大模型对齐与安全 - arXivDaily 专题

2301.08914 2023-01-24 cs.CL 57%

ExClaim: Explainable Neural Claim Verification Using Rationalization

Sai Gurrapu, Lifu Huang, Feras A. Batarseh

专题命中幻觉与事实性：trustworthy(abstract)；分类 cs.CL

Comments Published at 2022 IEEE 29th STC

2206.00002 2023-01-11 eess.IV cs.CV cs.LG 57%

Calibrated Bagging Deep Learning for Image Semantic Segmentation: A Case Study on COVID-19 Chest X-ray Image

Lucy Nwosu, Xiangfang Li, Lijun Qian, Seungchan Kim, Xishuang Dong

专题命中幻觉与事实性：safety(abstract)；分类 cs.LG

2212.14709 2023-01-02 cs.LG cond-mat.mtrl-sci 57%

A Learning-Based Optimal Uncertainty Quantification Method and Its Application to Ballistic Impact Problems

Xingsheng Sun, Burigede Liu

专题命中幻觉与事实性：safety(abstract)；分类 cs.LG

2212.08370 2022-12-19 cs.LG 57%

Shapley variable importance cloud for machine learning models

Yilin Ning, Mingxuan Liu, Nan Liu

专题命中幻觉与事实性：trustworthy(abstract)；分类 cs.LG

2211.08701 2022-11-17 cs.RO cs.CV cs.LG 57%

Interpretable Self-Aware Neural Networks for Robust Trajectory Prediction

Masha Itkina, Mykel J. Kochenderfer

专题命中幻觉与事实性：safety(abstract)；分类 cs.LG

Comments Conference on Robot Learning (CoRL) 2022, 15 pages, 4 figures

2210.17030 2022-11-03 q-fin.CP cs.LG 57%

Uncertainty Aware Trader-Company Method: Interpretable Stock Price Prediction Capturing Uncertainty

Yugo Fujimoto, Kei Nakagawa, Kentaro Imajo, Kentaro Minami

专题命中幻觉与事实性：safety(abstract)；分类 cs.LG

Comments IEEE BIGDATA 2022 Accepted

2208.03284 2022-11-02 hep-ex cs.LG hep-ph stat.ML 57%

Interpretable Uncertainty Quantification in AI for HEP

Thomas Y. Chen, Biprateep Dey, Aishik Ghosh, Michael Kagan, Brian Nord, Nesar Ramachandra

专题命中幻觉与事实性：trustworthy(abstract)；分类 cs.LG

Comments Submitted to the Proceedings of the US Community Study on the Future of Particle Physics (Snowmass 2021)

2210.14488 2022-10-27 stat.ML cs.LG cs.NA math.NA nlin.CD physics.ao-ph 57%

History-Based, Bayesian, Closure for Stochastic Parameterization: Application to Lorenz '96

Mohamed Aziz Bhouri, Pierre Gentine

专题命中幻觉与事实性：trustworthy(abstract)；分类 cs.LG

2210.12220 2022-10-25 cs.HC cs.LG 57%

Considerations for Visualizing Uncertainty in Clinical Machine Learning Models

Caitlin F. Harrigan, Gabriela Morgenshtern, Anna Goldenberg, Fanny Chevalier

专题命中幻觉与事实性：trustworthy(abstract)；分类 cs.LG

Comments Prepared for the CHI 2021 Workshop: Realizing AI in Healthcare: Challenges Appearing in the Wild https://dl.acm.org/doi/10.1145/3411763.3441347

2205.14094 2022-10-25 cs.AI 57%

Failure Detection in Medical Image Classification: A Reality Check and Benchmarking Testbed

Melanie Bernhardt, Fabio De Sousa Ribeiro, Ben Glocker

专题命中幻觉与事实性：safety(abstract)；分类 cs.AI

Comments Published in Transactions on Machine Learning Research (10/2022)

Journal ref Transactions on Machine Learning Research (10/2022)

2210.10526 2022-10-20 cs.LG cs.SD eess.AS 57%

Propagating Variational Model Uncertainty for Bioacoustic Call Label Smoothing

Georgios Rizos, Jenna Lawson, Simon Mitchell, Pranay Shah, Xin Wen, Cristina Banks-Leite, Robert Ewers, Bjoern W. Schuller

