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Distilling Knowledge

Author: Bruce T. MORAN

Publisher: Harvard University Press

Published: 2009-06-30

Total Pages: 221

ISBN-13: 0674041224

Reacting to the perception that the break, early on in the scientific revolution, between alchemy and chemistry was clean and abrupt, Moran literately and engagingly recaps what was actually a slow process. Far from being the superstitious amalgam it is now considered, alchemy was genuine science before and during the scientific revolution. The distinctive alchemical procedure--distillation--became the fundamental method of analytical chemistry, and the alchemical goal of transmuting "base metals" into gold and silver led to the understanding of compounds and elements. What alchemy very gradually but finally lost in giving way to chemistry was its spiritual or religious aspect, the linkages it discerned between purely physical and psychological properties. Drawing saliently from the most influential alchemical and scientific texts of the medieval to modern epoch (especially the turbulent and eventful seventeenth century), Moran fashions a model short history of science volume

Distilling Knowledge

Author: Bruce T. Moran

Publisher: Harvard University Press

Published: 2005-01-30

Total Pages: 238

ISBN-13: 9780674014954

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Alchemy can't be science--common sense tells us as much. But perhaps common sense is not the best measure of what science is, or was. In this book, Bruce Moran looks past contemporary assumptions and prejudices to determine what alchemists were actually doing in the context of early modern science. Examining the ways alchemy and chemistry were studied and practiced between 1400 and 1700, he shows how these approaches influenced their respective practitioners' ideas about nature and shaped their inquiries into the workings of the natural world. His work sets up a dialogue between what historians have usually presented as separate spheres; here we see how alchemists and early chemists exchanged ideas and methods and in fact shared a territory between their two disciplines. Distilling Knowledge suggests that scientific revolution may wear a different appearance in different cultural contexts. The metaphor of the Scientific Revolution, Moran argues, can be expanded to make sense of alchemy and other so-called pseudo-sciences--by including a new framework in which "process can count as an object, in which making leads to learning, and in which the messiness of conflict leads to discernment." Seen on its own terms, alchemy can stand within the bounds of demonstrative science.

Machine Learning and Knowledge Discovery in Databases

Author: Massih-Reza Amini

Publisher: Springer Nature

Published: 2023-03-16

Total Pages: 722

ISBN-13: 3031264096

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The multi-volume set LNAI 13713 until 13718 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2022, which took place in Grenoble, France, in September 2022. The 236 full papers presented in these proceedings were carefully reviewed and selected from a total of 1060 submissions. In addition, the proceedings include 17 Demo Track contributions. The volumes are organized in topical sections as follows: Part I: Clustering and dimensionality reduction; anomaly detection; interpretability and explainability; ranking and recommender systems; transfer and multitask learning; Part II: Networks and graphs; knowledge graphs; social network analysis; graph neural networks; natural language processing and text mining; conversational systems; Part III: Deep learning; robust and adversarial machine learning; generative models; computer vision; meta-learning, neural architecture search; Part IV: Reinforcement learning; multi-agent reinforcement learning; bandits and online learning; active and semi-supervised learning; private and federated learning; . Part V: Supervised learning; probabilistic inference; optimal transport; optimization; quantum, hardware; sustainability; Part VI: Time series; financial machine learning; applications; applications: transportation; demo track.

Computational Methods for Deep Learning

Author: Wei Qi Yan

Publisher: Springer Nature

Published: 2023-10-17

Total Pages: 235

ISBN-13: 9819948231

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The first edition of this textbook was published in 2021. Over the past two years, we have invested in enhancing all aspects of deep learning methods to ensure the book is comprehensive and impeccable. Taking into account feedback from our readers and audience, the author has diligently updated this book. The second edition of this textbook presents control theory, transformer models, and graph neural networks (GNN) in deep learning. We have incorporated the latest algorithmic advances and large-scale deep learning models, such as GPTs, to align with the current research trends. Through the second edition, this book showcases how computational methods in deep learning serve as a dynamic driving force in this era of artificial intelligence (AI). This book is intended for research students, engineers, as well as computer scientists with interest in computational methods in deep learning. Furthermore, it is also well-suited for researchers exploring topics such as machine intelligence, robotic control, and related areas.

Computer Vision – ECCV 2022

Author: Shai Avidan

Publisher: Springer Nature

Published: 2022-11-02

Total Pages: 801

ISBN-13: 3031200837

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The 39-volume set, comprising the LNCS books 13661 until 13699, constitutes the refereed proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022. The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.

Machine Learning and Knowledge Discovery in Databases: Research Track

Author: Danai Koutra

Publisher: Springer Nature

Published: 2023-09-16

Total Pages: 754

ISBN-13: 3031434188

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The multi-volume set LNAI 14169 until 14175 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2023, which took place in Turin, Italy, in September 2023. The 196 papers were selected from the 829 submissions for the Research Track, and 58 papers were selected from the 239 submissions for the Applied Data Science Track. The volumes are organized in topical sections as follows: Part I: Active Learning; Adversarial Machine Learning; Anomaly Detection; Applications; Bayesian Methods; Causality; Clustering. Part II: Computer Vision; Deep Learning; Fairness; Federated Learning; Few-shot learning; Generative Models; Graph Contrastive Learning. Part III: Graph Neural Networks; Graphs; Interpretability; Knowledge Graphs; Large-scale Learning. Part IV: Natural Language Processing; Neuro/Symbolic Learning; Optimization; Recommender Systems; Reinforcement Learning; Representation Learning. Part V: Robustness; Time Series; Transfer and Multitask Learning. Part VI: Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare & Bioinformatics; Human-Computer Interaction; Recommendation and Information Retrieval. Part VII: Sustainability, Climate, and Environment.- Transportation & Urban Planning.- Demo.

Artificial Neural Networks and Machine Learning – ICANN 2019: Deep Learning

Author: Igor V. Tetko

Publisher: Springer Nature

Published: 2019-09-09

Total Pages: 807

ISBN-13: 3030304841

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The proceedings set LNCS 11727, 11728, 11729, 11730, and 11731 constitute the proceedings of the 28th International Conference on Artificial Neural Networks, ICANN 2019, held in Munich, Germany, in September 2019. The total of 277 full papers and 43 short papers presented in these proceedings was carefully reviewed and selected from 494 submissions. They were organized in 5 volumes focusing on theoretical neural computation; deep learning; image processing; text and time series; and workshop and special sessions.

Distilling Democracy

Author: Jonathan Zimmerman

Publisher:

Published: 1999

Total Pages: 240

ISBN-13:

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Zimmerman (educational history, New York U.) examines the history of Scientific Temperance Instruction, a curriculum on the evils of alcohol which was originally developed and advocated by a grassroots movement, and ultimately was mandated in all American schools for a time. He traces today's debate on drug and alcohol education to issues raised in this seminal episode. The debate over STI, claims Zimmerman, was really about the balance between expertise and populist desire in determining what should be taught to America's children. Annotation copyrighted by Book News, Inc., Portland, OR

Fundamentals of Distillery Practice

Author: Herman F. Willkie

Publisher:

Published: 2021-04-02

Total Pages: 206

ISBN-13: 9781736980200

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Written specifically for use in the educational program of the production division of Seagram Distillers Corporation, this volume provides a fundamental explanation of the physical and chemical processes involved in the operation of a grain alcohol distillery.

ECAI 2020

Author: G. De Giacomo

Publisher: IOS Press

Published: 2020-09-11

Total Pages: 3122

ISBN-13: 164368101X

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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.