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Lightning UQ Box: A Comprehensive Framework for Uncertainty
  Quantification in Deep Learning

Lightning UQ Box: A Comprehensive Framework for Uncertainty Quantification in Deep Learning

4 October 2024
Nils Lehmann
Jakob Gawlikowski
Adam J. Stewart
Vytautas Jancauskas
Stefan Depeweg
Eric T. Nalisnick
N. Gottschling
ArXiv (abs)PDFHTMLGithub (179★)

Papers citing "Lightning UQ Box: A Comprehensive Framework for Uncertainty Quantification in Deep Learning"

23 / 23 papers shown
Title
Digital Typhoon: Long-term Satellite Image Dataset for the
  Spatio-Temporal Modeling of Tropical Cyclones
Digital Typhoon: Long-term Satellite Image Dataset for the Spatio-Temporal Modeling of Tropical Cyclones
A. Kitamoto
Jared Hwang
Bastien Vuillod
Lucas Gautier
Yingtao Tian
Tarin Clanuwat
29
12
0
05 Nov 2023
Do Bayesian Neural Networks Need To Be Fully Stochastic?
Do Bayesian Neural Networks Need To Be Fully Stochastic?
Mrinank Sharma
Sebastian Farquhar
Eric T. Nalisnick
Tom Rainforth
BDL
64
55
0
11 Nov 2022
CARD: Classification and Regression Diffusion Models
CARD: Classification and Regression Diffusion Models
Xizewen Han
Huangjie Zheng
Mingyuan Zhou
DiffM
97
115
0
15 Jun 2022
Uncertainty Toolbox: an Open-Source Library for Assessing, Visualizing,
  and Improving Uncertainty Quantification
Uncertainty Toolbox: an Open-Source Library for Assessing, Visualizing, and Improving Uncertainty Quantification
Youngseog Chung
I. Char
Han Guo
J. Schneider
Willie Neiswanger
72
71
0
21 Sep 2021
A Gentle Introduction to Conformal Prediction and Distribution-Free
  Uncertainty Quantification
A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification
Anastasios Nikolas Angelopoulos
Stephen Bates
OOD
201
617
0
15 Jul 2021
A Survey of Uncertainty in Deep Neural Networks
A Survey of Uncertainty in Deep Neural Networks
J. Gawlikowski
Cedrique Rovile Njieutcheu Tassi
Mohsin Ali
Jongseo Lee
Matthias Humt
...
R. Roscher
Muhammad Shahzad
Wen Yang
R. Bamler
Xiaoxiang Zhu
BDLUQCVOOD
229
1,150
0
07 Jul 2021
Valid prediction intervals for regression problems
Valid prediction intervals for regression problems
Nicolas Dewolf
B. De Baets
Willem Waegeman
151
45
0
01 Jul 2021
Laplace Redux -- Effortless Bayesian Deep Learning
Laplace Redux -- Effortless Bayesian Deep Learning
Erik A. Daxberger
Agustinus Kristiadi
Alexander Immer
Runa Eschenhagen
Matthias Bauer
Philipp Hennig
BDLUQCV
218
313
0
28 Jun 2021
Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep
  Learning
Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning
Zachary Nado
Neil Band
Mark Collier
Josip Djolonga
Michael W. Dusenberry
...
D. Sculley
Balaji Lakshminarayanan
Jasper Snoek
Y. Gal
Dustin Tran
UQCVELM
83
96
0
07 Jun 2021
What Are Bayesian Neural Network Posteriors Really Like?
What Are Bayesian Neural Network Posteriors Really Like?
Pavel Izmailov
Sharad Vikram
Matthew D. Hoffman
A. Wilson
UQCVBDL
72
385
0
29 Apr 2021
On Feature Collapse and Deep Kernel Learning for Single Forward Pass
  Uncertainty
On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertainty
Joost R. van Amersfoort
Lewis Smith
Andrew Jesson
Oscar Key
Y. Gal
UQCV
69
104
0
22 Feb 2021
A Review of Uncertainty Quantification in Deep Learning: Techniques,
  Applications and Challenges
A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges
Moloud Abdar
Farhad Pourpanah
Sadiq Hussain
Dana Rezazadegan
Li Liu
...
Xiaochun Cao
Abbas Khosravi
U. Acharya
V. Makarenkov
S. Nahavandi
BDLUQCV
341
1,922
0
12 Nov 2020
Uncertainty Sets for Image Classifiers using Conformal Prediction
Uncertainty Sets for Image Classifiers using Conformal Prediction
Anastasios Nikolas Angelopoulos
Stephen Bates
Jitendra Malik
Michael I. Jordan
UQCV
205
337
0
29 Sep 2020
Simple and Principled Uncertainty Estimation with Deterministic Deep
  Learning via Distance Awareness
Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness
Jeremiah Zhe Liu
Zi Lin
Shreyas Padhy
Dustin Tran
Tania Bedrax-Weiss
Balaji Lakshminarayanan
UQCVBDL
171
451
0
17 Jun 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
511
42,449
0
03 Dec 2019
Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction
  to Concepts and Methods
Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods
Eyke Hüllermeier
Willem Waegeman
PERUD
244
1,415
0
21 Oct 2019
Deep Evidential Regression
Deep Evidential Regression
Alexander Amini
Wilko Schwarting
A. Soleimany
Daniela Rus
EDLPERBDLUDUQCV
79
440
0
07 Oct 2019
Can You Trust Your Model's Uncertainty? Evaluating Predictive
  Uncertainty Under Dataset Shift
Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift
Yaniv Ovadia
Emily Fertig
Jie Jessie Ren
Zachary Nado
D. Sculley
Sebastian Nowozin
Joshua V. Dillon
Balaji Lakshminarayanan
Jasper Snoek
UQCV
167
1,695
0
06 Jun 2019
A Simple Baseline for Bayesian Uncertainty in Deep Learning
A Simple Baseline for Bayesian Uncertainty in Deep Learning
Wesley J. Maddox
T. Garipov
Pavel Izmailov
Dmitry Vetrov
A. Wilson
BDLUQCV
84
808
0
07 Feb 2019
Deep Learning in Robotics: A Review of Recent Research
Deep Learning in Robotics: A Review of Recent Research
H. A. Pierson
Michael S. Gashler
3DV
72
270
0
22 Jul 2017
Selective Classification for Deep Neural Networks
Selective Classification for Deep Neural Networks
Yonatan Geifman
Ran El-Yaniv
CVBM
95
527
0
23 May 2017
Deep Kernel Learning
Deep Kernel Learning
A. Wilson
Zhiting Hu
Ruslan Salakhutdinov
Eric Xing
BDL
246
886
0
06 Nov 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
UQCVBDL
821
9,318
0
06 Jun 2015
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