Preface

Alexander Cloninger, Timothy Doster, Tegan Emerson, Manohar Kaul, Ira Ktena, Henry Kvinge, Nina Miolane, Bastian Rice, Sarah Tymochko, Guy Wolf
Proceedings of Topological, Algebraic, and Geometric Learning Workshops 2022, PMLR 196:1-5, 2022.

Abstract

The deep learning revolution has provided us with resounding successes in different domains, such as image analysis. Despite initial claims to the contrary, however, the last years showed a dire need to understand and describe fundamental aspects of modern machine learning models. In this context, algebra, geometry, and topology offer a veritable cornucopia of different methods, ranging from the description of the boundary of neural network architectures to the development of more expressive models for graph learning, for example. There are few cross-pollination efforts between the machine learning community and the mathematical community at large. The papers in this collection represent two such efforts; all of them have been originally submitted to either the ICML Workshop on Topology, Algebra, and Geometry in Machine Learning or to the ICLR Workshop on Geometrical and Topological Representation Learning. We hope that this collection demonstrates to the reader the benefits of a more fundamental perspective on machine learning, and we hope that this will constitute the first of many such collections.

Cite this Paper

BibTeX
@InProceedings{pmlr-v196-cloninger22a, title = {Preface}, author = {Cloninger, Alexander and Doster, Timothy and Emerson, Tegan and Kaul, Manohar and Ktena, Ira and Kvinge, Henry and Miolane, Nina and Rice, Bastian and Tymochko, Sarah and Wolf, Guy}, booktitle = {Proceedings of Topological, Algebraic, and Geometric Learning Workshops 2022}, pages = {1--5}, year = {2022}, editor = {Cloninger, Alexander and Doster, Timothy and Emerson, Tegan and Kaul, Manohar and Ktena, Ira and Kvinge, Henry and Miolane, Nina and Rieck, Bastian and Tymochko, Sarah and Wolf, Guy}, volume = {196}, series = {Proceedings of Machine Learning Research}, month = {25 Feb--22 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v196/cloninger22a/cloninger22a.pdf}, url = {https://proceedings.mlr.press/v196/cloninger22a.html}, abstract = {The deep learning revolution has provided us with resounding successes in different domains, such as image analysis. Despite initial claims to the contrary, however, the last years showed a dire need to understand and describe fundamental aspects of modern machine learning models. In this context, algebra, geometry, and topology offer a veritable cornucopia of different methods, ranging from the description of the boundary of neural network architectures to the development of more expressive models for graph learning, for example. There are few cross-pollination efforts between the machine learning community and the mathematical community at large. The papers in this collection represent two such efforts; all of them have been originally submitted to either the ICML Workshop on Topology, Algebra, and Geometry in Machine Learning or to the ICLR Workshop on Geometrical and Topological Representation Learning. We hope that this collection demonstrates to the reader the benefits of a more fundamental perspective on machine learning, and we hope that this will constitute the first of many such collections.} }
Endnote
%0 Conference Paper %T Preface %A Alexander Cloninger %A Timothy Doster %A Tegan Emerson %A Manohar Kaul %A Ira Ktena %A Henry Kvinge %A Nina Miolane %A Bastian Rice %A Sarah Tymochko %A Guy Wolf %B Proceedings of Topological, Algebraic, and Geometric Learning Workshops 2022 %C Proceedings of Machine Learning Research %D 2022 %E Alexander Cloninger %E Timothy Doster %E Tegan Emerson %E Manohar Kaul %E Ira Ktena %E Henry Kvinge %E Nina Miolane %E Bastian Rieck %E Sarah Tymochko %E Guy Wolf %F pmlr-v196-cloninger22a %I PMLR %P 1--5 %U https://proceedings.mlr.press/v196/cloninger22a.html %V 196 %X The deep learning revolution has provided us with resounding successes in different domains, such as image analysis. Despite initial claims to the contrary, however, the last years showed a dire need to understand and describe fundamental aspects of modern machine learning models. In this context, algebra, geometry, and topology offer a veritable cornucopia of different methods, ranging from the description of the boundary of neural network architectures to the development of more expressive models for graph learning, for example. There are few cross-pollination efforts between the machine learning community and the mathematical community at large. The papers in this collection represent two such efforts; all of them have been originally submitted to either the ICML Workshop on Topology, Algebra, and Geometry in Machine Learning or to the ICLR Workshop on Geometrical and Topological Representation Learning. We hope that this collection demonstrates to the reader the benefits of a more fundamental perspective on machine learning, and we hope that this will constitute the first of many such collections.
APA
Cloninger, A., Doster, T., Emerson, T., Kaul, M., Ktena, I., Kvinge, H., Miolane, N., Rice, B., Tymochko, S. & Wolf, G.. (2022). Preface. Proceedings of Topological, Algebraic, and Geometric Learning Workshops 2022, in Proceedings of Machine Learning Research 196:1-5 Available from https://proceedings.mlr.press/v196/cloninger22a.html.

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