Inductive Biases In Machine Learning For Robotics And Control
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Inductive Biases In Machine Learning For Robotics And Control
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Author : Michael Lutter
language : en
Publisher: Springer Nature
Release Date : 2023-07-31
Inductive Biases In Machine Learning For Robotics And Control written by Michael Lutter and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-31 with Technology & Engineering categories.
One important robotics problem is “How can one program a robot to perform a task”? Classical robotics solves this problem by manually engineering modules for state estimation, planning, and control. In contrast, robot learning solely relies on black-box models and data. This book shows that these two approaches of classical engineering and black-box machine learning are not mutually exclusive. To solve tasks with robots, one can transfer insights from classical robotics to deep networks and obtain better learning algorithms for robotics and control. To highlight that incorporating existing knowledge as inductive biases in machine learning algorithms improves performance, this book covers different approaches for learning dynamics models and learning robust control policies. The presented algorithms leverage the knowledge of Newtonian Mechanics, Lagrangian Mechanics as well as the Hamilton-Jacobi-Isaacs differential equation as inductive bias and are evaluated on physical robots.
Change Of Representation And Inductive Bias
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Author : D. Paul Benjamin
language : en
Publisher: Springer
Release Date : 1989-12-31
Change Of Representation And Inductive Bias written by D. Paul Benjamin and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 1989-12-31 with Computers categories.
Natural Inductive Biases For Artificial Intelligence
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Author : T. Anderson Keller
language : en
Publisher:
Release Date : 2023
Natural Inductive Biases For Artificial Intelligence written by T. Anderson Keller and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.
"The study of inductive bias is one of the most all encompassing in all of machine learning. Inductive biases define not only the efficiency and speed of learning, but also what is ultimately possible to learn by a given machine learning system. The history of modern machine learning is intertwined with that of psychology, cognitive science and neuroscience, and therefore many of the most impactful inductive biases have come directly from these fields. Examples include convolutional neural networks, stemming from the observed organization of natural visual systems, and artificial neural networks themselves intending to model idolized abstract neural circuits. Given the dramatic successes of machine learning in recent years however, more emphasis has been placed on the engineering challenges faced by scaling up machine learning systems, with less focus on their inductive biases . This thesis will be an attempted step in the reverse direction. To do so, we will cover both naturally relevant learning algorithms, as well as natural structure inherent to neural representations. We will build artificial systems which are modeled after these natural properties, and we will demonstrate how they are both beneficial to computation, and may serve to help us better understand natural intelligence itself." --
Inductive Bias In Machine Learning
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Author : Luca Rendsburg
language : en
Publisher:
Release Date : 2022
Inductive Bias In Machine Learning written by Luca Rendsburg 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.
Inductive bias describes the preference for solutions that a machine learning algorithm holds before seeing any data. It is a necessary ingredient for the goal of machine learning, which is to generalize from a set of examples to unseen data points. Yet, the inductive bias of learning algorithms is often not specified explicitly in practice, which prevents a theoretical understanding and undermines trust in machine learning. This issue is most prominently visible in the contemporary case of deep learning, which is widely successful in applications but relies on many poorly understood techniques and heuristics. This thesis aims to uncover the hidden inductive biases of machine learning algorithms. In the first part of the thesis, we uncover the implicit inductive bias of NetGAN, a complex graph generative model with seemingly no prior preferences. We find that the root of its generalization properties does not lie in the GAN architecture but in an inconspicuous low-rank approximation. We then use this insight to strip NetGAN of all unnecessary parts, including the GAN, and obtain a highly simplified reformulation. Next, we present a generic algorithm that reverse-engineers hidden inductive bias in approximate Bayesian inference. While the inductive bias is completely described by the prior distribution in full Bayesian inference, real-world applications often resort to approximate techniques that can make uncontrollable errors. By reframing the problem in terms of incompatible conditional distributions, we arrive at a generic algorithm based on pseudo-Gibbs sampling that attributes the change in inductive bias to a change in the prior distribution. The last part of the thesis concerns a common inductive bias in causal learning, the assumption of independent causal mechanisms. Under this assumption, we consider estimators for confounding strength, which governs the generalization ability from observational distribution to the underlying causal model. We show that an existing estimator is generally inconsistent and propose a consistent estimator based on tools from random matrix theory.
Explanation Based Neural Network Learning
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Author : Sebastian Thrun
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Explanation Based Neural Network Learning written by Sebastian Thrun 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 2012-12-06 with Computers categories.
Lifelong learning addresses situations in which a learner faces a series of different learning tasks providing the opportunity for synergy among them. Explanation-based neural network learning (EBNN) is a machine learning algorithm that transfers knowledge across multiple learning tasks. When faced with a new learning task, EBNN exploits domain knowledge accumulated in previous learning tasks to guide generalization in the new one. As a result, EBNN generalizes more accurately from less data than comparable methods. Explanation-Based Neural Network Learning: A Lifelong Learning Approach describes the basic EBNN paradigm and investigates it in the context of supervised learning, reinforcement learning, robotics, and chess. `The paradigm of lifelong learning - using earlier learned knowledge to improve subsequent learning - is a promising direction for a new generation of machine learning algorithms. Given the need for more accurate learning methods, it is difficult to imagine a future for machine learning that does not include this paradigm.' From the Foreword by Tom M. Mitchell.
Change Of Representation And Inductive Bias
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Author : D. Paul Benjamin
language : en
Publisher:
Release Date : 1989-12-31
Change Of Representation And Inductive Bias written by D. Paul Benjamin and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1989-12-31 with categories.
