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MoEUT: Mixture-of-Experts Universal Transformers

MoEUT: Mixture-of-Experts Universal Transformers

25 May 2024
Róbert Csordás
Kazuki Irie
Jürgen Schmidhuber
Christopher Potts
Christopher D. Manning
    MoE
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Papers citing "MoEUT: Mixture-of-Experts Universal Transformers"

23 / 23 papers shown
Title
Do Language Models Use Their Depth Efficiently?
Do Language Models Use Their Depth Efficiently?
Róbert Csordás
Christopher D. Manning
Christopher Potts
158
1
0
20 May 2025
From Markov to Laplace: How Mamba In-Context Learns Markov Chains
From Markov to Laplace: How Mamba In-Context Learns Markov Chains
Marco Bondaschi
Nived Rajaraman
Xiuying Wei
Kannan Ramchandran
Razvan Pascanu
Çağlar Gülçehre
Michael C. Gastpar
Ashok Vardhan Makkuva
117
0
0
17 Feb 2025
Parameters vs FLOPs: Scaling Laws for Optimal Sparsity for Mixture-of-Experts Language Models
Parameters vs FLOPs: Scaling Laws for Optimal Sparsity for Mixture-of-Experts Language Models
Samira Abnar
Harshay Shah
Dan Busbridge
Alaaeldin Mohamed Elnouby Ali
J. Susskind
Vimal Thilak
MoE
LRM
106
7
0
28 Jan 2025
Relaxed Recursive Transformers: Effective Parameter Sharing with Layer-wise LoRA
Relaxed Recursive Transformers: Effective Parameter Sharing with Layer-wise LoRA
Sangmin Bae
Adam Fisch
Hrayr Harutyunyan
Ziwei Ji
Seungyeon Kim
Tal Schuster
KELM
110
6
0
28 Oct 2024
Mixture of Attention Heads: Selecting Attention Heads Per Token
Mixture of Attention Heads: Selecting Attention Heads Per Token
Xiaofeng Zhang
Songlin Yang
Zeyu Huang
Jie Zhou
Wenge Rong
Zhang Xiong
MoE
109
46
0
11 Oct 2022
In-context Learning and Induction Heads
In-context Learning and Induction Heads
Catherine Olsson
Nelson Elhage
Neel Nanda
Nicholas Joseph
Nova Dassarma
...
Tom B. Brown
Jack Clark
Jared Kaplan
Sam McCandlish
C. Olah
305
510
0
24 Sep 2022
Toy Models of Superposition
Toy Models of Superposition
Nelson Elhage
Tristan Hume
Catherine Olsson
Nicholas Schiefer
T. Henighan
...
Sam McCandlish
Jared Kaplan
Dario Amodei
Martin Wattenberg
C. Olah
AAML
MILM
172
363
0
21 Sep 2022
Scaling Laws vs Model Architectures: How does Inductive Bias Influence
  Scaling?
Scaling Laws vs Model Architectures: How does Inductive Bias Influence Scaling?
Yi Tay
Mostafa Dehghani
Samira Abnar
Hyung Won Chung
W. Fedus
J. Rao
Sharan Narang
Vinh Q. Tran
Dani Yogatama
Donald Metzler
AI4CE
89
104
0
21 Jul 2022
The Neural Data Router: Adaptive Control Flow in Transformers Improves
  Systematic Generalization
The Neural Data Router: Adaptive Control Flow in Transformers Improves Systematic Generalization
Róbert Csordás
Kazuki Irie
Jürgen Schmidhuber
AI4CE
66
57
0
14 Oct 2021
Making Transformers Solve Compositional Tasks
Making Transformers Solve Compositional Tasks
Santiago Ontañón
Joshua Ainslie
Vaclav Cvicek
Zachary Kenneth Fisher
88
72
0
09 Aug 2021
RoFormer: Enhanced Transformer with Rotary Position Embedding
RoFormer: Enhanced Transformer with Rotary Position Embedding
Jianlin Su
Yu Lu
Shengfeng Pan
Ahmed Murtadha
Bo Wen
Yunfeng Liu
206
2,415
0
20 Apr 2021
GShard: Scaling Giant Models with Conditional Computation and Automatic
  Sharding
GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding
Dmitry Lepikhin
HyoukJoong Lee
Yuanzhong Xu
Dehao Chen
Orhan Firat
Yanping Huang
M. Krikun
Noam M. Shazeer
Zhiwen Chen
MoE
86
1,156
0
30 Jun 2020
On Layer Normalization in the Transformer Architecture
On Layer Normalization in the Transformer Architecture
Ruibin Xiong
Yunchang Yang
Di He
Kai Zheng
Shuxin Zheng
Chen Xing
Huishuai Zhang
Yanyan Lan
Liwei Wang
Tie-Yan Liu
AI4CE
113
988
0
12 Feb 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
528
4,773
0
23 Jan 2020
PyTorch: An Imperative Style, High-Performance Deep Learning Library
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Adam Paszke
Sam Gross
Francisco Massa
Adam Lerer
James Bradbury
...
Sasank Chilamkurthy
Benoit Steiner
Lu Fang
Junjie Bai
Soumith Chintala
ODL
366
42,299
0
03 Dec 2019
BLiMP: The Benchmark of Linguistic Minimal Pairs for English
BLiMP: The Benchmark of Linguistic Minimal Pairs for English
Alex Warstadt
Alicia Parrish
Haokun Liu
Anhad Mohananey
Wei Peng
Sheng-Fu Wang
Samuel R. Bowman
72
491
0
02 Dec 2019
Exploring the Limits of Transfer Learning with a Unified Text-to-Text
  Transformer
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Colin Raffel
Noam M. Shazeer
Adam Roberts
Katherine Lee
Sharan Narang
Michael Matena
Yanqi Zhou
Wei Li
Peter J. Liu
AIMat
371
20,053
0
23 Oct 2019
ALBERT: A Lite BERT for Self-supervised Learning of Language
  Representations
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
Zhenzhong Lan
Mingda Chen
Sebastian Goodman
Kevin Gimpel
Piyush Sharma
Radu Soricut
SSL
AIMat
326
6,441
0
26 Sep 2019
SentencePiece: A simple and language independent subword tokenizer and
  detokenizer for Neural Text Processing
SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing
Taku Kudo
John Richardson
178
3,514
0
19 Aug 2018
Layer Normalization
Layer Normalization
Jimmy Lei Ba
J. Kiros
Geoffrey E. Hinton
340
10,467
0
21 Jul 2016
Adaptive Computation Time for Recurrent Neural Networks
Adaptive Computation Time for Recurrent Neural Networks
Alex Graves
86
6
0
29 Mar 2016
Identity Mappings in Deep Residual Networks
Identity Mappings in Deep Residual Networks
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
332
10,172
0
16 Mar 2016
The Goldilocks Principle: Reading Children's Books with Explicit Memory
  Representations
The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations
Felix Hill
Antoine Bordes
S. Chopra
Jason Weston
RALM
99
635
0
07 Nov 2015
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