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    World Conference on Timber Engineering 2025

    OVERVIEW OF THE BEHAVIOUR OF MASS-TIMBER MEMBERS SUBJECTED TO CONTACT CHARGE DETONATIONS AND NEAR-FIELD BLAST LOADS

    Trevor Czorny, Mehdi Saloo, Christian Viau, Alexander Helal p501-508 from 14th World Conference on Timber Engineering 2025 (WCTE 2025)
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  • OVERVIEW OF THE WORK WITHIN CEN TC 250/ SC5/ WG12 ASSESMENT AND RETROFITTING OF TIMBER EXISTING STRUCTURE

    World Conference on Timber Engineering 2025

    OVERVIEW OF THE WORK WITHIN CEN TC 250/ SC5/ WG12 ASSESMENT AND RETROFITTING OF TIMBER EXISTING STRUCTURE

    Vlatka Rajcic, Jorge M. Branco, Robert Jockwer, Eleftheria Tsakanika p28-35 from 14th World Conference on Timber Engineering 2025 (WCTE 2025)
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  • OVT-B: A New Large-Scale Benchmark for Open-Vocabulary Multi-Object Tracking

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    OVT-B: A New Large-Scale Benchmark for Open-Vocabulary Multi-Object Tracking

    Haiji Liang, Ruize Han p14849-14863 from Advances in Neural Information Processing Systems 37
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  • OW-VISCapTor: Abstractors for Open-World Video Instance Segmentation and Captioning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    OW-VISCapTor: Abstractors for Open-World Video Instance Segmentation and Captioning

    Anwesa Choudhuri, Girish Chowdhary, Alexander Schwing p42071-42096 from Advances in Neural Information Processing Systems 37
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  • OwMatch: Conditional Self-Labeling with Consistency for Open-World Semi-Supervised Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    OwMatch: Conditional Self-Labeling with Consistency for Open-World Semi-Supervised Learning

    Shengjie Niu, Lifan Lin, Jian Huang, Chao Wang p99836-99866 from Advances in Neural Information Processing Systems 37
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  • OxonFair: A Flexible Toolkit for Algorithmic Fairness

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    OxonFair: A Flexible Toolkit for Algorithmic Fairness

    Eoin Delaney, Zihao Fu, Sandra Wachter, Brent Mittelstadt, Chris Russell p94209-94245 from Advances in Neural Information Processing Systems 37
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  • P$^2$C$^2$Net: PDE-Preserved Coarse Correction Network for efficient prediction of spatiotemporal dynamics

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    P$^2$C$^2$Net: PDE-Preserved Coarse Correction Network for efficient prediction of spatiotemporal dynamics

    Qi Wang, Pu Ren, Hao Zhou, Xin-Yang Liu, Zhiwen Deng, Yi Zhang, Ruizhi Chengze, Hongsheng Liu, Zidong Wang, Jian-Xun Wang, Ji-Rong Wen, Hao Sun, Yang Liu p68897-68925 from Advances in Neural Information Processing Systems 37
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  • P-Flow: A Fast and Data-Efficient Zero-Shot TTS through Speech Prompting

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    P-Flow: A Fast and Data-Efficient Zero-Shot TTS through Speech Prompting

    Sungwon Kim, Kevin Shih, Rohan Badlani, Joao Felipe Santos, Evelina Bakhturina, Mikyas Desta, Rafael Valle, Sungroh Yoon, Bryan Catanzaro p74213-74228 from Advances in Neural Information Processing Systems 36
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  • P2P: Tuning Pre-Trained Image Models for Point Cloud Analysis with Point-To-Pixel Prompting

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    P2P: Tuning Pre-Trained Image Models for Point Cloud Analysis with Point-To-Pixel Prompting

    Ziyi Wang, Xumin Yu, Yongming Rao, Jie Zhou, Jiwen Lu p14388-14402 from Advances in Neural Information Processing Systems 35
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  • PAC Learning Linear Thresholds from Label Proportions

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    PAC Learning Linear Thresholds from Label Proportions

    Anand Brahmbhatt, Rishi Saket, Aravindan Raghuveer p66610-66646 from Advances in Neural Information Processing Systems 36
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  • PAC Prediction Sets for Meta-Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    PAC Prediction Sets for Meta-Learning

    Sangdon Park, Edgar Dobriban, Insup Lee, Osbert Bastani p37920-37931 from Advances in Neural Information Processing Systems 35
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  • PAC-Bayes Compression Bounds So Tight that They Can Explain Generalization

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    PAC-Bayes Compression Bounds So Tight that They Can Explain Generalization

    Sanae Lotfi, Marc Finzi, Sanyam Kapoor, Andres Potapczynski, Micah Goldblum, Andrew Wilson p31459-31473 from Advances in Neural Information Processing Systems 35
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