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1707.02476
Cited By
Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks
8 July 2017
John Bradshaw
A. G. Matthews
Zoubin Ghahramani
BDL
AAML
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Papers citing
"Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks"
50 / 95 papers shown
Response to Promises and Pitfalls of Deep Kernel Learning
A. Wilson
Zhiting Hu
Ruslan Salakhutdinov
Eric P. Xing
UQCV
200
0
0
25 Sep 2025
Revisiting Kernel Attention with Correlated Gaussian Process Representation
Conference on Uncertainty in Artificial Intelligence (UAI), 2025
Long Minh Bui
Tho Tran Huu
Duy-Tung Dinh
T. Nguyen
Trong Nghia Hoang
456
8
0
27 Feb 2025
Probabilistic Degeneracy Detection for Point-to-Plane Error Minimization
IEEE Robotics and Automation Letters (RA-L), 2024
Johan Hatleskog
Kostas Alexis
3DPC
564
13
0
14 Oct 2024
Uncertainty Quantification on Graph Learning: A Survey
Chao Chen
Chenghua Guo
Rui Xu
Xiangwen Liao
Xi Zhang
Sihong Xie
Hui Xiong
Mohit Bansal
Philip Yu
479
3
0
23 Apr 2024
A Kronecker product accelerated efficient sparse Gaussian Process (E-SGP) for flow emulation
Yu Duan
M. Eaton
Michael Bluck
275
0
0
13 Dec 2023
Introducing an Improved Information-Theoretic Measure of Predictive Uncertainty
Kajetan Schweighofer
L. Aichberger
Mykyta Ielanskyi
Sepp Hochreiter
237
25
0
14 Nov 2023
A Hierarchical Spatial Transformer for Massive Point Samples in Continuous Space
Wenchong He
Zhe Jiang
Tingsong Xiao
Zelin Xu
Shigang Chen
Ronald Fick
Miles Medina
Christine Angelini
314
18
0
08 Nov 2023
Modeling groundwater levels in California's Central Valley by hierarchical Gaussian process and neural network regression
Journal of Geophysical Research (JGR), 2023
Anshuman Pradhan
Kyra H Adams
Venkat Chandrasekaran
Zhen Liu
J. Reager
Andrew M. Stuart
M. Turmon
199
5
0
23 Oct 2023
Convolutional Deep Kernel Machines
International Conference on Learning Representations (ICLR), 2023
Edward Milsom
Ben Anson
Laurence Aitchison
BDL
547
6
0
18 Sep 2023
Quantification of Uncertainty with Adversarial Models
Neural Information Processing Systems (NeurIPS), 2023
Kajetan Schweighofer
L. Aichberger
Mykyta Ielanskyi
Günter Klambauer
Sepp Hochreiter
UQCV
390
31
0
06 Jul 2023
Evaluation of machine learning architectures on the quantification of epistemic and aleatoric uncertainties in complex dynamical systems
Stephen Guth
A. Mojahed
T. Sapsis
AI4CE
329
3
0
27 Jun 2023
Mitigating Transformer Overconfidence via Lipschitz Regularization
Conference on Uncertainty in Artificial Intelligence (UAI), 2023
Wenqian Ye
Yunsheng Ma
Xu Cao
Kun Tang
243
19
0
12 Jun 2023
Calibrating Transformers via Sparse Gaussian Processes
International Conference on Learning Representations (ICLR), 2023
Wenlong Chen
Yingzhen Li
UQCV
763
20
0
04 Mar 2023
Guided Deep Kernel Learning
Conference on Uncertainty in Artificial Intelligence (UAI), 2023
Idan Achituve
Gal Chechik
Ethan Fetaya
BDL
351
7
0
19 Feb 2023
Probabilistic Circuits That Know What They Don't Know
Conference on Uncertainty in Artificial Intelligence (UAI), 2023
Fabrizio G. Ventola
Steven Braun
Zhongjie Yu
Martin Mundt
Kristian Kersting
UQCV
TPM
365
11
0
13 Feb 2023
Uncertainty Estimation for Multi-view Data: The Power of Seeing the Whole Picture
Neural Information Processing Systems (NeurIPS), 2022
M. Jung
He Zhao
Joanna Dipnall
