大模型对齐与安全 - arXivDaily 专题

2112.06986 2021-12-21 cs.LG 57%

On The Reliability Of Machine Learning Applications In Manufacturing Environments

Nicolas Jourdan, Sagar Sen, Erik Johannes Husom, Enrique Garcia-Ceja, Tobias Biegel, Joachim Metternich

专题命中幻觉与事实性：safety(abstract)；分类 cs.LG

Comments Workshop on Distribution Shifts, 35th Conference on Neural Information Processing Systems (NeurIPS 2021)

2111.09808 2021-11-19 cs.LG cs.CV 57%

Exploring the Limits of Epistemic Uncertainty Quantification in Low-Shot Settings

Matias Valdenegro-Toro

专题命中幻觉与事实性：safety(abstract)；分类 cs.LG

Comments 7 pages, 3 figures, with supplementary material. LatinX in AI Research Workshop @ NeurIPS 2021

1911.07391 2021-11-16 cs.LG stat.ML 57%

Justification-Based Reliability in Machine Learning

Nurali Virani, Naresh Iyer, Zhaoyuan Yang

专题命中幻觉与事实性：safety(abstract)；分类 cs.LG

Comments Extended version of paper accepted at AAAI 2020 with supplementary materials, update remark and fix typo

2110.13947 2021-10-28 cs.CV cs.LG 57%

Collaborative Uncertainty in Multi-Agent Trajectory Forecasting

Bohan Tang, Yiqi Zhong, Ulrich Neumann, Gang Wang, Ya Zhang, Siheng Chen

专题命中幻觉与事实性：safety(abstract)；分类 cs.LG

Comments This paper has been accepted by NeurIPS 2021

2110.03553 2021-10-08 cs.AR cs.LG 57%

Shift-BNN: Highly-Efficient Probabilistic Bayesian Neural Network Training via Memory-Friendly Pattern Retrieving

Qiyu Wan, Haojun Xia, Xingyao Zhang, Lening Wang, Shuaiwen Leon Song, Xin Fu

专题命中幻觉与事实性：safety(abstract)；分类 cs.LG

Comments 54th IEEE/ACM International Symposium on Microarchitecture

2109.04432 2021-09-10 cs.LG cs.IR stat.ML 57%

Detecting and Mitigating Test-time Failure Risks via Model-agnostic Uncertainty Learning

Preethi Lahoti, Krishna P. Gummadi, Gerhard Weikum

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

Comments To appear in the 21st IEEE International Conference on Data Mining (ICDM 2021), Auckland, New Zealand

2108.08712 2021-08-20 cs.LG stat.ML 57%

Teaching Uncertainty Quantification in Machine Learning through Use Cases

Matias Valdenegro-Toro

专题命中幻觉与事实性：safety(abstract)；分类 cs.LG

Comments 2nd Teaching in Machine Learning Workshop, Camera Ready, 5 pages, 3 figures

2107.11750 2021-08-02 cs.LG cs.CV 57%

Improving Variational Autoencoder based Out-of-Distribution Detection for Embedded Real-time Applications

Yeli Feng, Daniel Jun Xian Ng, Arvind Easwaran

专题命中幻觉与事实性：safety(abstract)；分类 cs.LG

Comments This article appears as part of the ESWEEK-TECS special issue and will be presented in the International Conference on Embedded Software (EMSOFT), 2021

2107.14261 2021-08-02 physics.acc-ph cs.LG 57%

Quantifying Uncertainty for Machine Learning Based Diagnostic

Owen Convery, Lewis Smith, Yarin Gal, Adi Hanuka

专题命中幻觉与事实性：safety(abstract)；分类 cs.LG

Comments arXiv admin note: substantial text overlap with arXiv:2105.04654

1910.09457 2021-06-24 cs.LG stat.ML 57%

Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods

Eyke Hüllermeier, Willem Waegeman

专题命中幻觉与事实性：safety(abstract)；分类 cs.LG

Comments 59 pages

2010.12007 2021-06-16 cs.LG 57%

PRANK: motion Prediction based on RANKing

Yuriy Biktairov, Maxim Stebelev, Irina Rudenko, Oleh Shliazhko, Boris Yangel

专题命中幻觉与事实性：safety(abstract)；分类 cs.LG

Comments Accepted to NeurIPS 2020

2106.01410 2021-06-07 cs.AI 57%

Uncertainty Quantification 360: A Holistic Toolkit for Quantifying and Communicating the Uncertainty of AI

Soumya Ghosh, Q. Vera Liao, Karthikeyan Natesan Ramamurthy, Jiri Navratil, Prasanna Sattigeri, Kush R. Varshney, Yunfeng Zhang

