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Scaling Laws for Transfer

Scaling Laws for Transfer

2 February 2021
Danny Hernandez
Jared Kaplan
T. Henighan
Sam McCandlish
ArXivPDFHTML

Papers citing "Scaling Laws for Transfer"

17 / 67 papers shown
Title
The Efficiency Misnomer
The Efficiency Misnomer
Daoyuan Chen
Liuyi Yao
Dawei Gao
Ashish Vaswani
Yaliang Li
34
99
0
25 Oct 2021
Behavioral Experiments for Understanding Catastrophic Forgetting
Behavioral Experiments for Understanding Catastrophic Forgetting
Samuel J. Bell
Neil D. Lawrence
35
4
0
20 Oct 2021
Exploring the Limits of Large Scale Pre-training
Exploring the Limits of Large Scale Pre-training
Samira Abnar
Mostafa Dehghani
Behnam Neyshabur
Hanie Sedghi
AI4CE
60
114
0
05 Oct 2021
Scale Efficiently: Insights from Pre-training and Fine-tuning
  Transformers
Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers
Yi Tay
Mostafa Dehghani
J. Rao
W. Fedus
Samira Abnar
Hyung Won Chung
Sharan Narang
Dani Yogatama
Ashish Vaswani
Donald Metzler
206
110
0
22 Sep 2021
Scaling Laws for Neural Machine Translation
Scaling Laws for Neural Machine Translation
Behrooz Ghorbani
Orhan Firat
Markus Freitag
Ankur Bapna
M. Krikun
Xavier Garcia
Ciprian Chelba
Colin Cherry
40
99
0
16 Sep 2021
Robust fine-tuning of zero-shot models
Robust fine-tuning of zero-shot models
Mitchell Wortsman
Gabriel Ilharco
Jong Wook Kim
Mike Li
Simon Kornblith
...
Raphael Gontijo-Lopes
Hannaneh Hajishirzi
Ali Farhadi
Hongseok Namkoong
Ludwig Schmidt
VLM
64
691
0
04 Sep 2021
A Scaling Law for Synthetic-to-Real Transfer: How Much Is Your
  Pre-training Effective?
A Scaling Law for Synthetic-to-Real Transfer: How Much Is Your Pre-training Effective?
Hiroaki Mikami
Kenji Fukumizu
Shogo Murai
Shuji Suzuki
Yuta Kikuchi
Taiji Suzuki
S. Maeda
Kohei Hayashi
40
12
0
25 Aug 2021
Billion-Scale Pretraining with Vision Transformers for Multi-Task Visual
  Representations
Billion-Scale Pretraining with Vision Transformers for Multi-Task Visual Representations
Josh Beal
Hao Wu
Dong Huk Park
Andrew Zhai
Dmitry Kislyuk
ViT
21
29
0
12 Aug 2021
The Evolution of Out-of-Distribution Robustness Throughout Fine-Tuning
The Evolution of Out-of-Distribution Robustness Throughout Fine-Tuning
Anders Andreassen
Yasaman Bahri
Behnam Neyshabur
Rebecca Roelofs
OOD
OODD
30
78
0
30 Jun 2021
Generate, Annotate, and Learn: NLP with Synthetic Text
Generate, Annotate, and Learn: NLP with Synthetic Text
Xuanli He
Islam Nassar
J. Kiros
Gholamreza Haffari
Mohammad Norouzi
39
51
0
11 Jun 2021
Pretrained Transformers as Universal Computation Engines
Pretrained Transformers as Universal Computation Engines
Kevin Lu
Aditya Grover
Pieter Abbeel
Igor Mordatch
28
217
0
09 Mar 2021
Measuring Mathematical Problem Solving With the MATH Dataset
Measuring Mathematical Problem Solving With the MATH Dataset
Dan Hendrycks
Collin Burns
Saurav Kadavath
Akul Arora
Steven Basart
Eric Tang
D. Song
Jacob Steinhardt
ReLM
FaML
84
1,840
0
05 Mar 2021
Investigating the Limitations of Transformers with Simple Arithmetic
  Tasks
Investigating the Limitations of Transformers with Simple Arithmetic Tasks
Rodrigo Nogueira
Zhiying Jiang
Jimmy J. Li
LRM
24
122
0
25 Feb 2021
Learning Curve Theory
Learning Curve Theory
Marcus Hutter
140
59
0
08 Feb 2021
Measuring the Algorithmic Efficiency of Neural Networks
Measuring the Algorithmic Efficiency of Neural Networks
Danny Hernandez
Tom B. Brown
241
94
0
08 May 2020
Scaling Laws for Neural Language Models
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
264
4,489
0
23 Jan 2020
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Chelsea Finn
Pieter Abbeel
Sergey Levine
OOD
383
11,700
0
09 Mar 2017
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