Paper

  • Maximization of Average Precision for Deep Learning with Adversarial Ranking Robustness

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Maximization of Average Precision for Deep Learning with Adversarial Ranking Robustness

    Gang Li, Wei Tong, Tianbao Yang p15475-15496 from Advances in Neural Information Processing Systems 36
    Our Price: $0.00
  • Maximize to Explore: One Objective Function Fusing Estimation, Planning, and Exploration

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Maximize to Explore: One Objective Function Fusing Estimation, Planning, and Exploration

    Hao Hu, Zhihan Liu, Miao Lu, Zhaoran Wang, Wei Xiong, Zhuoran Yang, Shenao Zhang, Sirui Zheng, Han Zhong p22151-22165 from Advances in Neural Information Processing Systems 36
    Our Price: $0.00
  • Maximizing and Satisficing in Multi-Armed Bandits with Graph Information

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Maximizing and Satisficing in Multi-Armed Bandits with Graph Information

    Gautam Dasarathy, Mohit Malu, Nikhil Rao, Parth Thaker p2019-2032 from Advances in Neural Information Processing Systems 35
    Our Price: $0.00
  • Maximizing DPV Hosting Capacity with Regional Firm VRE Power

    American Solar Energy Society (ASES)

    Maximizing DPV Hosting Capacity with Regional Firm VRE Power

    Marc Perez p319-328 from 53rd American Solar Energy Society National Solar Conference 2024 (SOLAR 2024)
    Our Price: $0.00
  • Maximizing Revenue Under Market Shrinkage and Market Uncertainty

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Maximizing Revenue Under Market Shrinkage and Market Uncertainty

    Maria-Florina Balcan, Siddharth Prasad, Tuomas Sandholm p1643-1655 from Advances in Neural Information Processing Systems 35
    Our Price: $0.00
  • Maximizing utility in multi-agent environments by anticipating the behavior of other learners

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Maximizing utility in multi-agent environments by anticipating the behavior of other learners

    Angelos Assos, Yuval Dagan, Constantinos Daskalakis p38769-38798 from Advances in Neural Information Processing Systems 37
    Our Price: $0.00
  • Maximum a Posteriori Natural Scene Reconstruction from Retinal Ganglion Cells with Deep Denoiser Priors

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Maximum a Posteriori Natural Scene Reconstruction from Retinal Ganglion Cells with Deep Denoiser Priors

    Nora Brackbill, E. J. Chichilnisky, Alan Litke, Alexander Sher, Eero Simoncelli, Eric Wu p27212-27224 from Advances in Neural Information Processing Systems 35
    Our Price: $0.00
  • Maximum Average Randomly Sampled: A Scale Free and Non-parametric Algorithm for Stochastic Bandits

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Maximum Average Randomly Sampled: A Scale Free and Non-parametric Algorithm for Stochastic Bandits

    Masoud Moravej Khorasani, Erik Weyer p58865-58874 from Advances in Neural Information Processing Systems 36
    Our Price: $0.00
  • Maximum Class Separation as Inductive Bias in One Matrix

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Maximum Class Separation as Inductive Bias in One Matrix

    Gertjan Burghouts, Rita Cucchiara, Tejaswi Kasarla, Pascal Mettes, Elise Van Der Pol, Max Van Spengler p19553-19566 from Advances in Neural Information Processing Systems 35
    Our Price: $0.00
  • Maximum Common Subgraph Guided Graph Retrieval: Late and Early Interaction Networks

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Maximum Common Subgraph Guided Graph Retrieval: Late and Early Interaction Networks

    Soumen Chakrabarti, Abir De, Indradyumna Roy p32112-32126 from Advances in Neural Information Processing Systems 35
    Our Price: $0.00
  • Maximum Entropy Inverse Reinforcement Learning of Diffusion Models with Energy-Based Models

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Maximum Entropy Inverse Reinforcement Learning of Diffusion Models with Energy-Based Models

    Himchan Hwang, Dohyun Kwon, Yung-Kyun Noh, Frank Park, Sangwoong Yoon p24601-24624 from Advances in Neural Information Processing Systems 37
    Our Price: $0.00
  • Maximum Entropy Reinforcement Learning via Energy-Based Normalizing Flow

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Maximum Entropy Reinforcement Learning via Energy-Based Normalizing Flow

    Chen-Hao Chao, Chien Feng, Cheng-Kuang Lee, Chun-Yi Lee, Simon See, Wei-Fang Sun p56136-56165 from Advances in Neural Information Processing Systems 37
    Our Price: $0.00