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

Comments Added references

2106.01170 2021-06-03 cs.CL 57%

Detecting Bot-Generated Text by Characterizing Linguistic Accommodation in Human-Bot Interactions

Paras Bhatt, Anthony Rios

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

Comments 13 pages, to be published in Findings of ACL-IJCNLP 2021

2105.09474 2021-06-03 cs.LG stat.AP 57%

Quantifying sources of uncertainty in drug discovery predictions with probabilistic models

Stanley E. Lazic, Dominic P. Williams

专题命中幻觉与事实性：safety(abstract)；分类 cs.LG

Comments 34 pages, 9 figures

Journal ref Artificial Intelligence in the Life Sciences (2021)

2105.14645 2021-06-01 physics.comp-ph cs.LG 57%

Empirical Models for Multidimensional Regression of Fission Systems

Akshay J. Dave, Jiankai Yu, Jarod Wilson, Bren Phillips, Kaichao Sun, Benoit Forget

专题命中幻觉与事实性：safety(abstract)；分类 cs.LG

Comments 20 pages, 7 figures

2011.08712 2021-05-31 cs.CV cs.LG cs.NE eess.IV 57%

A Simple Framework to Quantify Different Types of Uncertainty in Deep Neural Networks for Image Classification

Aria Khoshsirat

专题命中幻觉与事实性：safety(abstract)；分类 cs.LG

2104.03834 2021-04-09 cs.LG cs.DC stat.ML 57%

Bayesian Variational Federated Learning and Unlearning in Decentralized Networks

Jinu Gong, Osvaldo Simeone, Joonhyuk Kang

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

Comments Submitted for conference publication

2103.08349 2021-04-06 cs.LG physics.app-ph 57%

Data-driven method for real-time prediction and uncertainty quantification of fatigue failure under stochastic loading using artificial neural networks and Gaussian process regression

Maor Farid

专题命中幻觉与事实性：safety(abstract)；分类 cs.LG

2006.06848 2021-03-19 stat.ML cs.LG 57%

Getting a CLUE: A Method for Explaining Uncertainty Estimates

Javier Antorán, Umang Bhatt, Tameem Adel, Adrian Weller, José Miguel Hernández-Lobato

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

Comments Accepted as an oral presentation at ICLR 2021

2103.08951 2021-03-17 cs.LG stat.AP 57%

Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties

Lisa Schut, Oscar Key, Rory McGrath, Luca Costabello, Bogdan Sacaleanu, Medb Corcoran, Yarin Gal

专题命中幻觉与事实性：safety(abstract)；分类 cs.LG

Comments 21 pages, 13 Figures

Journal ref Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS) 2021

2006.05821 2021-03-16 cs.RO cs.AI eess.SP 57%

Development of A Stochastic Traffic Environment with Generative Time-Series Models for Improving Generalization Capabilities of Autonomous Driving Agents

Anil Ozturk, Mustafa Burak Gunel, Melih Dal, Ugur Yavas, Nazim Kemal Ure

专题命中幻觉与事实性：safety(abstract)；分类 cs.AI

Comments 7 pages, 4 figures, 7 tables, IV2020

2101.02974 2021-01-11 cs.LG stat.ML 57%

Approaching Neural Network Uncertainty Realism

Joachim Sicking, Alexander Kister, Matthias Fahrland, Stefan Eickeler, Fabian Hüger, Stefan Rüping, Peter Schlicht, Tim Wirtz

专题命中幻觉与事实性：safety(abstract)；分类 cs.LG

Comments Accepted at the NeurIPS 2019 Workshop on Machine Learning for Autonomous Driving (ML4AD)

2010.10969 2021-01-07 cs.LG stat.ML 57%

Incorporating Interpretable Output Constraints in Bayesian Neural Networks

Wanqian Yang, Lars Lorch, Moritz A. Graule, Himabindu Lakkaraju, Finale Doshi-Velez

专题命中幻觉与事实性：safety(abstract)；分类 cs.LG

Comments 11 pages, with six supplementary pages. 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada. Code available at: https://github.com/dtak/ocbnn-public. Updated version (final, official submission to NeurIPS in January 2021) includes post-conference revisions: improved results in Section 6.2, and corrected minor errata in Appendix C

1910.04819 2021-01-05 cs.LG stat.ML 57%

Information Aware Max-Norm Dirichlet Networks for Predictive Uncertainty Estimation

Theodoros Tsiligkaridis

专题命中幻觉与事实性：safety(abstract)；分类 cs.LG

Comments To appear in Neural Networks. https://doi.org/10.1016/j.neunet.2020.12.011

