Domain Adaptation for Time series Transformers using One-step fine-tuning
Fuente:
arXiv
Saved in:
| Main Authors: | Khanal, Subina, Tirupathi, Seshu, Zizzo, Giulio, Rawat, Ambrish, Pedersen, Torben Bach |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Bridging the High-Frequency Data Gap: A Millisecond-Resolution Network Dataset for Advancing Time Series Foundation Models
by: Khanal, Subina, et al.
Published: (2026)
by: Khanal, Subina, et al.
Published: (2026)
Dynamic Features Adaptation in Networking: Toward Flexible training and Explainable inference
by: Belkhiter, Yannis, et al.
Published: (2025)
by: Belkhiter, Yannis, et al.
Published: (2025)
Pre-Hoc Predictions in AutoML: Leveraging LLMs to Enhance Model Selection and Benchmarking for Tabular datasets
by: Belkhiter, Yannis, et al.
Published: (2025)
by: Belkhiter, Yannis, et al.
Published: (2025)
Step-Tagging: Toward controlling the generation of Language Reasoning Models through step monitoring
by: Belkhiter, Yannis, et al.
Published: (2025)
by: Belkhiter, Yannis, et al.
Published: (2025)
Shapelets-Enriched Selective Forecasting using Time Series Foundation Models
by: Tomar, Shivani, et al.
Published: (2026)
by: Tomar, Shivani, et al.
Published: (2026)
Activated LoRA: Fine-tuned LLMs for Intrinsics
by: Greenewald, Kristjan, et al.
Published: (2025)
by: Greenewald, Kristjan, et al.
Published: (2025)
MoJE: Mixture of Jailbreak Experts, Naive Tabular Classifiers as Guard for Prompt Attacks
by: Cornacchia, Giandomenico, et al.
Published: (2024)
by: Cornacchia, Giandomenico, et al.
Published: (2024)
MAD-MAX: Modular And Diverse Malicious Attack MiXtures for Automated LLM Red Teaming
by: Schoepf, Stefan, et al.
Published: (2025)
by: Schoepf, Stefan, et al.
Published: (2025)
TRACES: Tagging Reasoning Steps for Adaptive Cost-Efficient Early-Stopping
by: Belkhiter, Yannis, et al.
Published: (2026)
by: Belkhiter, Yannis, et al.
Published: (2026)
Breaking MCP with Function Hijacking Attacks: Novel Threats for Function Calling and Agentic Models
by: Belkhiter, Yannis, et al.
Published: (2026)
by: Belkhiter, Yannis, et al.
Published: (2026)
Adversarial Prompt Evaluation: Systematic Benchmarking of Guardrails Against Prompt Input Attacks on LLMs
by: Zizzo, Giulio, et al.
Published: (2025)
by: Zizzo, Giulio, et al.
Published: (2025)
Differentially Private and Adversarially Robust Machine Learning: An Empirical Evaluation
by: Thakkar, Janvi, et al.
Published: (2024)
by: Thakkar, Janvi, et al.
Published: (2024)
Elevating Defenses: Bridging Adversarial Training and Watermarking for Model Resilience
by: Thakkar, Janvi, et al.
Published: (2023)
by: Thakkar, Janvi, et al.
Published: (2023)
Explainable Probabilistic Machine Learning for Predicting Drilling Fluid Loss of Circulation in Marun Oil Field
by: Damarla, Seshu Kumar, et al.
Published: (2025)
by: Damarla, Seshu Kumar, et al.
Published: (2025)
Attack Atlas: A Practitioner's Perspective on Challenges and Pitfalls in Red Teaming GenAI
by: Rawat, Ambrish, et al.
Published: (2024)
by: Rawat, Ambrish, et al.
Published: (2024)
Domain Adaptation for Industrial Time-series Forecasting via Counterfactual Inference
by: Min, Chao, et al.
Published: (2024)
by: Min, Chao, et al.
Published: (2024)
One-Class Domain Adaptation via Meta-Learning
by: Holly, Stephanie, et al.
Published: (2025)
by: Holly, Stephanie, et al.
Published: (2025)
Complexity-aware fine-tuning
by: Goncharov, Andrey, et al.
Published: (2025)
by: Goncharov, Andrey, et al.
Published: (2025)
LLMStinger: Jailbreaking LLMs using RL fine-tuned LLMs
by: Jha, Piyush, et al.
Published: (2024)
by: Jha, Piyush, et al.
Published: (2024)
Automatic Domain Adaptation by Transformers in In-Context Learning
by: Hataya, Ryuichiro, et al.
Published: (2024)
by: Hataya, Ryuichiro, et al.
