Training Epoch

A training epoch in machine learning represents a single pass through the entire training dataset during model training. Current research focuses on optimizing the number of epochs needed for effective model training, exploring strategies like early discarding of underperforming models and adaptive gradient correction to accelerate convergence. This optimization is crucial for improving efficiency in various applications, including federated learning and click-through rate prediction, where reducing training time and computational costs is paramount while maintaining or improving model accuracy. The findings are impacting the development of more efficient and robust machine learning algorithms across diverse fields.

Papers

October 30, 2024

(FL)$^2$: Overcoming Few Labels in Federated Semi-Supervised Learning
Seungjoo Lee, Thanh-Long V. Le, Jaemin Shin, Sung-Ju Lee
Semi Supervised Centralized Training Automatic Thresholding Training Epoch

April 5, 2024

The Unreasonable Effectiveness Of Early Discarding After One Epoch In Neural Network Hyperparameter Optimization
Romain Egele, Felix Mohr, Tom Viering, Prasanna Balaprakash
Neural Network Deep Learning Hyperparameter Optimization Pareto Front Precise Inter EPOCH Feature Training Epoch

March 26, 2024

Enhancing Privacy in Federated Learning through Local Training
Nicola Bastianello, Changxin Liu, Karl H. Johansson
Private Federated Learning Local Training Protecting Privacy Training Epoch

November 17, 2023

Leveraging Function Space Aggregation for Federated Learning at Scale
Nikita Dhawan, Nicole Mitchell, Zachary Charles, Zachary Garrett, Gintare Karolina Dziugaite
Fine Tuning Visual Analogue Scale Federated Averaging Training Epoch

May 31, 2023

Multi-Epoch Learning for Deep Click-Through Rate Prediction Models
Zhaocheng Liu, Zhongxiang Fan, Jian Liang, Dongying Kong, Han Li
Click Through Rate Prediction Training Epoch Multi Epoch Training

April 8, 2023

EMP-SSL: Towards Self-Supervised Learning in One Training Epoch
Shengbang Tong, Yubei Chen, Yi Ma, Yann Lecun
Self Supervised Learning Supervised Learning Self Supervised Method Tiny ImageNet Self Supervised Learning Method Training Epoch

March 28, 2023

FedAgg: Adaptive Federated Learning with Aggregated Gradients
Wenhao Yuan, Xuehe Wang
Gradient Descent Learning Rate Decentralized Learning Aggregated Gradient Adaptive Federated Learning Training Epoch

January 9, 2023

Balance is Essence: Accelerating Sparse Training via Adaptive Gradient Correction
Bowen Lei, Dongkuan Xu, Ruqi Zhang, Shuren He, Bani K. Mallick
Balancing Strategy Sparse Training Sparsity Constraint Training Epoch Gradient Prediction

September 4, 2022

Towards Understanding the Overfitting Phenomenon of Deep Click-Through Rate Prediction Models
Zhao-Yu Zhang, Xiang-Rong Sheng, Yujing Zhang, Biye Jiang, Shuguang Han, Hongbo Deng, Bo Zheng
Deep Neural Network Recommendation System Model Overfitting Click Through Rate Prediction Click Through Rate Industrial Recommender System Training Epoch

July 17, 2022

Fast-MoCo: Boost Momentum-based Contrastive Learning with Combinatorial Patches
Yuanzheng Ci, Chen Lin, Lei Bai, Wanli Ouyang
Contrastive Learning Self Supervised Self Supervision Supervision Signal Momentum Contrast Training Epoch

April 20, 2022

Federated Learning in Multi-Center Critical Care Research: A Systematic Case Study using the eICU Database
Arash Mehrjou, Ashkan Soleymani, Annika Buchholz, Jürgen Hetzel, Patrick Schwab, Stefan Bauer
Case Study Available Datasets Federated Averaging Intensive Care Unit Clinical Datasets Training Epoch