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On Calibration of Modern Neural Networks
v1v2 (latest)

On Calibration of Modern Neural Networks

International Conference on Machine Learning (ICML), 2017
14 June 2017
Chuan Guo
Geoff Pleiss
Yu Sun
Kilian Q. Weinberger
    UQCV
ArXiv (abs)PDFHTML

Papers citing "On Calibration of Modern Neural Networks"

50 / 3,766 papers shown
Uncertainty-Aware Step-wise Verification with Generative Reward Models
Uncertainty-Aware Step-wise Verification with Generative Reward Models
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Luckeciano C. Melo
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Shivalika Singh
Yarin Gal
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AI Generations: From AI 1.0 to AI 4.0
AI Generations: From AI 1.0 to AI 4.0
Jiahao Wu
Hengxu You
Jing Du
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226
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Smoothing Out Hallucinations: Mitigating LLM Hallucination with Smoothed Knowledge Distillation
Smoothing Out Hallucinations: Mitigating LLM Hallucination with Smoothed Knowledge Distillation
Hieu Nguyen
Zihao He
Shoumik Atul Gandre
Ujjwal Pasupulety
Sharanya Kumari Shivakumar
Kristina Lerman
HILM
348
11
0
16 Feb 2025
GeneralizeFormer: Layer-Adaptive Model Generation across Test-Time Distribution Shifts
GeneralizeFormer: Layer-Adaptive Model Generation across Test-Time Distribution ShiftsIEEE Workshop/Winter Conference on Applications of Computer Vision (WACV), 2025
Sameer Ambekar
Zehao Xiao
Xiantong Zhen
Cees G. M. Snoek
OOD
450
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15 Feb 2025
Representation Learning on Out of Distribution in Tabular Data
Representation Learning on Out of Distribution in Tabular Data
Achmad Ginanjar
Xue Li
Priyanka Singh
Wen Hua
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979
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0
14 Feb 2025
On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms
On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms
Luke E. Richards
Jessie Yaros
Jasen Babcock
Coung Ly
Robin Cosbey
Timothy Doster
Cynthia Matuszek
NAI
298
2
0
13 Feb 2025
Diverse Transformer Decoding for Offline Reinforcement Learning Using Financial Algorithmic Approaches
Diverse Transformer Decoding for Offline Reinforcement Learning Using Financial Algorithmic Approaches
D. Elbaz
Oren Salzman
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343
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Is LLM an Overconfident Judge? Unveiling the Capabilities of LLMs in Detecting Offensive Language with Annotation Disagreement
Is LLM an Overconfident Judge? Unveiling the Capabilities of LLMs in Detecting Offensive Language with Annotation DisagreementAnnual Meeting of the Association for Computational Linguistics (ACL), 2025
Junyu Lu
Kai Ma
Kaichun Wang
Kelaiti Xiao
Roy Ka-Wei Lee
Bo Xu
Liang Yang
Hongfei Lin
352
0
0
10 Feb 2025
Generalized Venn and Venn-Abers Calibration with Applications in Conformal Prediction
Generalized Venn and Venn-Abers Calibration with Applications in Conformal Prediction
L. Laan
Ahmed Alaa
433
4
0
08 Feb 2025
Confidence Elicitation: A New Attack Vector for Large Language Models
Confidence Elicitation: A New Attack Vector for Large Language ModelsInternational Conference on Learning Representations (ICLR), 2025
Brian Formento
Chuan-Sheng Foo
See-Kiong Ng
AAML
597
2
0
07 Feb 2025
Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models
Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR ModelsArtificial Intelligence in the Life Sciences (AILS), 2025
Hannah Rosa Friesacher
Emma Svensson
S. Winiwarter
Lewis H. Mervin
Adam Arany
Ola Engkvist
OOD
193
3
0
06 Feb 2025
Addressing Label Shift in Distributed Learning via Entropy Regularization
Addressing Label Shift in Distributed Learning via Entropy RegularizationInternational Conference on Learning Representations (ICLR), 2025
Zhiyuan Wu
Changkyu Choi
Xiangcheng Cao
Volkan Cevher
Ali Ramezani-Kebrya
378
0
0
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The Capabilities and Limitations of Weak-to-Strong Generalization: Generalization and Calibration
The Capabilities and Limitations of Weak-to-Strong Generalization: Generalization and Calibration
Wei Yao
Wenkai Yang
Liang Luo
Yankai Lin
Yong Liu
Yong Liu
ELM
963
3
0
03 Feb 2025
Rethinking Early Stopping: Refine, Then Calibrate
Rethinking Early Stopping: Refine, Then Calibrate
Eugene Berta
David Holzmüller
Michael I. Jordan
Francis Bach
381
6
0
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Technical report on label-informed logit redistribution for better domain generalization in low-shot classification with foundation models
Technical report on label-informed logit redistribution for better domain generalization in low-shot classification with foundation models
Behraj Khan
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1.1K
2
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Extending Information Bottleneck Attribution to Video Sequences
Extending Information Bottleneck Attribution to Video Sequences
Veronika Solopova
Lucas Schmidt
Dorothea Kolossa
278
1
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Random Forest Calibration
Random Forest CalibrationKnowledge-Based Systems (KBS), 2025
M. Shaker
Eyke Hüllermeier
151
4
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BLoB: Bayesian Low-Rank Adaptation by Backpropagation for Large Language Models
BLoB: Bayesian Low-Rank Adaptation by Backpropagation for Large Language ModelsNeural Information Processing Systems (NeurIPS), 2024
Yibin Wang
Haizhou Shi
Ligong Han
Dimitris N. Metaxas
Hao Wang
BDLUQLM
735
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28 Jan 2025
