End-to-End Training Induces Information Bottleneck through Layer-Role Differentiation: A Comparative Analysis with Layer-wise Training

14 February 2024

Papers citing "End-to-End Training Induces Information Bottleneck through Layer-Role Differentiation: A Comparative Analysis with Layer-wise Training"

9 / 9 papers shown

Title
Scalable Model Merging with Progressive Layer-wise Distillation Jing Xu Jiazheng Li J. Zhang MoMe FedML 85 0 0 18 Feb 2025
NeuLite: Memory-Efficient Federated Learning via Elastic Progressive Training Yebo Wu Li Li Chunlin Tian Dubing Chen Chengzhong Xu FedML 19 3 0 20 Aug 2024
Real Time American Sign Language Detection Using Yolo-v9 Amna Imran Meghana Shashishekhara Hulikal Hamza A. A. Gardi ObjD 31 2 0 25 Jul 2024
DualHSIC: HSIC-Bottleneck and Alignment for Continual Learning Zifeng Wang Zheng Zhan Yifan Gong Yucai Shao Stratis Ioannidis Yanzhi Wang Jennifer Dy CLL 45 10 0 30 Apr 2023
Pruning Adversarially Robust Neural Networks without Adversarial Examples T. Jian Zifeng Wang Yanzhi Wang Jennifer Dy Stratis Ioannidis AAML VLM 39 11 0 09 Oct 2022
SoftHebb: Bayesian Inference in Unsupervised Hebbian Soft Winner-Take-All Networks Timoleon Moraitis Dmitry Toichkin Adrien Journé Yansong Chua Qinghai Guo AAML BDL 68 28 0 12 Jul 2021
Pruning and Quantization for Deep Neural Network Acceleration: A Survey Tailin Liang C. Glossner Lei Wang Shaobo Shi Xiaotong Zhang MQ 124 671 0 24 Jan 2021
Training Deep Architectures Without End-to-End Backpropagation: A Survey on the Provably Optimal Methods Shiyu Duan José C. Príncipe MQ 20 3 0 09 Jan 2021
What is the State of Neural Network Pruning? Davis W. Blalock Jose Javier Gonzalez Ortiz Jonathan Frankle John Guttag 183 1,027 0 06 Mar 2020