Scale Efficiently: Insights from Pre-training and Fine-tuning
Transformers

Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers

22 September 2021

Mostafa Dehghani

Hyung Won Chung

Sharan Narang

Papers citing "Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers"

6 / 6 papers shown

Title
MLP-Mixer: An all-MLP Architecture for Vision Ilya O. Tolstikhin N. Houlsby Alexander Kolesnikov Lucas Beyer Xiaohua Zhai ... Andreas Steiner Daniel Keysers Jakob Uszkoreit Mario Lucic Alexey Dosovitskiy 222 2,132 0 04 May 2021
Carbon Emissions and Large Neural Network Training David A. Patterson Joseph E. Gonzalez Quoc V. Le Chen Liang Lluís-Miquel Munguía D. Rothchild David R. So Maud Texier J. Dean AI4CE 223 491 0 21 Apr 2021
Beyond Fully-Connected Layers with Quaternions: Parameterization of Hypercomplex Multiplications with $1/n$ Parameters Aston Zhang Yi Tay Shuai Zhang Alvin Chan A. Luu S. Hui Jie Fu MQ 148 77 0 17 Feb 2021
Scaling Laws for Neural Language Models Jared Kaplan Sam McCandlish T. Henighan Tom B. Brown B. Chess R. Child Scott Gray Alec Radford Jeff Wu Dario Amodei 215 3,054 0 23 Jan 2020
Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism M. Shoeybi M. Patwary Raul Puri P. LeGresley Jared Casper Bryan Catanzaro MoE 226 1,436 0 17 Sep 2019
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding Alex Jinpeng Wang Amanpreet Singh Julian Michael Felix Hill Omer Levy Samuel R. Bowman ELM 275 6,003 0 20 Apr 2018