Journal ref Neural Networks, Volume 135, March 2021, Pages 105-114

2010.01440 2020-11-20 cs.LG cs.SD eess.AS q-bio.QM 57%

Uncertainty-Aware Multi-Modal Ensembling for Severity Prediction of Alzheimer's Dementia

Utkarsh Sarawgi, Wazeer Zulfikar, Rishab Khincha, Pattie Maes

专题命中幻觉与事实性：safety(abstract)；分类 cs.LG

Comments To appear at NeurIPS Machine Learning for Health (ML4H) 2020

2009.09535 2020-09-22 stat.ML cs.LG 57%

Stochastic Gradient Langevin Dynamics Algorithms with Adaptive Drifts

Sehwan Kim, Qifan Song, Faming Liang

专题命中幻觉与事实性：safety(abstract)；分类 cs.LG

Comments 27 pages

2008.02866 2020-08-18 cs.CV cs.LG eess.IV 57%

Improving Explainability of Image Classification in Scenarios with Class Overlap: Application to COVID-19 and Pneumonia

Edward Verenich, Alvaro Velasquez, Nazar Khan, Faraz Hussain

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

Comments 7 pages, 6 figures

2007.03212 2020-07-08 cs.LG stat.ML 57%

Soft Labeling Affects Out-of-Distribution Detection of Deep Neural Networks

Doyup Lee, Yeongjae Cheon

专题命中幻觉与事实性：safety(abstract)；分类 cs.LG

Comments ICML'20 Workshop on Uncertainty and Robustness in Deep Learning

2002.09831 2020-02-25 cs.LG stat.ML 57%

On the Role of Dataset Quality and Heterogeneity in Model Confidence

Yuan Zhao, Jiasi Chen, Samet Oymak

专题命中幻觉与事实性：safety(abstract)；分类 cs.LG

Comments 25 pages, 14 figures

1905.11659 2020-02-04 cs.LG stat.ML 57%

Evaluating and Calibrating Uncertainty Prediction in Regression Tasks

Dan Levi, Liran Gispan, Niv Giladi, Ethan Fetaya

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

On The Reliability Of Machine Learning Applications In Manufacturing Environments

Exploring the Limits of Epistemic Uncertainty Quantification in Low-Shot Settings

Justification-Based Reliability in Machine Learning

Collaborative Uncertainty in Multi-Agent Trajectory Forecasting

Shift-BNN: Highly-Efficient Probabilistic Bayesian Neural Network Training via Memory-Friendly Pattern Retrieving

Detecting and Mitigating Test-time Failure Risks via Model-agnostic Uncertainty Learning

Teaching Uncertainty Quantification in Machine Learning through Use Cases

Improving Variational Autoencoder based Out-of-Distribution Detection for Embedded Real-time Applications

Quantifying Uncertainty for Machine Learning Based Diagnostic

Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods

PRANK: motion Prediction based on RANKing

Uncertainty Quantification 360: A Holistic Toolkit for Quantifying and Communicating the Uncertainty of AI

Detecting Bot-Generated Text by Characterizing Linguistic Accommodation in Human-Bot Interactions

Quantifying sources of uncertainty in drug discovery predictions with probabilistic models

Empirical Models for Multidimensional Regression of Fission Systems

A Simple Framework to Quantify Different Types of Uncertainty in Deep Neural Networks for Image Classification

Bayesian Variational Federated Learning and Unlearning in Decentralized Networks

Data-driven method for real-time prediction and uncertainty quantification of fatigue failure under stochastic loading using artificial neural networks and Gaussian process regression

Getting a CLUE: A Method for Explaining Uncertainty Estimates

Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties

Development of A Stochastic Traffic Environment with Generative Time-Series Models for Improving Generalization Capabilities of Autonomous Driving Agents

Approaching Neural Network Uncertainty Realism

Incorporating Interpretable Output Constraints in Bayesian Neural Networks

Information Aware Max-Norm Dirichlet Networks for Predictive Uncertainty Estimation

Uncertainty-Aware Multi-Modal Ensembling for Severity Prediction of Alzheimer's Dementia

Stochastic Gradient Langevin Dynamics Algorithms with Adaptive Drifts

Improving Explainability of Image Classification in Scenarios with Class Overlap: Application to COVID-19 and Pneumonia

Soft Labeling Affects Out-of-Distribution Detection of Deep Neural Networks

On the Role of Dataset Quality and Heterogeneity in Model Confidence

Evaluating and Calibrating Uncertainty Prediction in Regression Tasks