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Found 6 papers, 0 papers with code
SAFE: Machine Unlearning With Shard Graphs
We present Synergy Aware Forgetting Ensemble (SAFE), a method to adapt large models on a diverse collection of data while minimizing the expected cost to remove the influence of training samples from the trained model.
Learning Expressive Prompting With Residuals for Vision Transformers
Prompt learning is an efficient approach to adapt transformers by inserting learnable set of parameters into the input and intermediate representations of a pre-trained model.
DIVA: Dataset Derivative of a Learning Task
A learning task is a function from a training set to the validation error, which can be represented by a trained deep neural network (DNN).
Optimization Theory for ReLU Neural Networks Trained with Normalization Layers
The success of deep neural networks is in part due to the use of normalization layers.
On the Dynamics and Convergence of Weight Normalization for Training Neural Networks
We present a proof of convergence for ReLU networks trained with weight normalization.
Wasserstein Diffusion Tikhonov Regularization
We propose regularization strategies for learning discriminative models that are robust to in-class variations of the input data.