Published: (2024)
Quantum-PEFT: Ultra parameter-efficient fine-tuning
by: Koike-Akino, Toshiaki, et al.
Published: (2025)
by: Koike-Akino, Toshiaki, et al.
Published: (2025)
Multi-scale Time-stepping of Partial Differential Equations with Transformers
by: Hemmasian, AmirPouya, et al.
Published: (2023)
by: Hemmasian, AmirPouya, et al.
Published: (2023)
Digital Twin-Empowered Voltage Control for Power Systems
by: Xu, Jiachen, et al.
Published: (2024)
by: Xu, Jiachen, et al.
Published: (2024)
Learning from models beyond fine-tuning
by: Zheng, Hongling, et al.
Published: (2023)
by: Zheng, Hongling, et al.
Published: (2023)
Mechanistically analyzing the effects of fine-tuning on procedurally defined tasks
by: Jain, Samyak, et al.
Published: (2023)
by: Jain, Samyak, et al.
Published: (2023)
AdaFish: Fast low-rank parameter-efficient fine-tuning by using second-order information
by: Hu, Jiang, et al.
Published: (2024)
by: Hu, Jiang, et al.
Published: (2024)
Uncertainty quantification in fine-tuned LLMs using LoRA ensembles
by: Balabanov, Oleksandr, et al.
Published: (2024)
by: Balabanov, Oleksandr, et al.
Published: (2024)
Jal Anveshak: Prediction of fishing zones using fine-tuned LlaMa 2
by: Mejari, Arnav, et al.
Published: (2024)
by: Mejari, Arnav, et al.
Published: (2024)
Out-of-distribution materials property prediction using adversarial learning based fine-tuning
by: Li, Qinyang, et al.
Published: (2024)
by: Li, Qinyang, et al.
Published: (2024)
Structure-Attribute Transformations with Markov Chain Boost Graph Domain Adaptation
by: Liu, Zhen, et al.
Published: (2025)
by: Liu, Zhen, et al.
Published: (2025)
Universal Domain Adaptation Benchmark for Time Series Data Representation
by: Mussard, Romain, et al.
Published: (2025)
by: Mussard, Romain, et al.
Published: (2025)
Avoiding mode collapse in diffusion models fine-tuned with reinforcement learning
by: Barceló, Roberto, et al.
Published: (2024)
by: Barceló, Roberto, et al.
Published: (2024)
ReALLM: A general framework for LLM compression and fine-tuning
by: Leconte, Louis, et al.
Published: (2024)
by: Leconte, Louis, et al.
Published: (2024)
Empirical influence functions to understand the logic of fine-tuning
by: Matelsky, Jordan K., et al.
Published: (2024)
by: Matelsky, Jordan K., et al.
Published: (2024)
Replaying pre-training data improves fine-tuning
by: Kotha, Suhas, et al.
Published: (2026)
by: Kotha, Suhas, et al.
Published: (2026)
On fine-tuning Boltz-2 for protein-protein affinity prediction
by: King, James, et al.
Published: (2025)
by: King, James, et al.
Published: (2025)
DATTA: Domain Diversity Aware Test-Time Adaptation for Dynamic Domain Shift Data Streams
by: Ye, Chuyang, et al.
Published: (2024)
by: Ye, Chuyang, et al.
Published: (2024)
Vision Transformer-based Adversarial Domain Adaptation
by: Li, Yahan, et al.
Published: (2024)
by: Li, Yahan, et al.
Published: (2024)
Feature-Weighted MMD-CORAL for Domain Adaptation in Power Transformer Fault Diagnosis
by: Mahmoodiyan, Hootan, et al.
Published: (2025)
by: Mahmoodiyan, Hootan, et al.
Published: (2025)
Learning Compositional Transferability of Time Series for Source-Free Domain Adaptation
by: Sun, Hankang, et al.
Published: (2025)
by: Sun, Hankang, et al.
Published: (2025)
Similar Items
-
Bridging the High-Frequency Data Gap: A Millisecond-Resolution Network Dataset for Advancing Time Series Foundation Models
by: Khanal, Subina, et al.
Published: (2026) -
Dynamic Features Adaptation in Networking: Toward Flexible training and Explainable inference
by: Belkhiter, Yannis, et al.
Published: (2025) -
Pre-Hoc Predictions in AutoML: Leveraging LLMs to Enhance Model Selection and Benchmarking for Tabular datasets
by: Belkhiter, Yannis, et al.
Published: (2025) -
Step-Tagging: Toward controlling the generation of Language Reasoning Models through step monitoring
by: Belkhiter, Yannis, et al.
Published: (2025) -
Shapelets-Enriched Selective Forecasting using Time Series Foundation Models
by: Tomar, Shivani, et al.
Published: (2026)