专题命中幻觉与事实性：trustworthy(abstract)；分类 cs.LG

2210.10160 2022-10-20 cs.LG 57%

Uncertainty in Extreme Multi-label Classification

Jyun-Yu Jiang, Wei-Cheng Chang, Jiong Zhong, Cho-Jui Hsieh, Hsiang-Fu Yu

专题命中幻觉与事实性：trustworthy(abstract)；分类 cs.LG

Comments 14 pages, 1 figure, 8 tables

2210.09467 2022-10-19 cs.IR cs.AI 57%

Adversarial and Safely Scaled Question Generation

Sreehari Sankar, Zhihang Dong

专题命中幻觉与事实性：safety(abstract)；分类 cs.AI

Comments 15 pages, 8 figures, 2 tables

2210.00074 2022-10-17 cs.LG stat.AP 57%

Leveraging Industry 4.0 -- Deep Learning, Surrogate Model and Transfer Learning with Uncertainty Quantification Incorporated into Digital Twin for Nuclear System

M. Rahman, Abid Khan, Sayeed Anowar, Md Al-Imran, Richa Verma, Dinesh Kumar, Kazuma Kobayashi, Syed Alam

专题命中幻觉与事实性：safety(abstract)；分类 cs.LG

2207.02440 2022-10-17 cs.LG stat.ML 57%

PAC Prediction Sets for Meta-Learning

Sangdon Park, Edgar Dobriban, Insup Lee, Osbert Bastani

专题命中幻觉与事实性：safety(abstract)；分类 cs.LG

Comments Accepted to NeurIPS 2022

2206.01323 2022-10-14 cs.LG eess.SP 57%

SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG

Reinmar J Kobler, Jun-ichiro Hirayama, Qibin Zhao, Motoaki Kawanabe

专题命中幻觉与事实性：alignment(abstract)；分类 cs.LG

Comments 10 pages, accepted at NeurIPS 2022

2208.07464 2022-09-28 cs.LG math.OC stat.ML 57%

An Overview and Prospective Outlook on Robust Training and Certification of Machine Learning Models

Brendon G. Anderson, Tanmay Gautam, Somayeh Sojoudi

专题命中幻觉与事实性：safety(abstract)；分类 cs.LG

2208.01705 2022-08-09 cs.LG 57%

Success of Uncertainty-Aware Deep Models Depends on Data Manifold Geometry

Mark Penrod, Harrison Termotto, Varshini Reddy, Jiayu Yao, Finale Doshi-Velez, Weiwei Pan

专题命中幻觉与事实性：safety(abstract)；分类 cs.LG

Journal ref International Conference on Machine Learning. PMLR 162 (2022)

2207.13341 2022-07-28 cs.LG 57%

Towards Clear Expectations for Uncertainty Estimation

Victor Bouvier, Simona Maggio, Alexandre Abraham, Léo Dreyfus-Schmidt

专题命中幻觉与事实性：trustworthy(abstract)；分类 cs.LG

2207.03324 2022-07-08 cs.LG 57%

Calibrate to Interpret

Gregory Scafarto, Nicolas Posocco, Antoine Bonnefoy

专题命中幻觉与事实性：trustworthy(abstract)；分类 cs.LG

Comments 16 pages, 9 figures, accepted at ECML PKDD 2022

2206.10718 2022-06-23 physics.comp-ph cs.LG physics.geo-ph 57%

Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management

Aleksandra Pachalieva, Daniel O'Malley, Dylan Robert Harp, Hari Viswanathan

专题命中幻觉与事实性：trustworthy(abstract)；分类 cs.LG

Comments 12 pages, 5 figures

2201.08418 2022-06-03 eess.IV cs.AI cs.CV physics.med-ph 57%

SoftDropConnect (SDC) -- Effective and Efficient Quantification of the Network Uncertainty in Deep MR Image Analysis

Qing Lyu, Christopher T. Whitlow, Ge Wang

专题命中幻觉与事实性：trustworthy(abstract)；分类 cs.AI

2205.15265 2022-05-31 cs.LG cs.CV 57%

Going Beyond One-Hot Encoding in Classification: Can Human Uncertainty Improve Model Performance?

