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1703.04977
Cited By
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
15 March 2017
Alex Kendall
Y. Gal
BDL
OOD
UD
UQCV
PER
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Papers citing
"What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?"
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Title
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Uncertainty-aware Long-tailed Weights Model the Utility of Pseudo-labels for Semi-supervised Learning
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Junbiao Pang
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L-FUSION: Laplacian Fetal Ultrasound Segmentation & Uncertainty Estimation
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Samuel Budd
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Certainly Bot Or Not? Trustworthy Social Bot Detection via Robust Multi-Modal Neural Processes
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Yingguang Yang
Hao Liu
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3DH
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64
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Hao Xu
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Sen Lin
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159
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3DGS
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Zhongsen Li
Chenlin Du
Haokun Li
Yajie Wang
Huijun Chen
62
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Yuqian Zhang
Hang Liu
38
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49
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52
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Sheng-Jun Huang
EDL
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Kianoush Falahkheirkhah
Rohit Bhargava
57
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Multi-label out-of-distribution detection via evidential learning
Eduardo Aguilar
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OODD
58
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45
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UD
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66
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102
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Thomas Mortier
Eyke Hüllermeier
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Willem Waegeman
39
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Jon Vadillo
Roberto Santana
J. A. Lozano
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BDL
AAML
73
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Aziz Shameem
Mathieu Salzmann
Alexandre Alahi
44
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Conditional Distribution Quantization in Machine Learning
Blaise Delattre
Sylvain Delattre
Alexandre Verine
Alexandre Allauzen
53
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Amir El-Ghoussani
Vasileios Belagiannis
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59
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Reza Pourreza
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Roland Memisevic
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81
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Jörg Martin
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75
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46
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Jonathan Plenk
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67
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Hans Hallez
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The Curious Case of Arbitrariness in Machine Learning
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64
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116
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Towards Robust Unsupervised Attention Prediction in Autonomous Driving
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Remi Chierchia
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Classifier Ensemble for Efficient Uncertainty Calibration of Deep Neural Networks for Image Classification
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78
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Analog Bayesian neural networks are insensitive to the shape of the weight distribution
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Xinzhu Ma
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