3 citations · 10 across the 11 of their papers we have counts for
7 papers · 1 filter
Graph Neural Networks for Learning Equivariant Representations of Neural Networks
Miltiadis Kofinas, Boris Knyazev, Yan Zhang +5
Neural networks that process the parameters of other neural networks find applications in domains as diverse as classifying implicit neural representations, generating neural netwo…
Improved Generalization of Weight Space Networks via Augmentations
Aviv Shamsian, Aviv Navon, David W. Zhang +4
Learning in deep weight spaces (DWS), where neural networks process the weights of other neural networks, is an emerging research direction, with applications to 2D and 3D neural f…
Data Augmentations in Deep Weight Spaces
Aviv Shamsian, David W. Zhang, Aviv Navon +10
Learning in weight spaces, where neural networks process the weights of other deep neural networks, has emerged as a promising research direction with applications in various field…
Unlocking Slot Attention by Changing Optimal Transport Costs
Yan Zhang, David W. Zhang, Simon Lacoste-Julien +2
Slot attention is a powerful method for object-centric modeling in images and videos. However, its set-equivariance limits its ability to handle videos with a dynamic number of obj…
Multiset-Equivariant Set Prediction with Approximate Implicit Differentiation
Yan Zhang, David W. Zhang, Simon Lacoste-Julien +2
Most set prediction models in deep learning use set-equivariant operations, but they actually operate on multisets. We show that set-equivariant functions cannot represent certain…
Pruning Edges and Gradients to Learn Hypergraphs from Larger Sets
David W. Zhang, Gertjan J. Burghouts, Cees G. M. Snoek
This paper aims for set-to-hypergraph prediction, where the goal is to infer the set of relations for a given set of entities. This is a common abstraction for applications in part…
Set Prediction without Imposing Structure as Conditional Density Estimation
David W. Zhang, Gertjan J. Burghouts, Cees G. M. Snoek
Set prediction is about learning to predict a collection of unordered variables with unknown interrelations. Training such models with set losses imposes the structure of a metric…