Preface to Geometry-grounded Representation Learning and Generative Modeling (GRaM) Workshop

Sharvaree Vadgama; Erik Bekkers; Alison Pouplin; Sekou-Oumar Kaba; Robin Walters; Hannah Lawrence; Tegan Emerson; Henry Kvinge; Jakub Tomczak; Stephanie Jegelka

Preface to Geometry-grounded Representation Learning and Generative Modeling (GRaM) Workshop

Sharvaree Vadgama, Erik Bekkers, Alison Pouplin, Sekou-Oumar Kaba, Robin Walters, Hannah Lawrence, Tegan Emerson, Henry Kvinge, Jakub Tomczak, Stephanie Jegelka

Proceedings of the Geometry-grounded Representation Learning and Generative Modeling Workshop (GRaM), PMLR 251:1-6, 2024.

Abstract

The Geometry-grounded Representation Learning and Generative Modeling (GRaM) workshop at ICLR 2024 explored the concept of geometric grounding. A representation, method, or theory is grounded in geometry if it can be amenable to geometric reasoning, that is, it abides by the mathematics of geometry. This idea plays a crucial role in developing generative models that understand geometry and can aid in geometric representations. We explored many different aspects of geometric representations at the GRaM Workshop.

Cite this Paper

BibTeX

@InProceedings{pmlr-v251-vadgama24a,
  title = 	 {Preface to Geometry-grounded Representation Learning and Generative Modeling (GRaM) Workshop},
  author =       {Vadgama, Sharvaree and Bekkers, Erik and Pouplin, Alison and Kaba, Sekou-Oumar and Walters, Robin and Lawrence, Hannah and Emerson, Tegan and Kvinge, Henry and Tomczak, Jakub and Jegelka, Stephanie},
  booktitle = 	 {Proceedings of the Geometry-grounded Representation Learning and Generative Modeling Workshop (GRaM)},
  pages = 	 {1--6},
  year = 	 {2024},
  editor = 	 {Vadgama, Sharvaree and Bekkers, Erik and Pouplin, Alison and Kaba, Sekou-Oumar and Walters, Robin and Lawrence, Hannah and Emerson, Tegan and Kvinge, Henry and Tomczak, Jakub and Jegelka, Stephanie},
  volume = 	 {251},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {29 Jul},
  publisher =    {PMLR},
  pdf = 	 {https://raw.githubusercontent.com/mlresearch/v251/main/assets/vadgama24a/vadgama24a.pdf},
  url = 	 {https://proceedings.mlr.press/v251/vadgama24a.html},
  abstract = 	 {The Geometry-grounded Representation Learning and Generative Modeling (GRaM) workshop at ICLR 2024 explored the concept of geometric grounding. A representation, method, or theory is grounded in geometry if it can be amenable to geometric reasoning, that is, it abides by the mathematics of geometry. This idea plays a crucial role in developing generative models that understand geometry and can aid in geometric representations. We explored many different aspects of geometric representations at the GRaM Workshop.}
}

Endnote

%0 Conference Paper
%T Preface to Geometry-grounded Representation Learning and Generative Modeling (GRaM) Workshop
%A Sharvaree Vadgama
%A Erik Bekkers
%A Alison Pouplin
%A Sekou-Oumar Kaba
%A Robin Walters
%A Hannah Lawrence
%A Tegan Emerson
%A Henry Kvinge
%A Jakub Tomczak
%A Stephanie Jegelka
%B Proceedings of the Geometry-grounded Representation Learning and Generative Modeling Workshop (GRaM)
%C Proceedings of Machine Learning Research
%D 2024
%E Sharvaree Vadgama
%E Erik Bekkers
%E Alison Pouplin
%E Sekou-Oumar Kaba
%E Robin Walters
%E Hannah Lawrence
%E Tegan Emerson
%E Henry Kvinge
%E Jakub Tomczak
%E Stephanie Jegelka	
%F pmlr-v251-vadgama24a
%I PMLR
%P 1--6
%U https://proceedings.mlr.press/v251/vadgama24a.html
%V 251
%X The Geometry-grounded Representation Learning and Generative Modeling (GRaM) workshop at ICLR 2024 explored the concept of geometric grounding. A representation, method, or theory is grounded in geometry if it can be amenable to geometric reasoning, that is, it abides by the mathematics of geometry. This idea plays a crucial role in developing generative models that understand geometry and can aid in geometric representations. We explored many different aspects of geometric representations at the GRaM Workshop.

APA

Vadgama, S., Bekkers, E., Pouplin, A., Kaba, S., Walters, R., Lawrence, H., Emerson, T., Kvinge, H., Tomczak, J. & Jegelka, S.. (2024). Preface to Geometry-grounded Representation Learning and Generative Modeling (GRaM) Workshop. Proceedings of the Geometry-grounded Representation Learning and Generative Modeling Workshop (GRaM), in Proceedings of Machine Learning Research 251:1-6 Available from https://proceedings.mlr.press/v251/vadgama24a.html.

Related Material

Download PDF