Conformalized Answer Set Prediction for Knowledge Graph Embedding
Conformalized Answer Set Prediction for Knowledge Graph EmbeddingNorth American Chapter of the Association for Computational Linguistics (NAACL), 2024
Yuqicheng Zhu
Nico Potyka
Jiarong Pan
Bo Xiong
Yunjie He
Evgeny Kharlamov
Steffen Staab
462
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HopCast: Calibration of Autoregressive Dynamics Models
HopCast: Calibration of Autoregressive Dynamics Models
Muhammad Bilal Shahid
Cody H. Fleming
UQCV
487
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Breaking the SSL-AL Barrier: A Synergistic Semi-Supervised Active Learning Framework for 3D Object Detection
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Zengran Wang
Yanan Zhang
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389
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As Confidence Aligns: Exploring the Effect of AI Confidence on Human Self-confidence in Human-AI Decision Making
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Jingshu Li
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259
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Conformal Prediction of Classifiers with Many Classes based on Noisy Labels
Conformal Prediction of Classifiers with Many Classes based on Noisy LabelsInternational Symposium on Conformal and Probabilistic Prediction with Applications (ISCPPA), 2025
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Jacob Goldberger
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329
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Uncertainty Quantification With Noise Injection in Neural Networks: A Bayesian Perspective
Uncertainty Quantification With Noise Injection in Neural Networks: A Bayesian Perspective
Xueqiong Yuan
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E. Kuruoglu
UQCVBDL
280
1
0
21 Jan 2025
Enhancing Uncertainty Estimation in Semantic Segmentation via Monte-Carlo Frequency Dropout
Enhancing Uncertainty Estimation in Semantic Segmentation via Monte-Carlo Frequency DropoutIEEE International Symposium on Biomedical Imaging (ISBI), 2025
Tal Zeevi
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239
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Classifier Ensemble for Efficient Uncertainty Calibration of Deep Neural Networks for Image Classification
Classifier Ensemble for Efficient Uncertainty Calibration of Deep Neural Networks for Image Classification
Michael Schulze
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Oliver Wasenmüller
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229
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Confidence Estimation for Error Detection in Text-to-SQL Systems
Confidence Estimation for Error Detection in Text-to-SQL SystemsAAAI Conference on Artificial Intelligence (AAAI), 2025
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Confidence-Driven Deep Learning Framework for Early Detection of Knee Osteoarthritis
Confidence-Driven Deep Learning Framework for Early Detection of Knee Osteoarthritis
Zhe Wang
A. Chetouani
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Fang Chen
M. Jarraya
Fabian Bauer
Liping Zhang
Didier Hans
Rachid Jennane
251
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A Comparative Study on Multi-task Uncertainty Quantification in Semantic Segmentation and Monocular Depth Estimation
A Comparative Study on Multi-task Uncertainty Quantification in Semantic Segmentation and Monocular Depth EstimationTM. Technisches Messen (TM), 2024
S. Landgraf
Markus Hillemann
Theodor Kapler
Markus Ulrich
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187
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On the challenges of detecting MCI using EEG in the wild
On the challenges of detecting MCI using EEG in the wild
Aayush Mishra
David Joffe
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David S Oakley
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310
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Can Bayesian Neural Networks Explicitly Model Input Uncertainty?
Can Bayesian Neural Networks Explicitly Model Input Uncertainty?
Matias Valdenegro-Toro
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A Critical Synthesis of Uncertainty Quantification and Foundation Models in Monocular Depth Estimation
A Critical Synthesis of Uncertainty Quantification and Foundation Models in Monocular Depth Estimation
S. Landgraf
Rongjun Qin
Markus Ulrich
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292
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Uncertainty Guarantees on Automated Precision Weeding using Conformal Prediction
Uncertainty Guarantees on Automated Precision Weeding using Conformal Prediction
P. Melki
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Jérôme Dias
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206
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Aligning NLP Models with Target Population Perspectives using PAIR: Population-Aligned Instance Replication
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Cascaded Self-Evaluation Augmented Training for Lightweight Multimodal LLMs
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Tougher Text, Smarter Models: Raising the Bar for Adversarial Defence Benchmarks
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773
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Tuning Vision-Language Models with Candidate Labels by Prompt Alignment
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Contrastive Conditional Alignment based on Label Shift Calibration for Imbalanced Domain Adaptation
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