad and published by Apress. This book was released on 2015-04-27 with total page 263 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning techniques provide cost-effective alternatives to traditional methods for extracting underlying relationships between information and data and for predicting future events by processing existing information to train models. Efficient Learning Machines explores the major topics of machine learning, including knowledge discovery, classifications, genetic algorithms, neural networking, kernel methods, and biologically-inspired techniques. Mariette Awad and Rahul Khanna’s synthetic approach weaves together the theoretical exposition, design principles, and practical applications of efficient machine learning. Their experiential emphasis, expressed in their close analysis of sample algorithms throughout the book, aims to equip engineers, students of engineering, and system designers to design and create new and more efficient machine learning systems. Readers of Efficient Learning Machines will learn how to recognize and analyze the problems that machine learning technology can solve for them, how to implement and deploy standard solutions to sample problems, and how to design new systems and solutions. Advances in computing performance, storage, memory, unstructured information retrieval, and cloud computing have coevolved with a new generation of machine learning paradigms and big data analytics, which the authors present in the conceptual context of their traditional precursors. Awad and Khanna explore current developments in the deep learning techniques of deep neural networks, hierarchical temporal memory, and cortical algorithms. Nature suggests sophisticated learning techniques that deploy simple rules to generate highly intelligent and organized behaviors with adaptive, evolutionary, and distributed properties. The authors examine the most popular biologically-inspired algorithms, together with a sample application to distributed datacenter management. They also discuss machine learning techniques for addressing problems of multi-objective optimization in which solutions in real-world systems are constrained and evaluated based on how well they perform with respect to multiple objectives in aggregate. Two chapters on support vector machines and their extensions focus on recent improvements to the classification and regression techniques at the core of machine learning.

Download or read book Cognitively Inspired Natural Language Processing written by Abhijit Mishra and published by Springer. This book was released on 2018-08-01 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book shows ways of augmenting the capabilities of Natural Language Processing (NLP) systems by means of cognitive-mode language processing. The authors employ eye-tracking technology to record and analyze shallow cognitive information in the form of gaze patterns of readers/annotators who perform language processing tasks. The insights gained from such measures are subsequently translated into systems that help us (1) assess the actual cognitive load in text annotation, with resulting increase in human text-annotation efficiency, and (2) extract cognitive features that, when added to traditional features, can improve the accuracy of text classifiers. In sum, the authors’ work successfully demonstrates that cognitive information gleaned from human eye-movement data can benefit modern NLP. Currently available Natural Language Processing (NLP) systems are weak AI systems: they seek to capture the functionality of human language processing, without worrying about how this processing is realized in human beings’ hardware. In other words, these systems are oblivious to the actual cognitive processes involved in human language processing. This ignorance, however, is NOT bliss! The accuracy figures of all non-toy NLP systems saturate beyond a certain point, making it abundantly clear that “something different should be done.”

Download or read book The Oxford Handbook of Computational Linguistics written by Ruslan Mitkov and published by Oxford University Press. This book was released on 2022-03-09 with total page 1377 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ruslan Mitkov's highly successful Oxford Handbook of Computational Linguistics has been substantially revised and expanded in this second edition. Alongside updated accounts of the topics covered in the first edition, it includes 17 new chapters on subjects such as semantic role-labelling, text-to-speech synthesis, translation technology, opinion mining and sentiment analysis, and the application of Natural Language Processing in educational and biomedical contexts, among many others. The volume is divided into four parts that examine, respectively: the linguistic fundamentals of computational linguistics; the methods and resources used, such as statistical modelling, machine learning, and corpus annotation; key language processing tasks including text segmentation, anaphora resolution, and speech recognition; and the major applications of Natural Language Processing, from machine translation to author profiling. The book will be an essential reference for researchers and students in computational linguistics and Natural Language Processing, as well as those working in related industries.

Download or read book Keras 2 x Projects written by Giuseppe Ciaburro and published by Packt Publishing Ltd. This book was released on 2018-12-31 with total page 386 pages. Available in PDF, EPUB and Kindle. Book excerpt: Demonstrate fundamentals of Deep Learning and neural network methodologies using Keras 2.x Key FeaturesExperimental projects showcasing the implementation of high-performance deep learning models with Keras.Use-cases across reinforcement learning, natural language processing, GANs and computer vision.Build strong fundamentals of Keras in the area of deep learning and artificial intelligence.Book Description Keras 2.x Projects explains how to leverage the power of Keras to build and train state-of-the-art deep learning models through a series of practical projects that look at a range of real-world application areas. To begin with, you will quickly set up a deep learning environment by installing the Keras library. Through each of the projects, you will explore and learn the advanced concepts of deep learning and will learn how to compute and run your deep learning models using the advanced offerings of Keras. You will train fully-connected multilayer networks, convolutional neural networks, recurrent neural networks, autoencoders and generative adversarial networks using real-world training datasets. The projects you will undertake are all based on real-world scenarios of all complexity levels, covering topics such as language recognition, stock volatility, energy consumption prediction, faster object classification for self-driving vehicles, and more. By the end of this book, you will be well versed with deep learning and its implementation with Keras. You will have all the knowledge you need to train your own deep learning models to solve different kinds of problems. What you will learnApply regression methods to your data and understand how the regression algorithm worksUnderstand the basic concepts of classification methods and how to implement them in the Keras environmentImport and organize data for neural network classification analysisLearn about the role of rectified linear units in the Keras network architectureImplement a recurrent neural network to classify the sentiment of sentences from movie reviewsSet the embedding layer and the tensor sizes of a networkWho this book is for If you are a data scientist, machine learning engineer, deep learning practitioner or an AI engineer who wants to build speedy intelligent applications with minimal lines of codes, then this book is the best fit for you. Sound knowledge of machine learning and basic familiarity with Keras library would be useful.

Download or read book Understanding Deep Learning written by Simon J.D. Prince and published by MIT Press. This book was released on 2023-12-05 with total page 544 pages. Available in PDF, EPUB and Kindle. Book excerpt: An authoritative, accessible, and up-to-date treatment of deep learning that strikes a pragmatic middle ground between theory and practice. Deep learning is a fast-moving field with sweeping relevance in today’s increasingly digital world. Understanding Deep Learning provides an authoritative, accessible, and up-to-date treatment of the subject, covering all the key topics along with recent advances and cutting-edge concepts. Many deep learning texts are crowded with technical details that obscure fundamentals, but Simon Prince ruthlessly curates only the most important ideas to provide a high density of critical information in an intuitive and digestible form. From machine learning basics to advanced models, each concept is presented in lay terms and then detailed precisely in mathematical form and illustrated visually. The result is a lucid, self-contained textbook suitable for anyone with a basic background in applied mathematics. Up-to-date treatment of deep learning covers cutting-edge topics not found in existing texts, such as transformers and diffusion models Short, focused chapters progress in complexity, easing students into difficult concepts Pragmatic approach straddling theory and practice gives readers the level of detail required to implement naive versions of models Streamlined presentation separates critical ideas from background context and extraneous detail Minimal mathematical prerequisites, extensive illustrations, and practice problems make challenging material widely accessible Programming exercises offered in accompanying Python Notebooks