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. 2106.07998
  4. Cited By
Revisiting the Calibration of Modern Neural Networks

Revisiting the Calibration of Modern Neural Networks

15 June 2021
Matthias Minderer
Josip Djolonga
Rob Romijnders
F. Hubis
Xiaohua Zhai
N. Houlsby
Dustin Tran
Mario Lucic
    UQCV
ArXivPDFHTML

Papers citing "Revisiting the Calibration of Modern Neural Networks"

25 / 75 papers shown
Title
ScaleFace: Uncertainty-aware Deep Metric Learning
ScaleFace: Uncertainty-aware Deep Metric Learning
Roma Kail
Kirill Fedyanin
Nikita Muravev
Alexey Zaytsev
Maxim Panov
CVBM
UQCV
24
5
0
05 Sep 2022
Calibrated Selective Classification
Calibrated Selective Classification
Adam Fisch
Tommi Jaakkola
Regina Barzilay
26
16
0
25 Aug 2022
Adaptive Temperature Scaling for Robust Calibration of Deep Neural
  Networks
Adaptive Temperature Scaling for Robust Calibration of Deep Neural Networks
Sérgio A. Balanya
Juan Maroñas
Daniel Ramos
OOD
35
13
0
31 Jul 2022
An Impartial Take to the CNN vs Transformer Robustness Contest
An Impartial Take to the CNN vs Transformer Robustness Contest
Francesco Pinto
Philip H. S. Torr
P. Dokania
UQCV
AAML
27
48
0
22 Jul 2022
Assaying Out-Of-Distribution Generalization in Transfer Learning
Assaying Out-Of-Distribution Generalization in Transfer Learning
F. Wenzel
Andrea Dittadi
Peter V. Gehler
Carl-Johann Simon-Gabriel
Max Horn
...
Chris Russell
Thomas Brox
Bernt Schiele
Bernhard Schölkopf
Francesco Locatello
OOD
OODD
AAML
57
71
0
19 Jul 2022
Estimating Test Performance for AI Medical Devices under Distribution
  Shift with Conformal Prediction
Estimating Test Performance for AI Medical Devices under Distribution Shift with Conformal Prediction
Charles Lu
Syed Rakin Ahmed
Praveer Singh
Jayashree Kalpathy-Cramer
OOD
25
5
0
12 Jul 2022
Language Models (Mostly) Know What They Know
Language Models (Mostly) Know What They Know
Saurav Kadavath
Tom Conerly
Amanda Askell
T. Henighan
Dawn Drain
...
Nicholas Joseph
Benjamin Mann
Sam McCandlish
C. Olah
Jared Kaplan
ELM
47
712
0
11 Jul 2022
ProSelfLC: Progressive Self Label Correction Towards A Low-Temperature
  Entropy State
ProSelfLC: Progressive Self Label Correction Towards A Low-Temperature Entropy State
Xinshao Wang
Yang Hua
Elyor Kodirov
S. Mukherjee
David A. Clifton
N. Robertson
19
6
0
30 Jun 2022
Forecasting Future World Events with Neural Networks
Forecasting Future World Events with Neural Networks
Andy Zou
Tristan Xiao
Ryan Jia
Joe Kwon
Mantas Mazeika
Richard Li
Dawn Song
Jacob Steinhardt
Owain Evans
Dan Hendrycks
24
22
0
30 Jun 2022
COLD Fusion: Calibrated and Ordinal Latent Distribution Fusion for
  Uncertainty-Aware Multimodal Emotion Recognition
COLD Fusion: Calibrated and Ordinal Latent Distribution Fusion for Uncertainty-Aware Multimodal Emotion Recognition
M. Tellamekala
Shahin Amiriparian
Björn W. Schuller
Elisabeth André
T. Giesbrecht
M. Valstar
26
25
0
12 Jun 2022
Re-Examining Calibration: The Case of Question Answering
Re-Examining Calibration: The Case of Question Answering
Chenglei Si
Chen Zhao
Sewon Min
Jordan L. Boyd-Graber
61
30
0
25 May 2022
Calibration of Natural Language Understanding Models with Venn--ABERS
