Past Workshops

NeurReps 2022

The first NeurReps Workshop was held at NeurIPS 2022 on December 3rd in New Orleans. The workshop featured six invited talks, eleven contributed talks, two discussion panels, and 67 accepted submissions presented as posters. All recordings of the day's talks, as well as accepted papers and abstracts, can be found here.

Invited Speakers & Panelists

Bruno Olshausen

UC Berkeley

Irina Higgins


Taco Cohen


Erik Bekkers


Rose Yu

UC San Diego

Kristopher Jensen


Gabriel Kreiman


Manu Madhav



8:15 - 8:30

Opening Remarks

Sophia Sanborn 

Session 1:
Symmetry and Laws of Neural Representation

8:30 - 9:00

In search of invariance in brains and machines

Bruno Olshausen

9:00 - 9:30

Symmetry-based representations for artificial and biological intelligence

Irina Higgins

9:30 - 10:00

From equivariance to naturality

Taco Cohen

10:00 - 10:30

Coffee Break

Contributed Talks

10:30 - 10:40

Is the information geometry of probabilistic population codes learnable?

Vastola, Cohen, Drugowitsch

10:40 - 10:50

Computing Representations for Lie Algebraic Networks

Shutty, Wierzynski

10:50 - 11:00

Kendall Shape-VAE : Learning Shapes in a Generative Framework

Vadgama, Tomczak, Bekkers

11:00 - 11:05

Equivariance with Learned Canonical Mappings

Kaba, Mondal, Zhang, Bengio, Ravanbakhsh

11:05 - 11:10

Capacity of Group-invariant Linear Readouts from Equivariant Representations:
How Many Objects can be Linearly Classified Under All Possible Views?

Farrell, Bordelon, Trivedi, Pehlevan

11:10 - 11:15

Do Neural Networks Trained with Topological Features Learn Different Internal Representations?

McGuire, Jackson, Emerson, Kvinge

11:15 - 11:20

Expander Graph Propagation

Deac, Lackenby, Veličković

11:20 - 11:25

Homomorphism AutoEncoder ---
Learning Group Structured Representations from Observed Transitions

Keurti, Pan, Besserve, Grewe, Schölkopf

11:25 - 11:30

Sheaf Attention Networks

Barbero, Bodnar, Sáez de Ocáriz Borde, Lió

11:30 - 11:35

On the Expressive Power of Geometric Graph Neural Networks

Joshi, Bodnar, Mathis, Cohen, Liò

Panel Discussion I:
Geometric and topological principles for representation learning in ML

11:35 - 12:05


Irina Higgins, Taco Cohen, Erik Bekkers, Rose Yu


Nina Miolane

12:05 - 1:30

Lunch Break

Session II:
Latent Geometry in Neural Systems

1:30 - 2:00

Generative models of non-Euclidean neural population dynamics

Kristopher Jensen

2:00 - 2:30

Robustness of representations in artificial and biological neural networks

Gabriel Kreiman

2:30 - 3:00

Neural Ideograms and Equivariant Representation Learning

Erik Bekkers

Panel Discussion II:
Geometric and topological principles for representations in the brain

3:00 - 3:30


Bruno Olshausen, Kristopher Jensen, Gabriel Krieman, Manu Madhav


Christian Shewmake

Poster Session
Ballroom A/B

3:30 - 5:00

Poster Session

Contributing Authors