:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Zaccone, Riccardo, Laskaridis, Stefanos, Ciccone, Marco, Horváth, Samuel
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2602.02680
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Maestro: Uncovering Low-Rank Structures via Trainable Decomposition
by: Horvath, Samuel, et al.
Published: (2023)

LoFT: Low-Rank Adaptation That Behaves Like Full Fine-Tuning
by: Tastan, Nurbek, et al.
Published: (2025)

MoSE: Mixture of Slimmable Experts for Efficient and Adaptive Language Models
by: Tastan, Nurbek, et al.
Published: (2026)

Communication-Efficient Heterogeneous Federated Learning with Generalized Heavy-Ball Momentum
by: Zaccone, Riccardo, et al.
Published: (2023)

NoEsis: Differentially Private Knowledge Transfer in Modular LLM Adaptation
by: Romijnders, Rob, et al.
Published: (2025)

MELTing point: Mobile Evaluation of Language Transformers
by: Laskaridis, Stefanos, et al.
Published: (2024)

FlexLoRA: Entropy-Guided Flexible Low-Rank Adaptation
by: Liu, Muqing, et al.
Published: (2026)

NoRA: Nested Low-Rank Adaptation for Efficient Fine-Tuning Large Models
by: Lin, Cheng, et al.
Published: (2024)

CALR: Corrective Adaptive Low-Rank Decomposition for Efficient Large Language Model Layer Compression
by: Kautsar, Muchammad Daniyal, et al.
Published: (2025)

Operator SVD with Neural Networks via Nested Low-Rank Approximation
by: Ryu, J. Jon, et al.
Published: (2024)

Decoupling General and Personalized Knowledge in Federated Learning via Additive and Low-Rank Decomposition
by: Wu, Xinghao, et al.
Published: (2024)

Adaptive Anomaly Detection in Network Flows with Low-Rank Tensor Decompositions and Deep Unrolling
by: Schynol, Lukas, et al.
Published: (2024)

The Future of Consumer Edge-AI Computing
by: Laskaridis, Stefanos, et al.
Published: (2022)

Low-Rank Tensor Decompositions for the Theory of Neural Networks
by: Borsoi, Ricardo, et al.
Published: (2025)

MAP: Revisiting Weight Decomposition for Low-Rank Adaptation
by: Si, Chongjie, et al.
Published: (2025)

Optimal Brain Decomposition for Accurate LLM Low-Rank Approximation
by: Li, Yuhang, et al.
Published: (2026)

Importance-Guided Basis Selection for Low-Rank Decomposition of Large Language Models
by: Asante, Daniel Agyei, et al.
Published: (2026)

Initialization using Update Approximation is a Silver Bullet for Extremely Efficient Low-Rank Fine-Tuning
by: Ponkshe, Kaustubh, et al.
Published: (2024)

Compressing Large Language Models using Low Rank and Low Precision Decomposition
by: Saha, Rajarshi, et al.
Published: (2024)

Low-Rank Quantization-Aware Training for LLMs
by: Bondarenko, Yelysei, et al.
Published: (2024)

Towards Understanding the Nature of Attention with Low-Rank Sparse Decomposition
by: He, Zhengfu, et al.
Published: (2025)

InRank: Incremental Low-Rank Learning
by: Zhao, Jiawei, et al.
Published: (2023)

Compressed BC-LISTA via Low-Rank Convolutional Decomposition
by: Wang, Han, et al.
Published: (2026)

Dynamic Rank Reinforcement Learning for Adaptive Low-Rank Multi-Head Self Attention in Large Language Models
by: Erden, Caner
Published: (2025)

On Catastrophic Forgetting in Low-Rank Decomposition-Based Parameter-Efficient Fine-Tuning
by: Ahmad, Muhammad, et al.
Published: (2026)

Structured Variational $D$-Decomposition for Accurate and Stable Low-Rank Approximation
by: Katende, Ronald
Published: (2025)

OATS: Outlier-Aware Pruning Through Sparse and Low Rank Decomposition
by: Zhang, Stephen, et al.
Published: (2024)

Optimal Policy Sparsification and Low Rank Decomposition for Deep Reinforcement Learning
by: Goddla, Vikram
Published: (2024)

Low Rank Tensor Completion via Adaptive ADMM
by: Führling, Niclas, et al.
Published: (2026)

A Bayesian Interpretation of Adaptive Low-Rank Adaptation
by: Chen, Haolin, et al.
Published: (2024)

Low-Rank Compression of Language Models via Differentiable Rank Selection
by: Sundrani, Sidhant, et al.
Published: (2025)

FlexMoRE: A Flexible Mixture of Rank-heterogeneous Experts for Efficient Federatedly-trained Large Language Models
by: Pirchert, Annemette Brok, et al.
Published: (2026)

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning
by: Li, Shiwei, et al.
Published: (2025)

Basis Selection: Low-Rank Decomposition of Pretrained Large Language Models for Target Applications
by: Li, Yang, et al.
Published: (2024)

Can Low-Rank Knowledge Distillation in LLMs be Useful for Microelectronic Reasoning?
by: Rouf, Nirjhor, et al.
Published: (2024)

LoKO: Low-Rank Kalman Optimizer for Online Fine-Tuning of Large Models
by: Abdi, Hossein, et al.
Published: (2024)

Low-Rank Adapting Models for Sparse Autoencoders
by: Chen, Matthew, et al.
Published: (2025)

Low-Rank Continual Personalization of Diffusion Models
by: Staniszewski, Łukasz, et al.
Published: (2024)

Asymmetry in Low-Rank Adapters of Foundation Models
by: Zhu, Jiacheng, et al.
Published: (2024)

Accelerating the Low-Rank Decomposed Models
by: Hajimolahoseini, Habib, et al.
Published: (2024)