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Calibrating the Rigged Lottery: Making All Tickets Reliable

Calibrating the Rigged Lottery: Making All Tickets Reliable

18 February 2023
Bowen Lei
Ruqi Zhang
Dongkuan Xu
Bani Mallick
    UQCV
ArXivPDFHTML

Papers citing "Calibrating the Rigged Lottery: Making All Tickets Reliable"

11 / 11 papers shown
Title
Embracing Unknown Step by Step: Towards Reliable Sparse Training in Real
  World
Embracing Unknown Step by Step: Towards Reliable Sparse Training in Real World
Bowen Lei
Dongkuan Xu
Ruqi Zhang
Bani Mallick
UQCV
29
0
0
29 Mar 2024
Uncovering the Hidden Cost of Model Compression
Uncovering the Hidden Cost of Model Compression
Diganta Misra
Muawiz Chaudhary
Agam Goyal
Bharat Runwal
Pin-Yu Chen
VLM
24
0
0
29 Aug 2023
Rethinking Data Distillation: Do Not Overlook Calibration
Rethinking Data Distillation: Do Not Overlook Calibration
Dongyao Zhu
Bowen Lei
Jie M. Zhang
Yanbo Fang
Ruqi Zhang
Yiqun Xie
Dongkuan Xu
DD
FedML
11
14
0
24 Jul 2023
Quantifying lottery tickets under label noise: accuracy, calibration,
  and complexity
Quantifying lottery tickets under label noise: accuracy, calibration, and complexity
V. Arora
Daniele Irto
Sebastian Goldt
G. Sanguinetti
19
2
0
21 Jun 2023
Balance is Essence: Accelerating Sparse Training via Adaptive Gradient
  Correction
Balance is Essence: Accelerating Sparse Training via Adaptive Gradient Correction
Bowen Lei
Dongkuan Xu
Ruqi Zhang
Shuren He
Bani Mallick
25
6
0
09 Jan 2023
Superposing Many Tickets into One: A Performance Booster for Sparse
  Neural Network Training
Superposing Many Tickets into One: A Performance Booster for Sparse Neural Network Training
Lu Yin
Vlado Menkovski
Meng Fang
Tianjin Huang
Yulong Pei
Mykola Pechenizkiy
D. Mocanu
Shiwei Liu
24
8
0
30 May 2022
Powerpropagation: A sparsity inducing weight reparameterisation
Powerpropagation: A sparsity inducing weight reparameterisation
Jonathan Richard Schwarz
Siddhant M. Jayakumar
Razvan Pascanu
P. Latham
Yee Whye Teh
87
54
0
01 Oct 2021
Improving Uncertainty of Deep Learning-based Object Classification on
  Radar Spectra using Label Smoothing
Improving Uncertainty of Deep Learning-based Object Classification on Radar Spectra using Label Smoothing
Kanil Patel
William H. Beluch
K. Rambach
Michael Pfeiffer
B. Yang
UQCV
25
9
0
27 Sep 2021
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
247
9,042
0
06 Jun 2015
ImageNet Large Scale Visual Recognition Challenge
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky
Jia Deng
Hao Su
J. Krause
S. Satheesh
...
A. Karpathy
A. Khosla
Michael S. Bernstein
Alexander C. Berg
Li Fei-Fei
VLM
ObjD
279
39,083
0
01 Sep 2014
Improving neural networks by preventing co-adaptation of feature
  detectors
Improving neural networks by preventing co-adaptation of feature detectors
Geoffrey E. Hinton
Nitish Srivastava
A. Krizhevsky
Ilya Sutskever
Ruslan Salakhutdinov
VLM
243
7,597
0
03 Jul 2012
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