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Found 9 papers, 6 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.
Improved Generalization of Weight Space Networks via Augmentations
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 fields (INRs, NeRFs), as well as making inferences about other types of neural networks.
Diffusing More Objects for Semi-Supervised Domain Adaptation with Less Labeling
For object detection, it is possible to view the prediction of bounding boxes as a reverse diffusion process.
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.
Robust Scheduling with GFlowNets
Finding the best way to schedule operations in a computation graph is a classical NP-hard problem which is central to compiler optimization.
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.
