Tiny Models are the Computational Saver for Large Models

26 March 2024

Papers citing "Tiny Models are the Computational Saver for Large Models"

8 / 8 papers shown

Title
ORXE: Orchestrating Experts for Dynamically Configurable Efficiency Qingyuan Wang Guoxin Wang B. Cardiff Deepu John 38 0 0 07 May 2025
AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices Yuzhan Wang Sicong Liu Bin Guo Boqi Zhang Ke Ma Yasan Ding Hao Luo Yao Li Zhiwen Yu 76 1 0 01 Dec 2024
DyCE: Dynamically Configurable Exiting for Deep Learning Compression and Real-time Scaling Qingyuan Wang B. Cardiff Antoine Frappé Benoît Larras Deepu John 31 1 0 04 Mar 2024
Learning to Weight Samples for Dynamic Early-exiting Networks Yizeng Han Yifan Pu Zihang Lai Chaofei Wang S. Song Junfen Cao Wenhui Huang Chao Deng Gao Huang 59 54 0 17 Sep 2022
Tutel: Adaptive Mixture-of-Experts at Scale Changho Hwang Wei Cui Yifan Xiong Ziyue Yang Ze Liu ... Joe Chau Peng Cheng Fan Yang Mao Yang Y. Xiong MoE 97 110 0 07 Jun 2022
Masked Autoencoders Are Scalable Vision Learners Kaiming He Xinlei Chen Saining Xie Yanghao Li Piotr Dollár Ross B. Girshick ViT TPM 305 7,443 0 11 Nov 2021
Feature Pyramid Networks for Object Detection Nayeon Lee Piotr Dollár Ross B. Girshick Kaiming He Bharath Hariharan Serge J. Belongie ObjD 183 21,813 0 09 Dec 2016
U-Net: Convolutional Networks for Biomedical Image Segmentation Olaf Ronneberger Philipp Fischer Thomas Brox SSeg 3DV 300 75,834 0 18 May 2015