2025
GeoLoRA: Geometric integration for parameter efficient fine-tuning
International Conference on Learning Representations (ICLR), 2025
Low-Rank Adaptation (LoRA) has become a widely used method for parameter-efficient fine-tuning of large-scale, pre-trained neural networks. However, LoRA and its extensions face several challenges, including the need for rank adaptivity, robustness, and computational efficiency during the fine-tuning process. We …
2024
Geometry-aware training of factorized layers in tensor Tucker format
Advances in Neural Information Processing Systems (NeurIPS), 2024
Reducing parameter redundancies in neural network architectures is crucial for achieving feasible computational and memory requirements during training and inference phases. Given its easy implementation and flexibility, one promising approach is layer factorization, which reshapes weight tensors into a matrix format …
2023
Robust low-rank training via approximate orthonormal constraints
Advances in Neural Information Processing Systems (NeurIPS), 2023
With the growth of model and data sizes, a broad effort has been made to design pruning techniques that reduce the resource demand of deep learning pipelines, while retaining model performance. In order to reduce both inference and training costs, a prominent line of work uses low-rank matrix factorizations to …
2022
Low-rank lottery tickets: finding efficient low-rank neural networks via matrix differential equations
Advances in Neural Information Processing Systems (NeurIPS), 2022
Neural networks have achieved tremendous success in a large variety of applications. However, their memory footprint and computational demand can render them impractical in application settings with limited hardware or energy resources. In this work, we propose a novel algorithm to find efficient low-rank subnetworks. …