Copertina

Cyclical Log Annealing as a Learning Rate Scheduler

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autore principale:	Naveen, Philip
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Scaling Law with Learning Rate Annealing
di: Tissue, Howe, et al.
Pubblicazione: (2024)

ScheduleFree+: Scaling Learning-Rate-Free & Schedule-Free Learning to Large Language Models
di: Defazio, Aaron
Pubblicazione: (2026)

Optimal Linear Decay Learning Rate Schedules and Further Refinements
di: Defazio, Aaron, et al.
Pubblicazione: (2023)

Heterogeneous Learning Rate Scheduling for Neural Architecture Search on Long-Tailed Datasets
di: Tang, Chenxia
Pubblicazione: (2024)

Training Dynamics of the Cooldown Stage in Warmup-Stable-Decay Learning Rate Scheduler
di: Dremov, Aleksandr, et al.
Pubblicazione: (2025)

Power Scheduler: A Batch Size and Token Number Agnostic Learning Rate Scheduler
di: Shen, Yikang, et al.
Pubblicazione: (2024)

ContraLog: Log File Anomaly Detection with Contrastive Learning and Masked Language Modeling
di: Dietz, Simon, et al.
Pubblicazione: (2026)

CyclicFL: A Cyclic Model Pre-Training Approach to Efficient Federated Learning
di: Zhang, Pengyu, et al.
Pubblicazione: (2023)

Seesaw: Accelerating Training by Balancing Learning Rate and Batch Size Scheduling
di: Meterez, Alexandru, et al.
Pubblicazione: (2025)

Anytime Pretraining: Horizon-Free Learning-Rate Schedules with Weight Averaging
di: Meterez, Alexandru, et al.
Pubblicazione: (2026)

Selecting Decision-Relevant Concepts in Reinforcement Learning
di: Raman, Naveen, et al.
Pubblicazione: (2026)

Machine Learning for Pattern Detection in Printhead Nozzle Logging
di: Prianikov, Nikola, et al.
Pubblicazione: (2025)

Annealing Machine-assisted Learning of Graph Neural Network for Combinatorial Optimization
di: Loyola, Pablo, et al.
Pubblicazione: (2025)

Learning Cyclic Causal Models from Incomplete Data
di: Sethuraman, Muralikrishnna G., et al.
Pubblicazione: (2024)

A Multi-Power Law for Loss Curve Prediction Across Learning Rate Schedules
di: Luo, Kairong, et al.
Pubblicazione: (2025)

Non-equilibrium Annealed Adjoint Sampler
di: Choi, Jaemoo, et al.
Pubblicazione: (2025)

Annealing Optimization for Progressive Learning with Stochastic Approximation
di: Mavridis, Christos, et al.
Pubblicazione: (2022)

Learning From Scenarios for Stochastic Repairable Scheduling
di: Houten, Kim van den, et al.
Pubblicazione: (2023)

Offline Reinforcement Learning for Learning to Dispatch for Job Shop Scheduling
di: van Remmerden, Jesse, et al.
Pubblicazione: (2024)

Learning to Solve Job Shop Scheduling under Uncertainty
di: Infantes, Guillaume, et al.
Pubblicazione: (2024)

Measurement Scheduling for ICU Patients with Offline Reinforcement Learning
di: Ji, Zongliang, et al.
Pubblicazione: (2024)

Task Scheduling & Forgetting in Multi-Task Reinforcement Learning
di: Speckmann, Marc, et al.
Pubblicazione: (2025)

ReLA: Representation Learning and Aggregation for Job Scheduling with Reinforcement Learning
di: Kwan, Zhengyi, et al.
Pubblicazione: (2026)

Annealed Softmax Greedy in Many-Armed Bayesian Bandits
di: Overman, William, et al.
Pubblicazione: (2026)

Annealing Self-Distillation Rectification Improves Adversarial Training
di: Wu, Yu-Yu, et al.
Pubblicazione: (2023)

Variational Learning of Gaussian Process Latent Variable Models through Stochastic Gradient Annealed Importance Sampling
di: Xu, Jian, et al.
Pubblicazione: (2024)

PearSAN: A Machine Learning Method for Inverse Design using Pearson Correlated Surrogate Annealing
di: Bezick, Michael, et al.
Pubblicazione: (2024)

An Adaptive Simulated Annealing-Based Machine Learning Approach for Developing an E-Triage Tool for Hospital Emergency Operations
di: Ahmed, Abdulaziz, et al.
Pubblicazione: (2022)

Efficient Approximate Posterior Sampling with Annealed Langevin Monte Carlo
di: Parulekar, Advait, et al.
Pubblicazione: (2025)

Policy Gradient with Adaptive Entropy Annealing for Continual Fine-Tuning
di: Zhang, Yaqian, et al.
Pubblicazione: (2026)

Scaling and Transferability of Annealing Strategies in Large Language Model Training
di: Wang, Siqi, et al.
Pubblicazione: (2025)

Towards Provable Log Density Policy Gradient
di: Katdare, Pulkit, et al.
Pubblicazione: (2024)

Adaptive Memory Decay for Log-Linear Attention
di: Amin, Yaxita, et al.
Pubblicazione: (2026)

Bounding Evidence and Estimating Log-Likelihood in VAE
di: Struski, Łukasz, et al.
Pubblicazione: (2022)

LogGuardQ: A Cognitive-Enhanced Reinforcement Learning Framework for Cybersecurity Anomaly Detection in Security Logs
di: de Sousa, Umberto Gonçalves
Pubblicazione: (2025)

RL-MSA: a Reinforcement Learning-based Multi-line bus Scheduling Approach
di: Liu, Yingzhuo
Pubblicazione: (2024)

Intelligent Learning Rate Distribution to reduce Catastrophic Forgetting in Transformers
di: Kenneweg, Philip, et al.
Pubblicazione: (2024)

Don't Forget the Critic: Value-Based Data Rehearsal for Multi-Cyclic Continual Reinforcement Learning
di: Poole, Benjamin, et al.
Pubblicazione: (2026)

Learning Memory-Enhanced Improvement Heuristics for Flexible Job Shop Scheduling
di: Wang, Jiaqi, et al.
Pubblicazione: (2026)

Deep Reinforcement Learning Guided Improvement Heuristic for Job Shop Scheduling
di: Zhang, Cong, et al.
Pubblicazione: (2022)