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Found 7 papers, 1 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.
À-la-carte Prompt Tuning (APT): Combining Distinct Data Via Composable Prompting
During inference, models can be assembled based on arbitrary selections of data sources, which we call "\`a-la-carte learning".
A-La-Carte Prompt Tuning (APT): Combining Distinct Data via Composable Prompting
During inference, models can be assembled based on arbitrary selections of data sources, which we call a-la-carte learning.
Characterizing the Spectrum of the NTK via a Power Series Expansion
We provide expressions for the coefficients of this power series which depend on both the Hermite coefficients of the activation function as well as the depth of the network.
Spectral Bias Outside the Training Set for Deep Networks in the Kernel Regime
This bias depends on the model architecture and input distribution alone and thus does not depend on the target function which does not need to be in the RKHS of the kernel.
Implicit Bias of MSE Gradient Optimization in Underparameterized Neural Networks
We study the dynamics of a neural network in function space when optimizing the mean squared error via gradient flow.
PicoDomain: A Compact High-Fidelity Cybersecurity Dataset
Analysis of cyber relevant data has become an area of increasing focus.
