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Found 7 papers, 1 papers with code
Diffusion-based Neural Network Weights Generation
By learning the distribution of a neural network on a variety pretrained models, our approach enables adaptive sampling weights for unseen datasets achieving faster convergence and reaching competitive performance.
Set-based Neural Network Encoding
We propose an approach to neural network weight encoding for generalization performance prediction that utilizes set-to-set and set-to-vector functions to efficiently encode neural network parameters.
Scalable Set Encoding with Universal Mini-Batch Consistency and Unbiased Full Set Gradient Approximation
Recent work on mini-batch consistency (MBC) for set functions has brought attention to the need for sequentially processing and aggregating chunks of a partitioned set while guaranteeing the same output for all partitions.
Distortion-Aware Network Pruning and Feature Reuse for Real-time Video Segmentation
Real-time video segmentation is a crucial task for many real-world applications such as autonomous driving and robot control.
Set-based Meta-Interpolation for Few-Task Meta-Learning
Recently, several task augmentation methods have been proposed to tackle this issue using domain-specific knowledge to design augmentation techniques to densify the meta-training task distribution.
Mini-Batch Consistent Slot Set Encoder for Scalable Set Encoding
The proposed method adheres to the required symmetries of invariance and equivariance as well as maintaining MBC for any partition of the input set.
Set Based Stochastic Subsampling
Deep models are designed to operate on huge volumes of high dimensional data such as images.