Lan Du
Lan Du
UQCV
EDL
241
17
0
06 Oct 2022
On Attacking Out-Domain Uncertainty Estimation in Deep Neural Networks
International Joint Conference on Artificial Intelligence (IJCAI), 2022
Huimin Zeng
Zhenrui Yue
Yang Zhang
Ziyi Kou
Lanyu Shang
Dong Wang
OOD
AAML
216
9
0
03 Oct 2022
Bézier Gaussian Processes for Tall and Wide Data
Neural Information Processing Systems (NeurIPS), 2022
Martin Jørgensen
Michael A. Osborne
GP
405
2
0
01 Sep 2022
Light curve completion and forecasting using fast and scalable Gaussian processes (MuyGPs)
I. Goumiri
Alec M. Dunton
Amanda Muyskens
Benjamin W. Priest
R. E. Armstrong
268
5
0
31 Aug 2022
Deep Kernel Learning of Dynamical Models from High-Dimensional Noisy Data
Scientific Reports (Sci Rep), 2022
N. Botteghi
Mengwu Guo
C. Brune
389
15
0
27 Aug 2022
Interpretable Uncertainty Quantification in AI for HEP
Thomas Y. Chen
B. Dey
A. Ghosh
Michael Kagan
Brian D. Nord
Nesar Ramachandra
411
13
0
05 Aug 2022
Success of Uncertainty-Aware Deep Models Depends on Data Manifold Geometry
M. Penrod
Harrison Termotto
Varshini Reddy
Jiayu Yao
Finale Doshi-Velez
Weiwei Pan
AAML
OOD
275
1
0
02 Aug 2022
Distributional Gaussian Processes Layers for Out-of-Distribution Detection
S. Popescu
D. Sharp
James H. Cole
Konstantinos Kamnitsas
Ben Glocker
OOD
333
1
0
27 Jun 2022
Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction
International Conference on Learning Representations (ICLR), 2022
Jiajun He
Austin Tripp
José Miguel Hernández-Lobato
546
27
0
05 May 2022
A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness
Journal of machine learning research (JMLR), 2022
J. Liu
Shreyas Padhy
Jie Jessie Ren
Zi Lin
Yeming Wen
Ghassen Jerfel
Zachary Nado
Jasper Snoek
Dustin Tran
Balaji Lakshminarayanan
UQCV
BDL
599
71
0
01 May 2022
Unsupervised Restoration of Weather-affected Images using Deep Gaussian Process-based CycleGAN
International Conference on Pattern Recognition (ICPR), 2022
R. Yasarla
Vishwanath A. Sindagi
Vishal M. Patel
309
4
0
23 Apr 2022
DeepAdversaries: Examining the Robustness of Deep Learning Models for Galaxy Morphology Classification
A. Ćiprijanović
Diana Kafkes
Gregory F. Snyder
F. Sánchez
G. Perdue
K. Pedro
Brian D. Nord
Sandeep Madireddy
Stefan M. Wild
AAML
342
24
0
28 Dec 2021
Deep Bayesian Image Set Classification: A Defence Approach against Adversarial Attacks
N. Mirnateghi
Syed Afaq Ali Shah
Bennamoun
BDL
AAML
150
2
0
23 Aug 2021
Uncertainty-Aware Time-to-Event Prediction using Deep Kernel Accelerated Failure Time Models
Machine Learning in Health Care (MLHC), 2021
Zhiliang Wu
Yinchong Yang
Peter A. Fasching
Volker Tresp
BDL
188
11
0
26 Jul 2021
A Modulation Layer to Increase Neural Network Robustness Against Data Quality Issues
M. Abdelhack
Kailai Li
Sandhya Tripathi
Bradley A. Fritz
Daniel Felsky
M. Avidan
Yixin Chen
C. King
320
4
0
19 Jul 2021
Adversarial Attack for Uncertainty Estimation: Identifying Critical Regions in Neural Networks
Neural Processing Letters (NPL), 2021
Ismail Alarab
S. Prakoonwit
AAML
264
16
0
15 Jul 2021
Last Layer Marginal Likelihood for Invariance Learning
International Conference on Artificial Intelligence and Statistics (AISTATS), 2021
Pola Schwobel
Martin Jørgensen
Sebastian W. Ober
Mark van der Wilk
BDL
UQCV
245
31
0
14 Jun 2021
Quantifying Predictive Uncertainty in Medical Image Analysis with Deep Kernel Learning
