ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2212.04458
  4. Cited By
General-Purpose In-Context Learning by Meta-Learning Transformers

General-Purpose In-Context Learning by Meta-Learning Transformers

8 December 2022
Louis Kirsch
James Harrison
Jascha Narain Sohl-Dickstein
Luke Metz
ArXivPDFHTML

Papers citing "General-Purpose In-Context Learning by Meta-Learning Transformers"

16 / 66 papers shown
Title
Accelerating Neural Self-Improvement via Bootstrapping
Accelerating Neural Self-Improvement via Bootstrapping
Kazuki Irie
Jürgen Schmidhuber
27
1
0
02 May 2023
Structured State Space Models for In-Context Reinforcement Learning
Structured State Space Models for In-Context Reinforcement Learning
Chris Xiaoxuan Lu
Yannick Schroecker
Albert Gu
Emilio Parisotto
Jakob N. Foerster
Satinder Singh
Feryal M. P. Behbahani
AI4TS
97
82
0
07 Mar 2023
Memory-Based Meta-Learning on Non-Stationary Distributions
Memory-Based Meta-Learning on Non-Stationary Distributions
Tim Genewein
Grégoire Delétang
Anian Ruoss
L. Wenliang
Elliot Catt
Vincent Dutordoir
Jordi Grau-Moya
Laurent Orseau
Marcus Hutter
J. Veness
BDL
21
11
0
06 Feb 2023
Learning Functional Transduction
Learning Functional Transduction
Mathieu Chalvidal
Thomas Serre
Rufin VanRullen
AI4CE
35
2
0
01 Feb 2023
Human-Timescale Adaptation in an Open-Ended Task Space
Human-Timescale Adaptation in an Open-Ended Task Space
Adaptive Agent Team
Jakob Bauer
Kate Baumli
Satinder Baveja
Feryal M. P. Behbahani
...
Jakub Sygnowski
K. Tuyls
Sarah York
Alexander Zacherl
Lei Zhang
LM&Ro
OffRL
AI4CE
LRM
35
108
0
18 Jan 2023
Transformers as Algorithms: Generalization and Stability in In-context
  Learning
Transformers as Algorithms: Generalization and Stability in In-context Learning
Yingcong Li
M. E. Ildiz
Dimitris Papailiopoulos
Samet Oymak
20
152
0
17 Jan 2023
Why Can GPT Learn In-Context? Language Models Implicitly Perform
  Gradient Descent as Meta-Optimizers
Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers
Damai Dai
Yutao Sun
Li Dong
Y. Hao
Shuming Ma
Zhifang Sui
Furu Wei
LRM
23
148
0
20 Dec 2022
Transformers learn in-context by gradient descent
Transformers learn in-context by gradient descent
J. Oswald
Eyvind Niklasson
E. Randazzo
João Sacramento
A. Mordvintsev
A. Zhmoginov
Max Vladymyrov
MLT
30
429
0
15 Dec 2022
Neural Networks and the Chomsky Hierarchy
Neural Networks and the Chomsky Hierarchy
Grégoire Delétang
Anian Ruoss
Jordi Grau-Moya
Tim Genewein
L. Wenliang
...
Chris Cundy
Marcus Hutter
Shane Legg
Joel Veness
Pedro A. Ortega
UQCV
107
130
0
05 Jul 2022
Minimal Neural Network Models for Permutation Invariant Agents
Minimal Neural Network Models for Permutation Invariant Agents
J. Pedersen
S. Risi
48
3
0
12 May 2022
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Jason W. Wei
Xuezhi Wang
Dale Schuurmans
Maarten Bosma
Brian Ichter
F. Xia
Ed H. Chi
Quoc Le
Denny Zhou
LM&Ro
LRM
AI4CE
ReLM
367
8,495
0
28 Jan 2022
Long-Range Transformers for Dynamic Spatiotemporal Forecasting
Long-Range Transformers for Dynamic Spatiotemporal Forecasting
J. E. Grigsby
Zhe Wang
Nam Nguyen
Yanjun Qi
AI4TS
69
87
0
24 Sep 2021
Meta Learning Backpropagation And Improving It
Meta Learning Backpropagation And Improving It
Louis Kirsch
Jürgen Schmidhuber
51
56
0
29 Dec 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
246
4,489
0
23 Jan 2020
Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness
  of MAML
Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML
Aniruddh Raghu
M. Raghu
Samy Bengio
Oriol Vinyals
177
639
0
19 Sep 2019
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
338
11,684
0
09 Mar 2017
Previous
12