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FlexiBERT: Are Current Transformer Architectures too Homogeneous and
  Rigid?

FlexiBERT: Are Current Transformer Architectures too Homogeneous and Rigid?

23 May 2022
Shikhar Tuli
Bhishma Dedhia
Shreshth Tuli
N. Jha
ArXivPDFHTML

Papers citing "FlexiBERT: Are Current Transformer Architectures too Homogeneous and Rigid?"

6 / 6 papers shown
Title
EdgeTran: Co-designing Transformers for Efficient Inference on Mobile
  Edge Platforms
EdgeTran: Co-designing Transformers for Efficient Inference on Mobile Edge Platforms
Shikhar Tuli
N. Jha
34
3
0
24 Mar 2023
Primer: Searching for Efficient Transformers for Language Modeling
Primer: Searching for Efficient Transformers for Language Modeling
David R. So
Wojciech Mañke
Hanxiao Liu
Zihang Dai
Noam M. Shazeer
Quoc V. Le
VLM
83
152
0
17 Sep 2021
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language
  Understanding
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
294
6,950
0
20 Apr 2018
Neural Architecture Search with Reinforcement Learning
Neural Architecture Search with Reinforcement Learning
Barret Zoph
Quoc V. Le
264
5,326
0
05 Nov 2016
Effective Approaches to Attention-based Neural Machine Translation
Effective Approaches to Attention-based Neural Machine Translation
Thang Luong
Hieu H. Pham
Christopher D. Manning
214
7,923
0
17 Aug 2015
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
249
9,134
0
06 Jun 2015
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