Paper

  • Physics-Informed Bayesian Optimization of Variational Quantum Circuits

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Physics-Informed Bayesian Optimization of Variational Quantum Circuits

    Kim Nicoli, Christopher J. Anders, Lena Funcke, Tobias Hartung, Karl Jansen, Stefan Kühn, Klaus-Robert Müller, Paolo Stornati, Pan Kessel, Shinichi Nakajima p18341-18376 from Advances in Neural Information Processing Systems 36
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  • Physics-Informed Implicit Representations of Equilibrium Network Flows

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Physics-Informed Implicit Representations of Equilibrium Network Flows

    Kevin D. Smith, Francesco Seccamonte, Ananthram Swami, Francesco Bullo p7211-7221 from Advances in Neural Information Processing Systems 35
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  • Physics-informed Neural Networks for Functional Differential Equations: Cylindrical Approximation and Its Convergence Guarantees

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Physics-informed Neural Networks for Functional Differential Equations: Cylindrical Approximation and Its Convergence Guarantees

    Taiki Miyagawa, Takeru Yokota p72274-72409 from Advances in Neural Information Processing Systems 37
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  • Physics-Informed Regularization for Domain-Agnostic Dynamical System Modeling

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Physics-Informed Regularization for Domain-Agnostic Dynamical System Modeling

    Zijie Huang, Wanjia Zhao, Jingdong Gao, Ziniu Hu, Xiao Luo, Yadi Cao, Yuanzhou Chen, Yizhou Sun, Wei Wang p739-774 from Advances in Neural Information Processing Systems 37
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  • Physics-Informed Variational State-Space Gaussian Processes

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Physics-Informed Variational State-Space Gaussian Processes

    Oliver Hamelijnck, Arno Solin, Theodoros Damoulas p98505-98536 from Advances in Neural Information Processing Systems 37
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  • Physics-Regularized Multi-Modal Image Assimilation for Brain Tumor Localization

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Physics-Regularized Multi-Modal Image Assimilation for Brain Tumor Localization

    Michal Balcerak, Tamaz Amiranashvili, Andreas Wagner, Jonas Weidner, Petr Karnakov, Johannes Paetzold, Ivan Ezhov, Petros Koumoutsakos, Benedikt Wiestler, Bjoern Menze p41909-41933 from Advances in Neural Information Processing Systems 37
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  • Physion++: Evaluating Physical Scene Understanding that Requires Online Inference of Different Physical Properties

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Physion++: Evaluating Physical Scene Understanding that Requires Online Inference of Different Physical Properties

    Hsiao-Yu Tung, Mingyu Ding, Zhenfang Chen, Daniel Bear, Chuang Gan, Josh Tenenbaum, Dan Yamins, Judith Fan, Kevin Smith p67048-67068 from Advances in Neural Information Processing Systems 36
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  • Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generation

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generation

    Yuval Kirstain, Adam Polyak, Uriel Singer, Shahbuland Matiana, Joe Penna, Omer Levy p36652-36663 from Advances in Neural Information Processing Systems 36
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  • Picking on the Same Person: Does Algorithmic Monoculture Lead to Outcome Homogenization?

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Picking on the Same Person: Does Algorithmic Monoculture Lead to Outcome Homogenization?

    Rishi Bommasani, Kathleen A. Creel, Ananya Kumar, Dan Jurafsky, Percy Liang p3663-3678 from Advances in Neural Information Processing Systems 35
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  • PICProp: Physics-Informed Confidence Propagation for Uncertainty Quantification

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    PICProp: Physics-Informed Confidence Propagation for Uncertainty Quantification

    Qianli Shen, Wai Hoh Tang, Zhun Deng, Apostolos Psaros, Kenji Kawaguchi p33041-33064 from Advances in Neural Information Processing Systems 36
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  • PID-Inspired Inductive Biases for Deep Reinforcement Learning in Partially Observable Control Tasks

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    PID-Inspired Inductive Biases for Deep Reinforcement Learning in Partially Observable Control Tasks

    Ian Char, Jeff Schneider p59425-59463 from Advances in Neural Information Processing Systems 36
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  • Piecewise deterministic generative models

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Piecewise deterministic generative models

    Andrea Bertazzi, Dario Shariatian, Umut Simsekli, Eric Moulines, Alain Durmus p93336-93373 from Advances in Neural Information Processing Systems 37
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