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Found 6 papers, 4 papers with code
Pairwise Margin Maximization for Deep Neural Networks
The weight decay regularization term is widely used during training to constrain expressivity, avoid overfitting, and improve generalization.
Variance Pruning: Pruning Language Models via Temporal Neuron Variance
As language models become larger, different pruning methods have been proposed to reduce model size.
MixSize: Training Convnets With Mixed Image Sizes for Improved Accuracy, Speed and Scale Resiliency
Although trained on images of a specific size, it is well established that CNNs can be used to evaluate a wide range of image sizes at test time, by adjusting the size of intermediate feature maps.
Margin-Based Regularization and Selective Sampling in Deep Neural Networks
We derive a new margin-based regularization formulation, termed multi-margin regularization (MMR), for deep neural networks (DNNs).
Selective sampling for accelerating training of deep neural networks
We present a selective sampling method designed to accelerate the training of deep neural networks.
Mix & Match: training convnets with mixed image sizes for improved accuracy, speed and scale resiliency
Although trained on images of aspecific size, it is well established that CNNs can be used to evaluate a wide range of image sizes at test time, by adjusting the size of intermediate feature maps.
