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LiNeS: Post-training layer scaling prevents forgetting and enhances model merging

Ke Wang, Nikolaos Dimitriadis, Alessandro Favero, Guillermo Ortiz-Jimenez, Francois Fleuret, Pascal Frossard

Pareto Low-Rank Adapters: Efficient Multi-Task Learning with Preferences

Nikolaos Dimitriadis, Pascal Frossard, Francois Fleuret

Localizing Task Information for Improved Model Merging and Compression

Ke Wang, Nikolaos Dimitriadis, Guillermo Ortiz-Jimenez, Francois Fleuret, Pascal Frossard

Pareto Manifold Learning: Tackling Multiple Tasks via Ensembles of Single-Task Models

We present a novel approach to multi-task learning, that utilizes weight ensembles to produce a continuous Pareto Front in a single training run.

Nikolaos Dimitriadis, Pascal Frossard, Francois Fleuret

Pareto Manifold Learning: Tackling Multiple Tasks via Ensembles of Single-Task Models

SequeL: A Continual Learning Library in PyTorch and JAX

Nikolaos Dimitriadis, Francois Fleuret, Pascal Frossard

U-Boost NAS: Utilization-boosted Differentiable Neural Architecture Search

Optimizing resource utilization in target platforms is key to achieving high performance during DNN inference. While optimizations have …

Ahmet Caner Yüzügüler, Nikolaos Dimitriadis, Pascal Frossard

U-Boost NAS: Utilization-boosted Differentiable Neural Architecture Search

Advances in Morphological Neural Networks: Training, Pruning and Enforcing Shape Constraints

In this paper, we study an emerging class of neural networks, the Morphological Neural networks, from some modern perspectives. Our …

Nikolaos Dimitriadis, Petros Maragos