Deep Learning Generalization
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Deep Learning Generalization
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Author : Liu Peng
language : en
Publisher: CRC Press
Release Date : 2025-09-12
Deep Learning Generalization written by Liu Peng and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-09-12 with Computers categories.
This book provides a comprehensive exploration of generalization in deep learning, focusing on both theoretical foundations and practical strategies. It delves deeply into how machine learning models, particularly deep neural networks, achieve robust performance on unseen data. Key topics include balancing model complexity, addressing overfitting and underfitting, and understanding modern phenomena such as the double descent curve and implicit regularization. The book offers a holistic perspective by addressing the four critical components of model training: data, model architecture, objective functions, and optimization processes. It combines mathematical rigor with hands-on guidance, introducing practical implementation techniques using PyTorch to bridge the gap between theory and real-world applications. For instance, the book highlights how regularized deep learning models not only achieve better predictive performance but also assume a more compact and efficient parameter space. Structured to accommodate a progressive learning curve, the content spans foundational concepts like statistical learning theory to advanced topics like Neural Tangent Kernels and overparameterization paradoxes. By synthesizing classical and modern views of generalization, the book equips readers to develop a nuanced understanding of key concepts while mastering practical applications. For academics, the book serves as a definitive resource to solidify theoretical knowledge and explore cutting-edge research directions. For industry professionals, it provides actionable insights to enhance model performance systematically. Whether you're a beginner seeking foundational understanding or a practitioner exploring advanced methodologies, this book offers an indispensable guide to achieving robust generalization in deep learning.
Generalization With Deep Learning For Improvement On Sensing Capability
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Author : Zhenghua Chen
language : en
Publisher: World Scientific
Release Date : 2021-04-07
Generalization With Deep Learning For Improvement On Sensing Capability written by Zhenghua Chen and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-07 with Computers categories.
Deep Learning has achieved great success in many challenging research areas, such as image recognition and natural language processing. The key merit of deep learning is to automatically learn good feature representation from massive data conceptually. In this book, we will show that the deep learning technology can be a very good candidate for improving sensing capabilities.In this edited volume, we aim to narrow the gap between humans and machines by showcasing various deep learning applications in the area of sensing. The book will cover the fundamentals of deep learning techniques and their applications in real-world problems including activity sensing, remote sensing and medical sensing. It will demonstrate how different deep learning techniques help to improve the sensing capabilities and enable scientists and practitioners to make insightful observations and generate invaluable discoveries from different types of data.
Theoretical Insights On Generalization In Supervised And Self Supervised Deep Learning
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Author : Colin Wei
language : en
Publisher:
Release Date : 2022
Theoretical Insights On Generalization In Supervised And Self Supervised Deep Learning written by Colin Wei and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Deep learning has been extremely impactful empirically, but theoretical understanding lags behind. Neural networks are much more complex than more classical machine learning models in terms of both architecture and training algorithms, so traditional theoretical intuitions may not apply. This thesis seeks to gain a better theoretical understanding of generalization in deep learning. First, we study factors influencing generalization in supervised settings where all data are labeled, obtaining improved generalization bounds for neural networks by considering additional data-dependent properties of the model. Second, we study generalization in a setting with unlabeled data. In the vision setting, we present a theoretical framework for understanding recent self-training and self-supervised contrastive learning algorithms by leveraging a realistic assumption on the data. In the NLP setting, we analyze why pretraining can help with downstream tasks in the setting where data is generated according to an underlying latent variable model.
Ai Alignment And Generalization In Deep Learning
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Author : David Krueger
language : en
Publisher:
Release Date : 2021
Ai Alignment And Generalization In Deep Learning written by David Krueger and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
This thesis covers a number of works in deep learning aimed at understanding and improving generalization abilities of deep neural networks (DNNs). DNNs achieve unrivaled performance in a growing range of tasks and domains, yet their behavior during learning and deployment remains poorly understood. They can also be surprisingly brittle: in-distribution generalization can be a poor predictor of behavior or performance under distributional shifts, which typically cannot be avoided in practice. While these limitations are not unique to DNNs -- and indeed are likely to be challenges facing any AI systems of sufficient complexity -- the prevalence and power of DNNs makes them particularly worthy of study. I frame these challenges within the broader context of "AI Alignment": a nascent field focused on ensuring that AI systems behave in accordance with their user's intentions. While making AI systems more intelligent or capable can help make them more aligned, it is neither necessary nor sufficient for alignment. However, being able to align state-of-the-art AI systems (e.g. DNNs) is of great social importance in order to avoid undesirable and unsafe behavior from advanced AI systems. Without progress in AI Alignment, advanced AI systems might pursue objectives at odds with human survival, posing an existential risk (``x-risk'') to humanity. A core tenet of this thesis is that the achieving high performance on machine learning benchmarks if often a good indicator of AI systems' capabilities, but not their alignment. This is because AI systems often achieve high performance in unexpected ways that reveal the limitations of our performance metrics, and more generally, our techniques for specifying our intentions. Learning about human intentions using DNNs shows some promise, but DNNs are still prone to learning to solve tasks using concepts of "features" very different from those which are salient to humans. Indeed, this is a major source of their poor generalization on out-of-distribution data. By better understanding the successes and failures of DNN generalization and current methods of specifying our intentions, we aim to make progress towards deep-learning based AI systems that are able to understand users' intentions and act accordingly.
