Paper

  • Leveraging Contrastive Learning for Enhanced Node Representations in Tokenized Graph Transformers

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Leveraging Contrastive Learning for Enhanced Node Representations in Tokenized Graph Transformers

    Jinsong Chen, Hanpeng Liu, John Hopcroft, Kun He p85824-85845 from Advances in Neural Information Processing Systems 37
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  • Leveraging Drift to Improve Sample Complexity of Variance Exploding Diffusion Models

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Leveraging Drift to Improve Sample Complexity of Variance Exploding Diffusion Models

    Ruofeng Yang, Zhijie Wang, Bo Jiang, Shuai Li p107662-107702 from Advances in Neural Information Processing Systems 37
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  • Leveraging Early-Stage Robustness in Diffusion Models for Efficient and High-Quality Image Synthesis

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Leveraging Early-Stage Robustness in Diffusion Models for Efficient and High-Quality Image Synthesis

    Yulhwa Kim, Dongwon Jo, Hyesung Jeon, Taesu Kim, Daehyun Ahn, Hyungjun Kim, Jae-Joon Kim p1229-1244 from Advances in Neural Information Processing Systems 36
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  • Leveraging Environment Interaction for Automated PDDL Translation and Planning with Large Language Models

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Leveraging Environment Interaction for Automated PDDL Translation and Planning with Large Language Models

    Sadegh Mahdavi, Raquel Aoki, Keyi Tang, Yanshuai Cao p38960-39008 from Advances in Neural Information Processing Systems 37
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  • Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare

    Shengpu Tang, Maggie Makar, Michael Sjoding, Finale Doshi-Velez, Jenna Wiens p34272-34286 from Advances in Neural Information Processing Systems 35
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  • Leveraging Hallucinations to Reduce Manual Prompt Dependency in Promptable Segmentation

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Leveraging Hallucinations to Reduce Manual Prompt Dependency in Promptable Segmentation

    Jian Hu, Jiayi Lin, Junchi Yan, Shaogang Gong p107171-107197 from Advances in Neural Information Processing Systems 37
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  • Leveraging Inter-Layer Dependency for Post -Training Quantization

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Leveraging Inter-Layer Dependency for Post -Training Quantization

    Changbao Wang, Dandan Zheng, Yuanliu Liu, Liang Li p6666-6679 from Advances in Neural Information Processing Systems 35
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  • Leveraging Locality and Robustness to Achieve Massively Scalable Gaussian Process Regression

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Leveraging Locality and Robustness to Achieve Massively Scalable Gaussian Process Regression

    Robert Allison, Anthony Stephenson, Samuel F, Edward O Pyzer-Knapp p18906-18931 from Advances in Neural Information Processing Systems 36
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  • LEVERAGING NEURAL NETWORKS IN A HYBRID MODEL PREDICTIVE CONTROL FRAMEWORK FOR DISTRICT HEATING NETWORKS

    ECOS 2024

    LEVERAGING NEURAL NETWORKS IN A HYBRID MODEL PREDICTIVE CONTROL FRAMEWORK FOR DISTRICT HEATING NETWORKS

    Dubon Rodrigue, Mohamed Tahar Mabrouk, Bastien Pasdeloup, Patrick Meyer, Bruno Lacarrière p287-298 from 37th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems (ECOS 2024)
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  • Leveraging partial stragglers within gradient coding

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Leveraging partial stragglers within gradient coding

    Aditya Ramamoorthy, Ruoyu Meng, Vrinda Girimaji p60382-60402 from Advances in Neural Information Processing Systems 37
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  • Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning

    Lin Guan, Karthik Valmeekam, Sarath Sreedharan, Subbarao Kambhampati p79081-79094 from Advances in Neural Information Processing Systems 36
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  • Leveraging Separated World Model for Exploration in Visually Distracted Environments

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Leveraging Separated World Model for Exploration in Visually Distracted Environments

    Kaichen Huang, Shenghua Wan, Minghao Shao, Hai-Hang Sun, Le Gan, Shuai Feng, De-Chuan Zhan p82350-82374 from Advances in Neural Information Processing Systems 37
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