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Found 8 papers, 7 papers with code
Graph Neural Networks for Learning Equivariant Representations of Neural Networks
Neural networks that process the parameters of other neural networks find applications in domains as diverse as classifying implicit neural representations, generating neural network weights, and predicting generalization errors.
Data Augmentations in Deep Weight Spaces
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 fields, from analyzing and editing neural fields and implicit neural representations, to network pruning and quantization.
Unlocking Slot Attention by Changing Optimal Transport Costs
Slot attention is a powerful method for object-centric modeling in images and videos.
Self-Guided Diffusion Models
Diffusion models have demonstrated remarkable progress in image generation quality, especially when guidance is used to control the generative process.
Maximum Class Separation as Inductive Bias in One Matrix
This paper proposes a simple alternative: encoding maximum separation as an inductive bias in the network by adding one fixed matrix multiplication before computing the softmax activations.
Multiset-Equivariant Set Prediction with Approximate Implicit Differentiation
Most set prediction models in deep learning use set-equivariant operations, but they actually operate on multisets.
Pruning Edges and Gradients to Learn Hypergraphs from Larger Sets
We address two common scaling problems encountered in set-to-hypergraph tasks that limit the size of the input set: the exponentially growing number of hyperedges and the run-time complexity, both leading to higher memory requirements.
Set Prediction without Imposing Structure as Conditional Density Estimation
In this paper, we propose an alternative to training via set losses by viewing learning as conditional density estimation.