  Predictors
Calibration of Natural Language Understanding Models with Venn--ABERS Predictors
Patrizio Giovannotti
38
6
0
21 May 2022
A General Framework for quantifying Aleatoric and Epistemic uncertainty
  in Graph Neural Networks
A General Framework for quantifying Aleatoric and Epistemic uncertainty in Graph Neural Networks
Sai Munikoti
D. Agarwal
Laya Das
Balasubramaniam Natarajan
BDL
UD
31
13
0
20 May 2022
Evaluating Uncertainty Calibration for Open-Set Recognition
Evaluating Uncertainty Calibration for Open-Set Recognition
Zongyao Lyu
Nolan B. Gutierrez
William J. Beksi
UQCV
32
1
0
15 May 2022
Distinction Maximization Loss: Efficiently Improving Out-of-Distribution
  Detection and Uncertainty Estimation by Replacing the Loss and Calibrating
Distinction Maximization Loss: Efficiently Improving Out-of-Distribution Detection and Uncertainty Estimation by Replacing the Loss and Calibrating
David Macêdo
Cleber Zanchettin
Teresa B Ludermir
UQCV
28
4
0
12 May 2022
Calibrating for Class Weights by Modeling Machine Learning
Calibrating for Class Weights by Modeling Machine Learning
Andrew Caplin
Daniel Martin
Philip Marx
19
1
0
10 May 2022
It's DONE: Direct ONE-shot learning with quantile weight imprinting
It's DONE: Direct ONE-shot learning with quantile weight imprinting
Kazufumi Hosoda
Keigo Nishida
S. Seno
Tomohiro Mashita
H. Kashioka
I. Ohzawa
22
2
0
28 Apr 2022
A Stitch in Time Saves Nine: A Train-Time Regularizing Loss for Improved
  Neural Network Calibration
A Stitch in Time Saves Nine: A Train-Time Regularizing Loss for Improved Neural Network Calibration
R. Hebbalaguppe
Jatin Prakash
Neelabh Madan
Chetan Arora
UQCV
25
42
0
25 Mar 2022
How Do Vision Transformers Work?
How Do Vision Transformers Work?
Namuk Park
Songkuk Kim
ViT
35
465
0
14 Feb 2022
On the Value of ML Models
On the Value of ML Models
Fabio Casati
Pierre-Andre Noel
Jie Yang
19
7
0
13 Dec 2021
Deep Probability Estimation
Deep Probability Estimation
Sheng Liu
Aakash Kaku
Weicheng Zhu
M. Leibovich
S. Mohan
...
Haoxiang Huang
L. Zanna
N. Razavian
Jonathan Niles-Weed
C. Fernandez‐Granda
UQCV
OOD
28
14
0
21 Nov 2021
Meta-Calibration: Learning of Model Calibration Using Differentiable
  Expected Calibration Error
Meta-Calibration: Learning of Model Calibration Using Differentiable Expected Calibration Error
Ondrej Bohdal
Yongxin Yang
Timothy M. Hospedales
UQCV
OOD
43
21
0
17 Jun 2021
MLP-Mixer: An all-MLP Architecture for Vision
MLP-Mixer: An all-MLP Architecture for Vision
Ilya O. Tolstikhin
N. Houlsby
Alexander Kolesnikov
Lucas Beyer
Xiaohua Zhai
...
Andreas Steiner
Daniel Keysers
Jakob Uszkoreit
Mario Lucic
Alexey Dosovitskiy
271
2,603
0
04 May 2021
MetNet: A Neural Weather Model for Precipitation Forecasting
MetNet: A Neural Weather Model for Precipitation Forecasting
C. Sønderby
L. Espeholt
Jonathan Heek
Mostafa Dehghani
Avital Oliver
Tim Salimans
Shreya Agrawal
Jason Hickey
Nal Kalchbrenner
AI4Cl
228
273
0
24 Mar 2020
Simple and Scalable Predictive Uncertainty Estimation using Deep
  Ensembles
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan
Alexander Pritzel
Charles Blundell
UQCV
BDL
276
5,661
0
05 Dec 2016
Previous
12