Structured Representation Learning: From Homomorphisms and Disentanglement to Equivariance and Topography
暫譯: 結構化表示學習：從同態與解耦到等變性與拓撲學

Song, Yue, Keller, Thomas Anderson, Sebe, Nicu

Structured Representation Learning: From Homomorphisms and Disentanglement to Equivariance and Topography
  • 出版商: Springer
  • 出版日期: 2025-05-19
  • 售價: $1,700
  • 貴賓價: 9.5 折 $1,615
  • 語言: 英文
  • 頁數: 140
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 3031881109
  • ISBN-13: 9783031881107

    • 海外代購書籍(需單獨結帳)

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商品描述

This book introduces approaches to generalize the benefits of equivariant deep learning to a broader set of learned structures through learned homomorphisms. In the field of machine learning, the idea of incorporating knowledge of data symmetries into artificial neural networks is known as equivariant deep learning and has led to the development of cutting edge architectures for image and physical data processing. The power of these models originates from data-specific structures ingrained in them through careful engineering. To-date however, the ability for practitioners to build such a structure into models is limited to situations where the data must exactly obey specific mathematical symmetries. The authors discuss naturally inspired inductive biases, specifically those which may provide types of efficiency and generalization benefits through what are known as homomorphic representations, a new general type of structured representation inspired from techniques in physics and neuroscience. A review of some of the first attempts at building models with learned homomorphic representations are introduced. The authors demonstrate that these inductive biases improve the ability of models to represent natural transformations and ultimately pave the way to the future of efficient and effective artificial neural networks.

商品描述(中文翻譯)

本書介紹了通過學習同態（learned homomorphisms）將等變深度學習的好處推廣到更廣泛的學習結構的方法。在機器學習領域，將數據對稱性知識納入人工神經網絡的想法被稱為等變深度學習，並促進了針對圖像和物理數據處理的尖端架構的發展。這些模型的力量源於通過精心設計嵌入其中的數據特定結構。然而，迄今為止，從業者在模型中構建此類結構的能力僅限於數據必須完全遵循特定數學對稱性的情況。作者討論了自然啟發的歸納偏見，特別是那些通過所謂的同態表示（homomorphic representations）提供效率和泛化好處的類型，這是一種受到物理學和神經科學技術啟發的新型結構化表示。書中介紹了一些首次嘗試構建具有學習同態表示的模型的案例。作者展示了這些歸納偏見如何改善模型表示自然變換的能力，並最終為未來高效且有效的人工神經網絡鋪平道路。

作者簡介

Yue Song, Ph.D. is a Computing and Mathematical Sciences postdoctoral research fellow at Caltech. He pursued doctoral studies under the European Laboratory for Learning and Intelligent Systems (ELLIS), where he was affiliated with the Multimedia and Human Understanding Group (MHUG) at the University of Trento, Italy, and the Amsterdam Machine Learning Lab (AMLab) at the University of Amsterdam, the Netherlands. He researches structured representation learning, specifically leveraging beneficial inductive biases from scientific disciplines such as math, physics, and neuroscience to improve and explain existing machine learning models.

Thomas Anderson Keller, Ph.D., is a postdoctoral research fellow at the Kempner Institute at Harvard University. He completed his doctorate under the supervision of Max Welling at the University of Amsterdam in the Amsterdam Machine Learning Lab (AMLab). His current research focuses on structured representation learning, probabilistic generative modeling, and biologically plausible learning. His research explores ways to develop deep probabilistic generative models that are meaningfully structured with respect to observed, real-world transformations. In the long term, the goal of Dr. Keller's research is to understand the abstract mechanisms underlying the apparent sample efficiency and generalizability of natural intelligence, and ultimately integrate these into artificially intelligent systems.

Nicu Sebe, Ph.D., is a Professor at the University of Trento, Italy, where he is leading the research in the areas of multimedia analysis and human behavior understanding. He was the general co-chair of the IEEE FG 2008 and ACM Multimedia 2013. He was a program chair of ACM Multimedia 2011 and 2007, ECCV 2016, ICCV 2017, and ICPR 2020, and a general chair of ACM Multimedia 2022. He serves as the Co-Editor in Chief of the Computer Vision and Image Understanding journal. He is a fellow of IAPR and of .the European Lab for Learning and Intelligent Systems (ELLIS).

Max Welling, Ph.D., is a Research Chair in Machine Learning at the University of Amsterdam and a Distinguished Scientist at MSR. He is a Fellow at the Canadian Institute for Advanced Research (CIFAR) and the European Lab for Learning and Intelligent Systems (ELLIS) where he also serves on the founding board. His previous appointments include VP at Qualcomm Technologies, professor at UC Irvine, postdoc at U. Toronto and UCL under the supervision of Prof. Geoffrey Hinton, and postdoc at Caltech under the supervision of Prof. Pietro Perona. He finished his Ph.D. in theoretical high energy physics under the supervision of Nobel laureate Prof. Gerard 't Hooft.

作者簡介(中文翻譯)

岳松博士是加州理工學院計算與數學科學的博士後研究員。他在歐洲學習與智能系統實驗室（ELLIS）進行博士研究，並與意大利特倫托大學的多媒體與人類理解組（MHUG）及荷蘭阿姆斯特丹大學的阿姆斯特丹機器學習實驗室（AMLab）有關聯。他的研究專注於結構化表示學習，特別是利用數學、物理學和神經科學等科學領域的有益歸納偏見來改善和解釋現有的機器學習模型。

托馬斯·安德森·凱勒博士是哈佛大學肯普納研究所的博士後研究員。他在阿姆斯特丹大學的阿姆斯特丹機器學習實驗室（AMLab）完成了在馬克斯·韋林教授指導下的博士學位。他目前的研究重點是結構化表示學習、概率生成建模和生物學上合理的學習。他的研究探索開發深度概率生成模型的方法，這些模型在觀察到的現實世界轉換方面具有有意義的結構。長期來看，凱勒博士的研究目標是理解自然智能表現出的樣本效率和可概括性背後的抽象機制，並最終將這些整合到人工智能系統中。

尼庫·塞貝博士是意大利特倫托大學的教授，負責多媒體分析和人類行為理解領域的研究。他曾擔任IEEE FG 2008和ACM Multimedia 2013的共同主席，並擔任ACM Multimedia 2011和2007、ECCV 2016、ICCV 2017及ICPR 2020的程序主席，以及ACM Multimedia 2022的總主席。他擔任《計算機視覺與圖像理解》期刊的共同主編，並是IAPR及歐洲學習與智能系統實驗室（ELLIS）的研究員。

馬克斯·韋林博士是阿姆斯特丹大學的機器學習研究主席及微軟研究院的傑出科學家。他是加拿大高級研究所（CIFAR）和歐洲學習與智能系統實驗室（ELLIS）的研究員，並在該實驗室的創始董事會中任職。他之前的職位包括高通技術公司的副總裁、加州大學爾灣分校的教授、多倫多大學和倫敦大學學院的博士後研究員（在喬弗瑞·辛頓教授的指導下），以及在加州理工學院的博士後研究員（在皮耶特羅·佩羅納教授的指導下）。他在諾貝爾獎得主赫拉德·'t Hooft教授的指導下完成了理論高能物理學的博士學位。

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