IEEE International Conference on Healthcare Informatics (ICHI), 2021
Zhiliang Wu
Yinchong Yang
Jindong Gu
Volker Tresp
UQCV
MedIm
145
10
0
01 Jun 2021
Sparse Uncertainty Representation in Deep Learning with Inducing Weights
Neural Information Processing Systems (NeurIPS), 2021
H. Ritter
Martin Kukla
Chen Zhang
Yingzhen Li
UQCV
BDL
247
22
0
30 May 2021
Priors in Bayesian Deep Learning: A Review
International Statistical Review (ISR), 2021
Vincent Fortuin
UQCV
BDL
570
171
0
14 May 2021
Stochastic-Shield: A Probabilistic Approach Towards Training-Free Adversarial Defense in Quantized CNNs
Lorena Qendro
Sangwon Ha
R. D. Jong
Partha P. Maji
AAML
FedML
MQ
197
7
0
13 May 2021
Towards Adversarial Patch Analysis and Certified Defense against Crowd Counting
ACM Multimedia (ACM MM), 2021
Qiming Wu
Zhikang Zou
Pan Zhou
Xiaoqing Ye
Binghui Wang
Ang Li
AAML
362
11
0
22 Apr 2021
Adversarial Robustness Guarantees for Gaussian Processes
Journal of machine learning research (JMLR), 2021
A. Patané
Arno Blaas
Luca Laurenti
L. Cardelli
Stephen J. Roberts
Marta Z. Kwiatkowska
GP
AAML
367
10
0
07 Apr 2021
Calibrated simplex-mapping classification
PLoS ONE (PLOS ONE), 2021
R. Heese
J. Schmid
Michal Walczak
Michael Bortz
304
4
0
04 Mar 2021
Highly Efficient Representation and Active Learning Framework and Its Application to Imbalanced Medical Image Classification
Heng Hao
H. Moon
Sima Didari
J. Woo
P. Bangert
AI4TS
360
0
0
25 Feb 2021
The Promises and Pitfalls of Deep Kernel Learning
Conference on Uncertainty in Artificial Intelligence (UAI), 2021
Sebastian W. Ober
C. Rasmussen
Mark van der Wilk
UQCV
BDL
454
122
0
24 Feb 2021
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
474
122
0
22 Feb 2021
GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning
International Conference on Machine Learning (ICML), 2021
Idan Achituve
Aviv Navon
Yochai Yemini
Gal Chechik
Ethan Fetaya
GP
380
41
0
15 Feb 2021
Key Technology Considerations in Developing and Deploying Machine Learning Models in Clinical Radiology Practice
JMIR Medical Informatics (JMIR Med Inform), 2021
V. Kulkarni
M. Gawali
A. Kharat
VLM
300
29
0
03 Feb 2021
Defence against adversarial attacks using classical and quantum-enhanced Boltzmann machines
Aidan Kehoe
P. Wittek
Yanbo Xue
Alejandro Pozas-Kerstjens
AAML
348
9
0
21 Dec 2020
A Study on the Uncertainty of Convolutional Layers in Deep Neural Networks
International Journal of Machine Learning and Cybernetics (IJMLC), 2020
Hao Shen
Sihong Chen
Ran Wang
229
7
0
27 Nov 2020
A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges
Information Fusion (Inf. Fusion), 2020
Moloud Abdar
Farhad Pourpanah
Sadiq Hussain
Dana Rezazadegan
Tianpeng Liu
...
Xiaochun Cao
Abbas Khosravi
U. Acharya
V. Makarenkov
S. Nahavandi
BDL
UQCV
1.2K
2,489
0
12 Nov 2020
Stationary Activations for Uncertainty Calibration in Deep Learning
Neural Information Processing Systems (NeurIPS), 2020
Lassi Meronen
Christabella Irwanto
Arno Solin
UQCV
BDL
208
21
0
19 Oct 2020
Hybrid Deep Learning Gaussian Process for Diabetic Retinopathy Diagnosis and Uncertainty Quantification
Santiago Toledo-Cortés
Melissa De La Pava
Oscar J. Perdomo
Fabio A. González
BDL
MedIm
140
20
0
29 Jul 2020
Attacking and Defending Machine Learning Applications of Public Cloud
Dou Goodman
Xin Hao
SILM
AAML
214
7
0
27 Jul 2020
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