Training Time

Training time in machine learning focuses on optimizing the efficiency and effectiveness of model development, aiming to reduce computational costs and improve generalization performance without sacrificing accuracy. Current research explores diverse strategies, including hybrid training methods combining online and offline learning, hardware-aware optimization for multi-accelerator systems, and adaptive training frameworks that dynamically adjust computational resources. These advancements are crucial for deploying large-scale models on resource-constrained devices and accelerating the development of complex AI systems across various applications.

Papers

April 29, 2022

Por Qu\'e N\~ao Utiliser Alla Spr{\aa}k? Mixed Training with Gradient Optimization in Few-Shot Cross-Lingual Transfer
Haoran Xu, Kenton Murray
NLP Task Cross Lingual Transfer Gradient Based Optimization Language Specific Target Language Training Time Shot Cross Lingual Mixed Supervised Learning

March 20, 2022

Hybrid training of optical neural networks
James Spall, Xianxin Guo, A. I. Lvovsky
Neural Network Linear Classifier Optical Neural Network Training Time Optical Processor

January 21, 2022

Training Hybrid Classical-Quantum Classifiers via Stochastic Variational Optimization
Ivana Nikoloska, Osvaldo Simeone
Quantum Machine Learning Near Term Quantum Hybrid Quantum Classical Quantum Classifier Training Time Stochastic Variational Inference Quantum Neuron

November 4, 2021

Bootstrap Your Object Detector via Mixed Training
Mengde Xu, Zheng Zhang, Fangyun Wei, Yutong Lin, Yue Cao, Stephen Lin, Han Hu, Xiang Bai
Data Augmentation Object Detector Training Time Strong Augmentation

Training Time

Papers

Por Qu\'e N\~ao Utiliser Alla Spr{\aa}k? Mixed Training with Gradient Optimization in Few-Shot Cross-Lingual Transfer

Hybrid training of optical neural networks

Training Hybrid Classical-Quantum Classifiers via Stochastic Variational Optimization

Bootstrap Your Object Detector via Mixed Training