:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Hegde, Suhas G, Kaur, Shilpy, Tiwari, Aruna
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	https://arxiv.org/abs/2503.19530
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

SVFT: Parameter-Efficient Fine-Tuning with Singular Vectors
by: Lingam, Vijay, et al.
Published: (2024)

CLOVER: Cross-Layer Orthogonal Vectors Pruning and Fine-Tuning
by: Meng, Fanxu, et al.
Published: (2024)

PiSSA: Principal Singular Values and Singular Vectors Adaptation of Large Language Models
by: Meng, Fanxu, et al.
Published: (2024)

Singular Vectors of Attention Heads Align with Features
by: Franco, Gabriel, et al.
Published: (2026)

VP-VAE: Rethinking Vector Quantization via Adaptive Vector Perturbation
by: Zhai, Linwei, et al.
Published: (2026)

Gradient-based Fine-Tuning through Pre-trained Model Regularization
by: Liu, Xuanbo, et al.
Published: (2025)

OSF: On Pre-training and Scaling of Sleep Foundation Models
by: Shuai, Zitao, et al.
Published: (2026)

Efficient Online RL Fine Tuning with Offline Pre-trained Policy Only
by: Xiao, Wei, et al.
Published: (2025)

Beyond Components: Singular Vector-Based Interpretability of Transformer Circuits
by: Ahmad, Areeb, et al.
Published: (2025)

Adaptive Helpfulness-Harmlessness Alignment with Preference Vectors
by: Liang, Ren-Wei, et al.
Published: (2025)

H2Tune: Federated Foundation Model Fine-Tuning with Hybrid Heterogeneity
by: Guo, Wei, et al.
Published: (2025)

Why Deep Jacobian Spectra Separate: Depth-Induced Scaling and Singular-Vector Alignment
by: Haas, Nathanaël, et al.
Published: (2026)

Bias Fitting to Mitigate Length Bias of Reward Model in RLHF
by: Zhao, Kangwen, et al.
Published: (2025)

Point-PEFT: Parameter-Efficient Fine-Tuning for 3D Pre-trained Models
by: Tang, Yiwen, et al.
Published: (2023)

FedPFT: Federated Proxy Fine-Tuning of Foundation Models
by: Peng, Zhaopeng, et al.
Published: (2024)

Parameter-Efficient Fine-Tuning for Foundation Models
by: Zhang, Dan, et al.
Published: (2025)

TabTune: A Unified Library for Inference and Fine-Tuning Tabular Foundation Models
by: Tanna, Aditya, et al.
Published: (2025)

Shifting Perspectives: Steering Vectors for Robust Bias Mitigation in LLMs
by: Siddique, Zara, et al.
Published: (2025)

VFA: Relieving Vector Operations in Flash Attention with Global Maximum Pre-computation
by: Sun, Yupeng, et al.
Published: (2026)

TuneComp: Joint Fine-tuning and Compression for Large Foundation Models
by: Chen, Xiangyu, et al.
Published: (2025)

Addressing Bias Through Ensemble Learning and Regularized Fine-Tuning
by: Radwan, Ahmed, et al.
Published: (2024)

APAR: Modeling Irregular Target Functions in Tabular Regression via Arithmetic-Aware Pre-Training and Adaptive-Regularized Fine-Tuning
by: Wu, Hong-Wei, et al.
Published: (2024)

In-Context Fine-Tuning for Time-Series Foundation Models
by: Das, Abhimanyu, et al.
Published: (2024)

Stanford Sleep Bench: Evaluating Polysomnography Pre-training Methods for Sleep Foundation Models
by: Kjaer, Magnus Ruud, et al.
Published: (2025)

Measuring Pre-training Data Quality without Labels for Time Series Foundation Models
by: Wen, Songkang, et al.
Published: (2024)

Fine-Tuning Pre-trained Large Time Series Models for Prediction of Wind Turbine SCADA Data
by: Fan, Yuwei, et al.
Published: (2024)

A Stronger Mixture of Low-Rank Experts for Fine-Tuning Foundation Models
by: Sun, Mengyang, et al.
Published: (2025)

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning
by: Bian, Jieming, et al.
Published: (2025)

Prompt Tuning with Diffusion for Few-Shot Pre-trained Policy Generalization
by: Hu, Shengchao, et al.
Published: (2024)

On Task Vectors and Gradients
by: Zhou, Luca, et al.
Published: (2025)

Deep Support Vectors
by: Lee, Junhoo, et al.
Published: (2024)

KBVQ-MoE: KLT-guided SVD with Bias-Corrected Vector Quantization for MoE Large Language Models
by: Xu, Zukang, et al.
Published: (2026)

Unlocking the Power of Function Vectors for Characterizing and Mitigating Catastrophic Forgetting in Continual Instruction Tuning
by: Jiang, Gangwei, et al.
Published: (2025)

Task-Aware Parameter-Efficient Fine-Tuning of Large Pre-Trained Models at the Edge
by: Hu, Senkang, et al.
Published: (2025)

Sparse is Enough in Fine-tuning Pre-trained Large Language Models
by: Song, Weixi, et al.
Published: (2023)

Mochi: Aligning Pre-training and Inference for Efficient Graph Foundation Models via Meta-Learning
by: Mattos, João, et al.
Published: (2026)

Domain-Aware Fine-Tuning of Foundation Models
by: Kaplan, Ugur Ali, et al.
Published: (2024)

Educational Cone Model in Embedding Vector Spaces
by: Ehara, Yo
Published: (2025)

ABBA-Adapters: Efficient and Expressive Fine-Tuning of Foundation Models
by: Singhal, Raghav, et al.
Published: (2025)

Rethinking Cross-Modal Fine-Tuning: Optimizing the Interaction Between Feature Alignment and Target Fitting
by: Tran, Trong Khiem, et al.
Published: (2026)