Paper

  • Predicting Global Label Relationship Matrix for Graph Neural Networks under Heterophily

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Predicting Global Label Relationship Matrix for Graph Neural Networks under Heterophily

    Xiangjing Hu, Irwin King, Langzhang Liang, Zixing Song, Zenglin Xu p10909-10921 from Advances in Neural Information Processing Systems 36
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  • Predicting Ground State Properties: Constant Sample Complexity and Deep Learning Algorithms

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Predicting Ground State Properties: Constant Sample Complexity and Deep Learning Algorithms

    Chiranjib Bhattacharyya, Devdatt Dubhashi, Alexandru Gheorghiu, Laura Lewis, Marc Wanner p33962-34024 from Advances in Neural Information Processing Systems 37
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  • Predicting Label Distribution from Multi-Label Ranking

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Predicting Label Distribution from Multi-Label Ranking

    Xiuyi Jia, Yunan Lu p36931-36943 from Advances in Neural Information Processing Systems 35
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  • Predicting Label Distribution from Ternary Labels

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Predicting Label Distribution from Ternary Labels

    Xiuyi Jia, Yunan Lu p70431-70452 from Advances in Neural Information Processing Systems 37
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  • PREDICTING MODULUS OF RUPTURE OF HEAT-TREATED WOODS BY ARTIFICIAL NEURAL NETWORK COMBINED WITH GENETIC ALGORITHM

    World Conference on Timber Engineering 2023 (WCTE 2023)

    PREDICTING MODULUS OF RUPTURE OF HEAT-TREATED WOODS BY ARTIFICIAL NEURAL NETWORK COMBINED WITH GENETIC ALGORITHM

    Mehdi Nikoo, Reza Abbasi Malekabadi, Ghazanfarah Hafeez p84-88 from World Conference on Timber Engineering (WCTE 2023)
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  • Predicting mutational effects on protein-protein binding via a side-chain diffusion probabilistic model

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Predicting mutational effects on protein-protein binding via a side-chain diffusion probabilistic model

    Dongbo Bu, Shiwei Liu, Milong Ren, Chungong Yu, Haicang Zhang, Tian Zhu p48994-49005 from Advances in Neural Information Processing Systems 36
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  • Predicting Student Performance in Engineering Courses: A Risk Model Analysis

    Research In Engineering Education Network

    Predicting Student Performance in Engineering Courses: A Risk Model Analysis

    Veronica Abuchar, Jose De La Hoz, Camilo Vieira, Carlos Arteta p969-977 from 9th Research in Engineering Education Symposium and 32nd Australasian Association for Engineering Education Conference (REES AAEE 2021)
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  • PREDICTING THE EFFECTIVE CHAR DEPTH IN TIMBER ELEMENTS EXPOSED TO NATURAL FIRES, INCLUDING THE COOLING PHASE

    World Conference on Timber Engineering 2023 (WCTE 2023)

    PREDICTING THE EFFECTIVE CHAR DEPTH IN TIMBER ELEMENTS EXPOSED TO NATURAL FIRES, INCLUDING THE COOLING PHASE

    Andrea Lucherini, Daniela Šejnová Pitelková, Vladimír Mózer p1681-1690 from World Conference on Timber Engineering (WCTE 2023)
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  • Predicting the Performance of Foundation Models via Agreement-on-the-Line

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Predicting the Performance of Foundation Models via Agreement-on-the-Line

    Christina Baek, Taeyoun Kim, Zico Kolter, Aman Mehra, Aditi Raghunathan, Rahul Saxena p31854-31906 from Advances in Neural Information Processing Systems 37
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  • Predicting Weather-Dependent Energy Savings for Low-Income Residential Buildings for Specific Upgrades with Limited Data

    American Solar Energy Society (ASES)

    Predicting Weather-Dependent Energy Savings for Low-Income Residential Buildings for Specific Upgrades with Limited Data

    Phillip Clayton p112-117 from 53rd American Solar Energy Society National Solar Conference 2024 (SOLAR 2024)
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  • Predicting Whole Helmet Blunt Impact Performance from Isolated Pad Testing Using Machine Learning

    Royal Military Academy (Belgium)

    Predicting Whole Helmet Blunt Impact Performance from Isolated Pad Testing Using Machine Learning

    J. Zhang, D. Midgett, M. Yates, C. Bradfield, J. Dunn, J. Leon, E. Cawi, C. Pyles, Q. Luong, L. Ruiz, C. Trageser, J. Hopping p257-266 from 17th Personal Armour Systems Symposium (PASS 2025)
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  • Prediction and Control in Continual Reinforcement Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Prediction and Control in Continual Reinforcement Learning

    Nishanth Anand, Doina Precup p63779-63817 from Advances in Neural Information Processing Systems 36
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