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1703.04977
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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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Hyperbolic Active Learning for Semantic Segmentation under Domain Shift
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Balanced Energy Regularization Loss for Out-of-distribution Detection
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CLARA: Classifying and Disambiguating User Commands for Reliable Interactive Robotic Agents
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Multi-view 3D Object Reconstruction and Uncertainty Modelling with Neural Shape Prior
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Is the Volume of a Credal Set a Good Measure for Epistemic Uncertainty?
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207
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Overcoming the Limitations of Localization Uncertainty: Efficient & Exact Non-Linear Post-Processing and Calibration
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147
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15 Jun 2023
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Deep Gaussian Mixture Ensembles
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185
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Estimating the Uncertainty in Emotion Attributes using Deep Evidential Regression
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Chuxu Zhang
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137
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Variational Imbalanced Regression: Fair Uncertainty Quantification via Probabilistic Smoothing
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Hao Wang
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Conformalizing Machine Translation Evaluation
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André F.T. Martins
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Efficient Uncertainty Quantification and Reduction for Over-Parameterized Neural Networks
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Topology-Aware Uncertainty for Image Segmentation
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Xiaoling Hu
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Lue Fan
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219
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A. Kummert
227
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108
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Learning Probabilistic Coordinate Fields for Robust Correspondences
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Weiyue Zhao
Hao Lu
Xinyi Ye
Zhiguo Cao
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223
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Moritz Luszek
Jan Siegemund
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167
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Gibbs Sampling the Posterior of Neural Networks
Giovanni Piccioli
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Lenka Zdeborová
255
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Multiple output samples per input in a single-output Gaussian process
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Nancy F. Chen
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201
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Haochen Wang
Yuchao Wang
Yujun Shen
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Yuxi Wang
Zhaoxiang Zhang
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242
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04 Jun 2023
Sen2Pro: A Probabilistic Perspective to Sentence Embedding from Pre-trained Language Model
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Haiyun Jiang
Lemao Liu
Shuming Shi
197
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04 Jun 2023
Probabilistic Concept Bottleneck Models
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Eunji Kim
Dahuin Jung
Sangha Park
Siwon Kim
Sung-Hoon Yoon
383
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02 Jun 2023
dugMatting: Decomposed-Uncertainty-Guided Matting
International Conference on Machine Learning (ICML), 2023
Jiawei Wu
Changqing Zhang
Zuoyong Li
Huazhu Fu
Xi Peng
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185
6
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02 Jun 2023
Calibrating Multimodal Learning
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Huanrong Zhang
Changqing Zhang
Bing Wu
Huazhu Fu
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Q. Hu
233
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02 Jun 2023
Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding
International Conference on Learning Representations (ICLR), 2023
Alizée Pace
Hugo Yèche
Bernhard Schölkopf
Gunnar Rätsch
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202
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Per-Erik Forssén
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395
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Symmetric Uncertainty-Aware Feature Transmission for Depth Super-Resolution
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Wuxuan Shi
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302
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Quantifying Representation Reliability in Self-Supervised Learning Models
Conference on Uncertainty in Artificial Intelligence (UAI), 2023
Young-Jin Park
Hao Wang
Shervin Ardeshir
Navid Azizan
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278
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31 May 2023
A technique to jointly estimate depth and depth uncertainty for unmanned aerial vehicles
International Conference on Systems, Signals, and Image Processing (ICSSIP), 2023
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237
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31 May 2023
DiffLoad: Uncertainty Quantification in Load Forecasting with Diffusion Model
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Zhixian Wang
Qingsong Wen
Chaoli Zhang
Liang Sun
Yi Wang
DiffM
311
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Elongated Physiological Structure Segmentation via Spatial and Scale Uncertainty-aware Network
International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2023
Yinglin Zhang
Ruiling Xi
Huazhu Fu
D. Towey
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152
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USIM-DAL: Uncertainty-aware Statistical Image Modeling-based Dense Active Learning for Super-resolution
Conference on Uncertainty in Artificial Intelligence (UAI), 2023
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Uddeshya Upadhyay
Zeynep Akata
Biplab Banerjee
206
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Xiaofeng Liu
Jonghye Woo
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Uncertainty Quantification over Graph with Conformalized Graph Neural Networks
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Guoliang Li
He Li
Shuai Wang
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162
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Distribution-Free Model-Agnostic Regression Calibration via Nonparametric Methods
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Zhongze Cai
Xiaocheng Li
199
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International Conference on 3D Vision (3DV), 2023
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203
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Dennis Becker
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201
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Towards Generalizable Medical Image Segmentation with Pixel-wise Uncertainty Estimation
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Daoan Zhang
Zhong Li
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Ruizhen Li
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194
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A Survey on Deep Learning-Based Monocular Spacecraft Pose Estimation: Current State, Limitations and Prospects
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Fully Bayesian VIB-DeepSSM
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UQ for Credit Risk Management: A deep evidence regression approach
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