Paper

  • Parametric model reduction of mean-field and stochastic systems via higher-order action matching

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Parametric model reduction of mean-field and stochastic systems via higher-order action matching

    Jules Berman, Tobias Blickhan, Benjamin Peherstorfer p56588-56618 from Advances in Neural Information Processing Systems 37
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  • PARAMETRIC SENSITIVITY OF A PARTIALLY EVAPORATING ORGANIC RANKINE CYCLE WITH THERMAL NON-EQUILIBRIUM EXPANSION

    ECOS 2024

    PARAMETRIC SENSITIVITY OF A PARTIALLY EVAPORATING ORGANIC RANKINE CYCLE WITH THERMAL NON-EQUILIBRIUM EXPANSION

    Xander van Heule, Michel De Paepe, Steven Lecompte p2262-2273 from 37th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems (ECOS 2024)
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  • PARAMETRIC STUDY ON THE BENDING PERFORMANCE OF WOODEN DOWELS

    World Conference on Timber Engineering 2025

    PARAMETRIC STUDY ON THE BENDING PERFORMANCE OF WOODEN DOWELS

    Inayat Ullah Khan, Mahbube Subhani, Kazem Ghabraie, Mahmud Ashraf p2874-2880 from 14th World Conference on Timber Engineering 2025 (WCTE 2025)
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  • Parametrically Retargetable Decision-Makers Tend to Seek Power

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Parametrically Retargetable Decision-Makers Tend to Seek Power

    Alex Turner, Prasad Tadepalli p31391-31401 from Advances in Neural Information Processing Systems 35
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  • Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense

    Kalpesh Krishna, Yixiao Song, Marzena Karpinska, John Wieting, Mohit Iyyer p27469-27500 from Advances in Neural Information Processing Systems 36
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  • Paraphrasing is All You Need for Novel Object Captioning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Paraphrasing is All You Need for Novel Object Captioning

    Cheng-Fu Yang, Yao-Hung Hubert Tsai, Wan-Cyuan Fan, Russ Salakhutdinov, Louis-Philippe Morency, Frank Wang p6492-6504 from Advances in Neural Information Processing Systems 35
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  • Pard: Permutation-Invariant Autoregressive Diffusion for Graph Generation

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Pard: Permutation-Invariant Autoregressive Diffusion for Graph Generation

    Lingxiao Zhao, Xueying Ding, Leman Akoglu p7156-7184 from Advances in Neural Information Processing Systems 37
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  • Pareto Frontiers in Deep Feature Learning: Data, Compute, Width, and Luck

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Pareto Frontiers in Deep Feature Learning: Data, Compute, Width, and Luck

    Benjamin Edelman, Surbhi Goel, Sham Kakade, Eran Malach, Cyril Zhang p48021-48034 from Advances in Neural Information Processing Systems 36
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  • Pareto Set Learning for Expensive Multi-Objective Optimization

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Pareto Set Learning for Expensive Multi-Objective Optimization

    Xi Lin, Zhiyuan Yang, Xiaoyuan Zhang, Qingfu Zhang p19231-19247 from Advances in Neural Information Processing Systems 35
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  • Parsel??: Algorithmic Reasoning with Language Models by Composing Decompositions

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Parsel??: Algorithmic Reasoning with Language Models by Composing Decompositions

    Eric Zelikman, Qian Huang, Gabriel Poesia, Noah Goodman, Nick Haber p31466-31523 from Advances in Neural Information Processing Systems 36
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  • Parseval Regularization for Continual Reinforcement Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Parseval Regularization for Continual Reinforcement Learning

    Wesley Chung, Lynn Cherif, David Meger, Doina Precup p127937-127967 from Advances in Neural Information Processing Systems 37
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  • Parsimony or Capability? Decomposition Delivers Both in Long-term Time Series Forecasting

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Parsimony or Capability? Decomposition Delivers Both in Long-term Time Series Forecasting

    Jinliang Deng, Feiyang Ye, Du Yin, Xuan Song, Ivor Tsang, Hui Xiong p66687-66712 from Advances in Neural Information Processing Systems 37
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