Machine Learning For Networking
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Author : Éric Renault
language : en
Publisher: Springer
Release Date : 2019-05-10
Machine Learning For Networking written by Éric Renault and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-10 with Computers categories.
This book constitutes the thoroughly refereed proceedings of the First International Conference on Machine Learning for Networking, MLN 2018, held in Paris, France, in November 2018. The 22 revised full papers included in the volume were carefully reviewed and selected from 48 submissions. They present new trends in the following topics: Deep and reinforcement learning; Pattern recognition and classification for networks; Machine learning for network slicing optimization, 5G system, user behavior prediction, multimedia, IoT, security and protection; Optimization and new innovative machine learning methods; Performance analysis of machine learning algorithms; Experimental evaluations of machine learning; Data mining in heterogeneous networks; Distributed and decentralized machine learning algorithms; Intelligent cloud-support communications, resource allocation, energy-aware/green communications, software defined networks, cooperative networks, positioning and navigation systems, wireless communications, wireless sensor networks, underwater sensor networks.
Recent Advances In Robot Learning
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Author : Judy A. Franklin
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Recent Advances In Robot Learning written by Judy A. Franklin 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 2012-12-06 with Computers categories.
Recent Advances in Robot Learning contains seven papers on robot learning written by leading researchers in the field. As the selection of papers illustrates, the field of robot learning is both active and diverse. A variety of machine learning methods, ranging from inductive logic programming to reinforcement learning, is being applied to many subproblems in robot perception and control, often with objectives as diverse as parameter calibration and concept formulation. While no unified robot learning framework has yet emerged to cover the variety of problems and approaches described in these papers and other publications, a clear set of shared issues underlies many robot learning problems. Machine learning, when applied to robotics, is situated: it is embedded into a real-world system that tightly integrates perception, decision making and execution. Since robot learning involves decision making, there is an inherent active learning issue. Robotic domains are usually complex, yet the expense of using actual robotic hardware often prohibits the collection of large amounts of training data. Most robotic systems are real-time systems. Decisions must be made within critical or practical time constraints. These characteristics present challenges and constraints to the learning system. Since these characteristics are shared by other important real-world application domains, robotics is a highly attractive area for research on machine learning. On the other hand, machine learning is also highly attractive to robotics. There is a great variety of open problems in robotics that defy a static, hand-coded solution. Recent Advances in Robot Learning is an edited volume of peer-reviewed original research comprising seven invited contributions by leading researchers. This research work has also been published as a special issue of Machine Learning (Volume 23, Numbers 2 and 3).
Knowledge Guided Machine Learning
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Author : Anuj Karpatne
language : en
Publisher: CRC Press
Release Date : 2022-08-15
Knowledge Guided Machine Learning written by Anuj Karpatne and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-15 with Business & Economics categories.
Given their tremendous success in commercial applications, machine learning (ML) models are increasingly being considered as alternatives to science-based models in many disciplines. Yet, these "black-box" ML models have found limited success due to their inability to work well in the presence of limited training data and generalize to unseen scenarios. As a result, there is a growing interest in the scientific community on creating a new generation of methods that integrate scientific knowledge in ML frameworks. This emerging field, called scientific knowledge-guided ML (KGML), seeks a distinct departure from existing "data-only" or "scientific knowledge-only" methods to use knowledge and data at an equal footing. Indeed, KGML involves diverse scientific and ML communities, where researchers and practitioners from various backgrounds and application domains are continually adding richness to the problem formulations and research methods in this emerging field. Knowledge Guided Machine Learning: Accelerating Discovery using Scientific Knowledge and Data provides an introduction to this rapidly growing field by discussing some of the common themes of research in KGML using illustrative examples, case studies, and reviews from diverse application domains and research communities as book chapters by leading researchers. KEY FEATURES First-of-its-kind book in an emerging area of research that is gaining widespread attention in the scientific and data science fields Accessible to a broad audience in data science and scientific and engineering fields Provides a coherent organizational structure to the problem formulations and research methods in the emerging field of KGML using illustrative examples from diverse application domains Contains chapters by leading researchers, which illustrate the cutting-edge research trends, opportunities, and challenges in KGML research from multiple perspectives Enables cross-pollination of KGML problem formulations and research methods across disciplines Highlights critical gaps that require further investigation by the broader community of researchers and practitioners to realize the full potential of KGML
Metaheuristics Algorithm And Optimization Of Engineering And Complex Systems
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Author : R., Thanigaivelan
language : en
Publisher: IGI Global
Release Date : 2024-07-23
Metaheuristics Algorithm And Optimization Of Engineering And Complex Systems written by R., Thanigaivelan and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-23 with Computers categories.
In the field of engineering, optimization and decision-making have become pivotal concerns. The ever-increasing demand for data processing has given rise to issues such as extended processing times and escalated memory utilization, posing formidable obstacles across various engineering domains. Problems persist, requiring not only solutions but advancements beyond existing best practices. Creating and implementing novel heuristic algorithms is a time-intensive process, yet the imperative to do so remains strong, driven by the potential to significantly lower computational costs even with marginal improvements. This book, titled Metaheuristics Algorithm and Optimization of Engineering and Complex Systems, is a beacon of innovation in this context. It examines the critical need for inventive algorithmic solutions, exploring hyperheuristic approaches that offer solutions such as automating search spaces through integrated heuristics. Designed to cater to a broad audience, this book is a valuable resource for both novice and experienced dynamic optimization practitioners. By addressing the spectrum of theory and practice, as well as discrete versus continuous dynamic optimization, it becomes an indispensable reference in a captivating and emerging field. With a deliberate focus on inclusivity, the book is poised to benefit anyone with an interest in staying abreast of the latest developments in dynamic optimization.