20 citations · 39 across the 9 of their papers we have counts for
5 papers · 1 filter
TailLoR: Protecting Principal Components in Parameter-Efficient Continual Learning
Marius Dragoi, Ioana Pintilie, Alexandra Dragomir +2
Parameter-efficient finetuning methods based on spectral decomposition have enabled progress in Continual Learning. In this paper we introduce TailLoR, which utilizes the singular…
JumpLoRA: Sparse Adapters for Continual Learning in Large Language Models
Alexandra Dragomir, Ioana Pintilie, Antonio Barbalau +6
Adapter-based methods have become a cost-effective approach to continual learning (CL) for Large Language Models (LLMs), by sequentially learning a low-rank update matrix for each…
Rethinking Sparse Autoencoders: Select-and-Project for Fairness and Control from Encoder Features Alone
Antonio Bărbălau, Cristian Daniel Păduraru, Teodor Poncu +2
Sparse Autoencoders (SAEs) are widely employed for mechanistic interpretability and model steering. Within this context, steering is by design performed by means of decoding altere…
Curriculum-enhanced GroupDRO: Challenging the Norm of Avoiding Curriculum Learning in Subpopulation Shift Setups
Antonio Barbalau
In subpopulation shift scenarios, a Curriculum Learning (CL) approach would only serve to imprint the model weights, early on, with the easily learnable spurious correlations featu…
A Generic and Model-Agnostic Exemplar Synthetization Framework for Explainable AI
Antonio Barbalau, Adrian Cosma, Radu Tudor Ionescu +1
With the growing complexity of deep learning methods adopted in practical applications, there is an increasing and stringent need to explain and interpret the decisions of such met…