Warmup Period

A "warmup period" in machine learning refers to an initial training phase designed to improve model performance and stability. Current research focuses on optimizing warmup strategies across diverse applications, including federated learning, where personalized warmup methods using subnetworks enhance convergence in heterogeneous data settings, and recommendation systems, employing adversarial autoencoders to address the cold-start problem for new items. These techniques aim to mitigate challenges like conflicting updates or poorly initialized embeddings, ultimately leading to improved accuracy, faster convergence, and more robust hyperparameter tuning. The impact extends to various fields, improving the efficiency and reliability of machine learning models in applications ranging from cybersecurity (e.g., continuous authentication) to personalized recommendations.

Papers

October 31, 2024

Analyzing & Reducing the Need for Learning Rate Warmup in GPT Training
Atli Kosson, Bettina Messmer, Martin Jaggi
Learning Rate Community Need Batch Size Warm Start Warmup Period

October 3, 2024

FedPeWS: Personalized Warmup via Subnetworks for Enhanced Heterogeneous Federated Learning
Nurbek Tastan, Samuel Horvath, Martin Takac, Karthik Nandakumar
Data Heterogeneity Heterogeneous Federated Learning Unobserved Heterogeneity Warmup Period

June 13, 2024

Why Warmup the Learning Rate? Underlying Mechanisms and Improvements
Dayal Singh Kalra, Maissam Barkeshli
Deep Learning Learning Rate Large Relevance Improvement Functional Mechanism Warmup Period

February 28, 2023

Item Cold Start Recommendation via Adversarial Variational Auto-encoder Warm-up
Shenzheng Zhang, Qi Tan, Xinzhi Zheng, Yi Ren, Xu Zhao
Variational Autoencoder Item Popularity Cold Start Recommendation Conditional Variational Auto Encoder Warmup Period

November 10, 2022

Warmup and Transfer Knowledge-Based Federated Learning Approach for IoT Continuous Authentication
Mohamad Wazzeh, Hakima Ould-Slimane, Chamseddine Talhi, Azzam Mourad, Mohsen Guizani
Machine Learning Federated Learning Formality Transfer Identity Verification Federated Learning Approach Behavioral Biometrics Warmup Period

Warmup Period

Papers

Analyzing & Reducing the Need for Learning Rate Warmup in GPT Training

FedPeWS: Personalized Warmup via Subnetworks for Enhanced Heterogeneous Federated Learning

Why Warmup the Learning Rate? Underlying Mechanisms and Improvements

Item Cold Start Recommendation via Adversarial Variational Auto-encoder Warm-up

Warmup and Transfer Knowledge-Based Federated Learning Approach for IoT Continuous Authentication