Search Results for author:
Found 6 papers, 3 papers with code
BrainWash: A Poisoning Attack to Forget in Continual Learning
Continual learning has gained substantial attention within the deep learning community, offering promising solutions to the challenging problem of sequential learning.
NOLA: Compressing LoRA using Linear Combination of Random Basis
These methods can reduce the number of parameters needed to fine-tune an LLM by several orders of magnitude.
PRANC: Pseudo RAndom Networks for Compacting deep models
We demonstrate that a deep model can be reparametrized as a linear combination of several randomly initialized and frozen deep models in the weight space.
Sparsity and Heterogeneous Dropout for Continual Learning in the Null Space of Neural Activations
Overcoming catastrophic forgetting in deep neural networks has become an active field of research in recent years.
A Simple Baseline for Low-Budget Active Learning
However, most such methods assume that a large subset of the data can be annotated.
TaxoNN: A Light-Weight Accelerator for Deep Neural Network Training
In this work, first we present a novel approach to add the training ability to a baseline DNN accelerator (inference only) by splitting the SGD algorithm into simple computational elements.
