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

    Michael Saxon, Mark Steyvers, William Yang Wang, Xinyi Wang, Wanrong Zhu 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

    Lu Chen, Hongshen Xu, Kai Yu, Danyang Zhang, Situo Zhang, Zihan Zhao 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, Trevor Darrell, Kurt Keutzer, Bo Li, Chunyuan Li, Ziwei Liu, Sheng Shen, Jingkang Yang 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

    Shixiang Shane Gu, Yusuke Iwasawa, Takeshi Kojima, Yutaka Matsuo, Machel Reid 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

    Marc Finzi, Nate Gruver, 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

    David Hsu, Wee Sun Lee, Zirui Zhao 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

    Renhe Jiang, Noboru Koshizuka, Makoto Onizuka, Ryosuke Shibasaki, Jiawei Wang, Zengqing Wu, Chuan Xiao, Chuang Yang 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, Richard L Lewis, Satinder Singh, Logan Walls 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, Frank Hutter, Samuel Müller 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.

    Evelina Fedorenko, Eghbal Hosseini 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

    Umang Bhatt, Katherine Collins, Samuel Dooley, Micah Goldblum, Nate Gruver, Sanyam Kapoor, Arka Pal, Manley Roberts, Adrian Weller, 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, George Karypis, Leonard Lausen, Renato Negrinho, Samson Tan, Sheng Zha, Jinman Zhao 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

    Runji Lin, Weiyu Ma, Qirui Mi, Jun Wang, Yuqiao Wu, Xue Yan, Yongcheng Zeng, Haifeng Zhang 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

    Samuel Norman-Haignere, David Skrill 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)

    Majid Benam, James Bennett, Enrico Cioni, R. del Rio-Chanona, Pieter Francois, Jakob Hauser, Daniel Hoyer, Daniel Kondor, Jenny Reddish, Peter Turchin, Federica Villa 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

    Yongli Hu, Alan Liew, Linhao Luo, Shirui Pan, Kai Sun, Jiapu Wang, Wei Wei, 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

    Wenyan Cong, Zhiwen Fan, Boris Ivanovic, Achuta Kadambi, Renjie Li, Marco Pavone, Peihao Wang, Yue Wang, Zhangyang Wang, Kairun Wen, Danfei Xu, Jian Zhang, Shijie Zhou 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

    Peter Bartlett, Yuhang Cai, Michael Lindsey, Song Mei, Jingfeng Wu 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

    Liang Chen, Jianming Liang, Yu Liu, Ping Luo, Guanglu Song, Zeyue Xue, Zhuofan Zong 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

    Jan-Christian Huetter, Romain Lopez, 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

    Marco Bornstein, Furong Huang, Tahseen Rabbani 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

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