Paper

  • Physics-Informed Bayesian Optimization of Variational Quantum Circuits

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Physics-Informed Bayesian Optimization of Variational Quantum Circuits

    Christopher J. Anders, Lena Funcke, Tobias Hartung, Karl Jansen, Pan Kessel, Stefan Kühn, Klaus-Robert Müller, Shinichi Nakajima, Kim Nicoli, Paolo Stornati 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

    Francesco Bullo, Francesco Seccamonte, Kevin D. Smith, Ananthram Swami 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

    Yadi Cao, Yuanzhou Chen, Jingdong Gao, Ziniu Hu, Zijie Huang, Xiao Luo, Yizhou Sun, Wei Wang, Wanjia Zhao 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

    Theodoros Damoulas, Oliver Hamelijnck, Arno Solin 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

    Tamaz Amiranashvili, Michal Balcerak, Ivan Ezhov, Petr Karnakov, Petros Koumoutsakos, Bjoern Menze, Johannes Paetzold, Andreas Wagner, Jonas Weidner, Benedikt Wiestler 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

    Daniel Bear, Zhenfang Chen, Mingyu Ding, Judith Fan, Chuang Gan, Kevin Smith, Josh Tenenbaum, Hsiao-Yu Tung, Dan Yamins 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, Omer Levy, Shahbuland Matiana, Joe Penna, Adam Polyak, Uriel Singer 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, Dan Jurafsky, Ananya Kumar, 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

    Zhun Deng, Kenji Kawaguchi, Apostolos Psaros, Qianli Shen, Wai Hoh Tang 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, Alain Durmus, Eric Moulines, Dario Shariatian, Umut Simsekli p93336-93373 from Advances in Neural Information Processing Systems 37
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