Christoph Koller, Göran Kauermann, Xiao Xiang Zhu

专题命中幻觉与事实性：trustworthy(abstract)；分类 cs.LG

2204.00382 2022-05-12 cs.LG cs.CV 57%

Autoencoder Attractors for Uncertainty Estimation

Steve Dias Da Cruz, Bertram Taetz, Thomas Stifter, Didier Stricker

专题命中幻觉与事实性：safety(abstract)；分类 cs.LG

Comments This paper is accepted at IEEE International Conference on Pattern Recognition (ICPR), 2022

2204.13963 2022-05-02 cs.LG 57%

Tailored Uncertainty Estimation for Deep Learning Systems

Joachim Sicking, Maram Akila, Jan David Schneider, Fabian Hüger, Peter Schlicht, Tim Wirtz, Stefan Wrobel

专题命中幻觉与事实性：trustworthy(abstract)；分类 cs.LG

2203.06290 2022-03-15 cs.LG cs.SY eess.SY math.OC 57%

SOCKS: A Stochastic Optimal Control and Reachability Toolbox Using Kernel Methods

Adam J. Thorpe, Meeko M. K. Oishi

专题命中幻觉与事实性：safety(abstract)；分类 cs.LG

2104.06135 2022-02-25 cs.LG cs.NE stat.ML 57%

Multivariate Deep Evidential Regression

Nis Meinert, Alexander Lavin

专题命中幻觉与事实性：safety(abstract)；分类 cs.LG

Comments 20 pages, 13 figures

2112.09196 2022-01-26 cs.LG 57%

Benchmarking Uncertainty Quantification on Biosignal Classification Tasks under Dataset Shift

Tong Xia, Jing Han, Cecilia Mascolo

专题命中幻觉与事实性：trustworthy(abstract)；分类 cs.LG

Comments Accepted by The 6th International Workshop on Health Intelligence (W3PHIAI-22)

2201.05669 2022-01-19 q-bio.QM cs.LG 57%

Prediction of Drug-Induced TdP Risks Using Machine Learning and Rabbit Ventricular Wedge Assay

Nan Miles Xi, Dalong Patrick Huang

专题命中幻觉与事实性：safety(abstract)；分类 cs.LG

Comments arXiv admin note: text overlap with arXiv:2108.00543

2102.12680 2021-12-28 cs.LG 57%

Confidence Calibration with Bounded Error Using Transformations

Sooyong Jang, Radoslav Ivanov, Insup Lee, James Weimer

专题命中幻觉与事实性：safety(abstract)；分类 cs.LG

1. 幻觉与事实性 1738 篇

ExClaim: Explainable Neural Claim Verification Using Rationalization

Calibrated Bagging Deep Learning for Image Semantic Segmentation: A Case Study on COVID-19 Chest X-ray Image

A Learning-Based Optimal Uncertainty Quantification Method and Its Application to Ballistic Impact Problems

Shapley variable importance cloud for machine learning models

Interpretable Self-Aware Neural Networks for Robust Trajectory Prediction

Uncertainty Aware Trader-Company Method: Interpretable Stock Price Prediction Capturing Uncertainty

Interpretable Uncertainty Quantification in AI for HEP

History-Based, Bayesian, Closure for Stochastic Parameterization: Application to Lorenz '96

Considerations for Visualizing Uncertainty in Clinical Machine Learning Models

Failure Detection in Medical Image Classification: A Reality Check and Benchmarking Testbed

Propagating Variational Model Uncertainty for Bioacoustic Call Label Smoothing

Uncertainty in Extreme Multi-label Classification

Adversarial and Safely Scaled Question Generation

Leveraging Industry 4.0 -- Deep Learning, Surrogate Model and Transfer Learning with Uncertainty Quantification Incorporated into Digital Twin for Nuclear System

PAC Prediction Sets for Meta-Learning

SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG

An Overview and Prospective Outlook on Robust Training and Certification of Machine Learning Models

Success of Uncertainty-Aware Deep Models Depends on Data Manifold Geometry

Towards Clear Expectations for Uncertainty Estimation

Calibrate to Interpret

Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management

SoftDropConnect (SDC) -- Effective and Efficient Quantification of the Network Uncertainty in Deep MR Image Analysis

Going Beyond One-Hot Encoding in Classification: Can Human Uncertainty Improve Model Performance?

Autoencoder Attractors for Uncertainty Estimation

Tailored Uncertainty Estimation for Deep Learning Systems

SOCKS: A Stochastic Optimal Control and Reachability Toolbox Using Kernel Methods

Multivariate Deep Evidential Regression

Benchmarking Uncertainty Quantification on Biosignal Classification Tasks under Dataset Shift

Prediction of Drug-Induced TdP Risks Using Machine Learning and Rabbit Ventricular Wedge Assay

Confidence Calibration with Bounded Error Using Transformations