Generalization And Adaptation Of Deep Learning Models For Semantic Segmentation
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Author : Xueqing Deng
language : en
Publisher:
Release Date : 2021
Generalization And Adaptation Of Deep Learning Models For Semantic Segmentation written by Xueqing Deng and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
Thanks to the development of deep neural networks, a number of computer vision tasks have achieved great success. However, the focus has been mostly limited to benchmarks with regular scenes in a supervised training fashion. A deep learning model trained with perfect and ideal benchmark datasets can have difficulty when applied to real-world scenes where the data are captured under different settings, for example. This indicates the model has poor generalization capability. Problems also occur when a benchmark model is applied to a different real-world application than it was designed for and where the input data varies. Therefore, this dissertation seeks to improve model generalization and adaptation for the computer vision problem of semantic segmentation particularly for real-world applications.
Improving Deep Learning Generalization From The Perspective Of Dual Branch Capsule Network Contrastive Ace And Edge Gnn
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Author : 王允琪
language : en
Publisher:
Release Date : 2022
Improving Deep Learning Generalization From The Perspective Of Dual Branch Capsule Network Contrastive Ace And Edge Gnn written by 王允琪 and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with Deep learning (Machine learning) categories.
A Theory Of Learning And Generalization
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Author : Mathukumalli Vidyasagar
language : en
Publisher: Springer
Release Date : 1997
A Theory Of Learning And Generalization written by Mathukumalli Vidyasagar and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997 with Computers categories.
A Theory of Learning and Generalization provides a formal mathematical theory for addressing intuitive questions of the type: How does a machine learn a new concept on the basis of examples? How can a neural network, after sufficient training, correctly predict the output of a previously unseen input? How much training is required to achieve a specified level of accuracy in the prediction? How can one "identify" the dynamical behaviour of a nonlinear control system by observing its input-output behaviour over a finite interval of time? This is the first book to treat the problem of machine learning in conjunction with the theory of empirical processes, the latter being a well-established branch of probability theory. The treatment of both topics side by side leads to new insights, as well as new results in both topics. An extensive references section and open problems will help readers to develop their own work in the field.
Normalization And Generalization In Deep Learning
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Author : Griffin Hurt
language : en
Publisher:
Release Date : 2023
Normalization And Generalization In Deep Learning written by Griffin Hurt and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with Computational learning theory categories.
"In this thesis, we discuss the importance of data normalization in deep learning and its relationship with generalization. Normalization is a staple of deep learning architectures and has been shown to improve the stability and generalizability of deep learning models, yet the reason why these normalization techniques work is still unknown and is an active area of research. Inspired by this uncertainty, we explore how different normalization techniques perform when employed in different deep learning architectures, while also exploring generalization and metrics associated with generalization in congruence with our investigation into normalization. The goal behind our experiments was to investigate if there exist any identifiable trends for the different normalization methods across an array of different training schemes with respect to the various metrics employed. We found that class similarity was seemingly the strongest predictor for train accuracy, test accuracy, and generalization ratio across all employed metrics. Overall, BatchNorm and EvoNormBO generally performed the best on measures of test and train accuracy, while InstanceNorm and Plain performed the worst."--Abstract.
Learning And Generalisation
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Author : Mathukumalli Vidyasagar
language : en
Publisher: Springer Science & Business Media
Release Date : 2002-09-27
Learning And Generalisation written by Mathukumalli Vidyasagar 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 2002-09-27 with Technology & Engineering categories.
How does a machine learn a new concept on the basis of examples? This second edition takes account of important new developments in the field. It also deals extensively with the theory of learning control systems, now comparably mature to learning of neural networks.
Latent Data Augmentation And Modular Structure For Improved Generalization
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Author : Alexander Lamb
language : en
Publisher:
Release Date : 2022
Latent Data Augmentation And Modular Structure For Improved Generalization written by Alexander Lamb and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
This thesis explores the nature of generalization in deep learning and several settings in which it fails. In particular, deep neural networks can struggle to generalize in settings with limited data, insufficient supervision, challenging long-range dependencies, or complex structure and subsystems. This thesis explores the nature of these challenges for generalization in deep learning and presents several algorithms which seek to address these challenges. In the first article, we show how training with interpolated hidden states can improve generalization and calibration in deep learning. We also introduce a theory showing how our algorithm, which we call Manifold Mixup, leads to a flattening of the per-class hidden representations, which can be seen as a compression of the information in the hidden states. The second article is related to the first and shows how interpolated examples can be used for semi-supervised learning. In addition to interpolating the input examples, the model's interpolated predictions are used as targets for these examples. This improves results on standard benchmarks as well as classic 2D toy problems for semi-supervised learning. The third article studies how a recurrent neural network can be divided into multiple modules with different parameters and well separated hidden states, as well as a competition mechanism restricting updating of the hidden states to a subset of the most relevant modules on a specific time-step. This improves systematic generalization when the pattern distribution is changed between the training and evaluation phases. It also improves generalization in reinforcement learning. In the fourth article, we show that attention can be used to control the flow of information between successive layers in deep networks. This allows each layer to only process the subset of the previously computed layers' outputs which are most relevant. This improves generalization on relational reasoning tasks as well as standard benchmark classification tasks.