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December 3rd, 2022
New Orleans Convention Center
December 3rd, 2022
New Orleans Convention Center
Talks and Panels
Rooms 283 - 285
Talks and Panels
Rooms 283 - 285
Please note that the Poster Session will be held in Ballroom A/B
Please note that the Poster Session will be held in Ballroom A/B
8:15 - 8:30
Opening Remarks
Opening Remarks
Sophia Sanborn
Session 1:
Symmetry and Laws of Neural Representation
Session 1:
Symmetry and Laws of Neural Representation
8:30 - 9:00
In search of invariance in brains and machines
In search of invariance in brains and machines
Bruno Olshausen
9:00 - 9:30
Symmetry-based representations for artificial and biological intelligence
Symmetry-based representations for artificial and biological intelligence
Irina Higgins
9:30 - 10:00
From equivariance to naturality
From equivariance to naturality
Taco Cohen
10:00 - 10:30
Coffee Break
Coffee Break
Contributed Talks
Contributed Talks
10:30 - 10:40
Is the information geometry of probabilistic population codes learnable?
Is the information geometry of probabilistic population codes learnable?
Vastola, Cohen, Drugowitsch
10:40 - 10:50
Computing Representations for Lie Algebraic Networks
Computing Representations for Lie Algebraic Networks
Shutty, Wierzynski
10:50 - 11:00
Kendall Shape-VAE : Learning Shapes in a Generative Framework
Kendall Shape-VAE : Learning Shapes in a Generative Framework
Vadgama, Tomczak, Bekkers
11:00 - 11:05
Equivariance with Learned Canonical Mappings
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?
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?
Do Neural Networks Trained with Topological Features Learn Different Internal Representations?
McGuire, Jackson, Emerson, Kvinge
11:15 - 11:20
Expander Graph Propagation
Expander Graph Propagation
Deac, Lackenby, Veličković
11:20 - 11:25
Homomorphism AutoEncoder ---
Learning Group Structured Representations from Observed Transitions
Homomorphism AutoEncoder ---
Learning Group Structured Representations from Observed Transitions
Keurti, Pan, Besserve, Grewe, Schölkopf
11:25 - 11:30
Sheaf Attention Networks
Sheaf Attention Networks
Barbero, Bodnar, Sáez de Ocáriz Borde, Lió
11:30 - 11:35
On the Expressive Power of Geometric Graph Neural Networks
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
Panel Discussion I:
Geometric and topological principles for representation learning in ML
11:35 - 12:05
Panelists
Panelists
Irina Higgins, Taco Cohen, Erik Bekkers, Rose Yu
Moderator
Moderator
Nina Miolane
12:05 - 1:30
Lunch Break
Lunch Break
Session II:
Latent Geometry in Neural Systems
Session II:
Latent Geometry in Neural Systems
1:30 - 2:00
Generative models of non-Euclidean neural population dynamics
Generative models of non-Euclidean neural population dynamics
Kristopher Jensen
2:00 - 2:30
Robustness of representations in artificial and biological neural networks
Robustness of representations in artificial and biological neural networks
Gabriel Kreiman
2:30 - 3:00
Neural Ideograms and Equivariant Representation Learning
Neural Ideograms and Equivariant Representation Learning
Erik Bekkers
Panel Discussion II:
Geometric and topological principles for representations in the brain
Panel Discussion II:
Geometric and topological principles for representations in the brain
3:00 - 3:30
Panelists
Panelists
Bruno Olshausen, Kristopher Jensen, Gabriel Krieman, Manu Madhav
Moderator
Moderator
Christian Shewmake
Poster Session
Ballroom A/B
Poster Session
Ballroom A/B
3:30 - 5:00
Poster Session
Poster Session
Contributing Authors
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