3 citations · 8 across the 11 of their papers we have counts for
12 papers
Model soups need only one ingredient
Alireza Abdollahpoorrostam, Nikolaos Dimitriadis, Adam Hazimeh +1
Fine-tuning large pre-trained models on a target distribution often improves in-distribution (ID) accuracy, but at the cost of out-of-distribution (OOD) robustness as representatio…
MEMOIR: Lifelong Model Editing with Minimal Overwrite and Informed Retention for LLMs
Ke Wang, Yiming Qin, Nikolaos Dimitriadis +2
Language models deployed in real-world systems often require post-hoc updates to incorporate new or corrected knowledge. However, editing such models efficiently and reliably-witho…
Single-Input Multi-Output Model Merging: Leveraging Foundation Models for Dense Multi-Task Learning
Juan Garcia Giraldo, Nikolaos Dimitriadis, Ke Wang +1
Model merging is a flexible and computationally tractable approach to merge single-task checkpoints into a multi-task model. Prior work has solely focused on constrained multi-task…
LiNeS: Post-training Layer Scaling Prevents Forgetting and Enhances Model Merging
Ke Wang, Nikolaos Dimitriadis, Alessandro Favero +3
Fine-tuning pre-trained models has become the standard approach to endow them with specialized knowledge, but it poses fundamental challenges. In particular, \textit{(i)} fine-tuni…
Pareto Low-Rank Adapters: Efficient Multi-Task Learning with Preferences
Nikolaos Dimitriadis, Pascal Frossard, Francois Fleuret
Multi-task trade-offs in machine learning can be addressed via Pareto Front Learning (PFL) methods that parameterize the Pareto Front (PF) with a single model. PFL permits to selec…
Localizing Task Information for Improved Model Merging and Compression
Ke Wang, Nikolaos Dimitriadis, Guillermo Ortiz-Jimenez +2
Model merging and task arithmetic have emerged as promising scalable approaches to merge multiple single-task checkpoints to one multi-task model, but their applicability is reduce…
Flexible Channel Dimensions for Differentiable Architecture Search
Ahmet Caner Yüzügüler, Nikolaos Dimitriadis, Pascal Frossard
Finding optimal channel dimensions (i.e., the number of filters in DNN layers) is essential to design DNNs that perform well under computational resource constraints. Recent work i…
SequeL: A Continual Learning Library in PyTorch and JAX
Nikolaos Dimitriadis, Francois Fleuret, Pascal Frossard
Continual Learning is an important and challenging problem in machine learning, where models must adapt to a continuous stream of new data without forgetting previously acquired kn…
Pareto Manifold Learning: Tackling multiple tasks via ensembles of single-task models
Nikolaos Dimitriadis, Pascal Frossard, François Fleuret
In Multi-Task Learning (MTL), tasks may compete and limit the performance achieved on each other, rather than guiding the optimization to a solution, superior to all its single-tas…
U-Boost NAS: Utilization-Boosted Differentiable Neural Architecture Search
Ahmet Caner Yüzügüler, Nikolaos Dimitriadis, Pascal Frossard
Optimizing resource utilization in target platforms is key to achieving high performance during DNN inference. While optimizations have been proposed for inference latency, memory…
The Theoretical Expressiveness of Maxpooling
Kyle Matoba, Nikolaos Dimitriadis, François Fleuret
Over the decade since deep neural networks became state of the art image classifiers there has been a tendency towards less use of max pooling: the function that takes the largest…
Advances in the training, pruning and enforcement of shape constraints of Morphological Neural Networks using Tropical Algebra
Nikolaos Dimitriadis, Petros Maragos
In this paper we study an emerging class of neural networks based on the morphological operators of dilation and erosion. We explore these networks mathematically from a tropical g…