Neural Information Processing Systems Foundation, Inc. (NeurIPS)

  • Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning

    Xinyi Wang, Wanrong Zhu, Michael Saxon, Mark Steyvers, William Yang Wang p15614-15638 from Advances in Neural Information Processing Systems 36
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  • Large Language Models Are Semi-Parametric Reinforcement Learning Agents

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Large Language Models Are Semi-Parametric Reinforcement Learning Agents

    Danyang Zhang, Lu Chen, Situo Zhang, Hongshen Xu, Zihan Zhao, Kai Yu p78227-78239 from Advances in Neural Information Processing Systems 36
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  • Large Language Models are Visual Reasoning Coordinators

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Large Language Models are Visual Reasoning Coordinators

    Liangyu Chen, Bo Li, Sheng Shen, Jingkang Yang, Chunyuan Li, Kurt Keutzer, Trevor Darrell, Ziwei Liu p70115-70140 from Advances in Neural Information Processing Systems 36
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  • Large Language Models Are Zero-Shot Reasoners

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Large Language Models Are Zero-Shot Reasoners

    Takeshi Kojima, Shixiang (Shane) Gu, Machel Reid, Yutaka Matsuo, Yusuke Iwasawa p22199-22213 from Advances in Neural Information Processing Systems 35
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  • Large Language Models Are Zero-Shot Time Series Forecasters

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Large Language Models Are Zero-Shot Time Series Forecasters

    Nate Gruver, Marc Finzi, Shikai Qiu, Andrew Wilson p19622-19635 from Advances in Neural Information Processing Systems 36
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  • Large Language Models as Commonsense Knowledge for Large-Scale Task Planning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Large Language Models as Commonsense Knowledge for Large-Scale Task Planning

    Zirui Zhao, Wee Sun Lee, David Hsu p31967-31987 from Advances in Neural Information Processing Systems 36
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  • Large Language Models as Urban Residents: An LLM Agent Framework for Personal Mobility Generation

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Large Language Models as Urban Residents: An LLM Agent Framework for Personal Mobility Generation

    Jiawei Wang, Renhe Jiang, Chuang Yang, Zengqing Wu, Makoto Onizuka, Ryosuke Shibasaki, Noboru Koshizuka, Chuan Xiao p124547-124574 from Advances in Neural Information Processing Systems 37
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  • Large Language Models can Implement Policy Iteration

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Large Language Models can Implement Policy Iteration

    Ethan Brooks, Logan Walls, Richard L Lewis, Satinder Singh p30349-30366 from Advances in Neural Information Processing Systems 36
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  • Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering

    Noah Hollmann, Samuel Müller, Frank Hutter p44753-44775 from Advances in Neural Information Processing Systems 36
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  • Large language models implicitly learn to straighten neural sentence trajectories to construct a predictive representation of natural language.

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Large language models implicitly learn to straighten neural sentence trajectories to construct a predictive representation of natural language.

    Eghbal Hosseini, Evelina Fedorenko p43918-43930 from Advances in Neural Information Processing Systems 36
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  • Large Language Models Must Be Taught to Know What They Don’t Know

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Large Language Models Must Be Taught to Know What They Don’t Know

    Sanyam Kapoor, Nate Gruver, Manley Roberts, Katherine Collins, Arka Pal, Umang Bhatt, Adrian Weller, Samuel Dooley, Micah Goldblum, Andrew Wilson p85932-85972 from Advances in Neural Information Processing Systems 37
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  • Large Language Models of Code Fail at Completing Code with Potential Bugs

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Large Language Models of Code Fail at Completing Code with Potential Bugs

    Tuan Dinh, Jinman Zhao, Samson Tan, Renato Negrinho, Leonard Lausen, Sheng Zha, George Karypis p41386-41412 from Advances in Neural Information Processing Systems 36
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  • Large Language Models Play StarCraft II:Benchmarks and A Chain of Summarization Approach

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Large Language Models Play StarCraft II:Benchmarks and A Chain of Summarization Approach

