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

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.

Yue Song, Ph.D. 是加州理工學院的計算與數學科學博士後研究員。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。

Thomas Anderson Keller, Ph.D. 是哈佛大學Kempner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Max Welling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。他的研究探索如何開發與觀察到的現實世界轉變有意義結構的深度概率生成模型。長期來看，Keller博士的研究目標是理解自然智能表面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。

Nicu Sebe, Ph.D. 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。他是《計算機視覺與圖像理解》期刊的共同主編，也是IAPR和歐洲學習與智能系統實驗室（ELLIS）的成員。

Max Welling, Ph.D. 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ELLIS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、在多倫多大學和倫敦大學學院（UCL）在Geoffrey Hinton教授的指導下的博士後研究員，以及在加州理工學院在Pietro Perona教授的指導下的博士後研究員。他在諾貝爾獎得主Gerard 't Hooft教授的指導下完成了理論高能物理的博士學位。