    Weiyu Ma, Qirui Mi, Yongcheng Zeng, Xue Yan, Yuqiao Wu, Runji Lin, Haifeng Zhang, Jun Wang p133386-133442 from Advances in Neural Information Processing Systems 37
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  • Large language models transition from integrating across position-yoked, exponential windows to structure-yoked, power-law windows

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Large language models transition from integrating across position-yoked, exponential windows to structure-yoked, power-law windows

    David Skrill, Samuel Norman-Haignere p638-654 from Advances in Neural Information Processing Systems 36
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  • Large Language Models' Expert-level Global History Knowledge Benchmark (HiST-LLM)

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Large Language Models' Expert-level Global History Knowledge Benchmark (HiST-LLM)

    Jakob Hauser, Daniel Kondor, Jenny Reddish, Majid Benam, Enrico Cioni, Federica Villa, James Bennett, Daniel Hoyer, Pieter Francois, Peter Turchin, R. Del Rio-Chanona p32336-32369 from Advances in Neural Information Processing Systems 37
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  • Large Language Models-guided Dynamic Adaptation for Temporal Knowledge Graph Reasoning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Large Language Models-guided Dynamic Adaptation for Temporal Knowledge Graph Reasoning

    Jiapu Wang, Kai Sun, Linhao Luo, Wei Wei, Yongli Hu, Alan Liew, Shirui Pan, Baocai Yin p8384-8410 from Advances in Neural Information Processing Systems 37
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  • Large Pre-trained time series models for cross-domain Time series analysis tasks

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Large Pre-trained time series models for cross-domain Time series analysis tasks

    Harshavardhan Kamarthi, B. Prakash p56190-56214 from Advances in Neural Information Processing Systems 37
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  • Large Scale Transfer Learning for Tabular Data via Language Modeling

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Large Scale Transfer Learning for Tabular Data via Language Modeling

    Josh Gardner, Juan Perdomo, Ludwig Schmidt p45155-45205 from Advances in Neural Information Processing Systems 37
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  • Large Spatial Model: End-to-end Unposed Images to Semantic 3D

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Large Spatial Model: End-to-end Unposed Images to Semantic 3D

    Zhiwen Fan, Jian Zhang, Wenyan Cong, Peihao Wang, Renjie Li, Kairun Wen, Shijie Zhou, Achuta Kadambi, Zhangyang Wang, Danfei Xu, Boris Ivanovic, Marco Pavone, Yue Wang p40212-40229 from Advances in Neural Information Processing Systems 37
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  • Large Stepsize Gradient Descent for Non-Homogeneous Two-Layer Networks: Margin Improvement and Fast Optimization

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Large Stepsize Gradient Descent for Non-Homogeneous Two-Layer Networks: Margin Improvement and Fast Optimization

    Yuhang Cai, Jingfeng Wu, Song Mei, Michael Lindsey, Peter Bartlett p71306-71351 from Advances in Neural Information Processing Systems 37
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  • Large-Batch Optimization for Dense Visual Predictions: Training Faster R-CNN in 4.2 Minutes

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Large-Batch Optimization for Dense Visual Predictions: Training Faster R-CNN in 4.2 Minutes

    Zeyue Xue, Jianming Liang, Guanglu Song, Zhuofan Zong, Liang Chen, Yu Liu, Ping Luo p18694-18706 from Advances in Neural Information Processing Systems 35
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  • Large-Scale Differentiable Causal Discovery of Factor Graphs

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Large-Scale Differentiable Causal Discovery of Factor Graphs

    Romain Lopez, Jan-Christian Huetter, Jonathan Pritchard, Aviv Regev p19290-19303 from Advances in Neural Information Processing Systems 35
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  • Large-Scale Distributed Learning via Private On-Device LSH

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Large-Scale Distributed Learning via Private On-Device LSH

    Tahseen Rabbani, Marco Bornstein, Furong Huang p16153-16171 from Advances in Neural Information Processing Systems 36
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  • Large-Scale Optimization of Partial AUC in a Range of False Positive Rates

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Large-Scale Optimization of Partial AUC in a Range of False Positive Rates

    Yao Yao, Qihang Lin, Tianbao Yang p31239-31253 from Advances in Neural Information Processing Systems 35
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