Neural Information Processing Systems Foundation, Inc. (NeurIPS)

  • Indoor Air Quality Dataset with Activities of Daily Living in Low to Middle-income Communities

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Indoor Air Quality Dataset with Activities of Daily Living in Low to Middle-income Communities

    Sandip Chakraborty, Prasenjit Karmakar, Swadhin Pradhan p70076-70100 from Advances in Neural Information Processing Systems 37
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  • Induced Model Matching: Restricted Models Help Train Full-Featured Models

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Induced Model Matching: Restricted Models Help Train Full-Featured Models

    Usama Muneeb, Mesrob Ohannessian p62617-62647 from Advances in Neural Information Processing Systems 37
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  • Inductive biases of multi-task learning and finetuning: multiple regimes of feature reuse

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Inductive biases of multi-task learning and finetuning: multiple regimes of feature reuse

    Jack Lindsey, Samuel Lippl p118745-118776 from Advances in Neural Information Processing Systems 37
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  • Inevitable Trade-off between Watermark Strength and Speculative Sampling Efficiency for Language Models

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Inevitable Trade-off between Watermark Strength and Speculative Sampling Efficiency for Language Models

    Zhengmian Hu, Heng Huang p55370-55402 from Advances in Neural Information Processing Systems 37
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  • Inexact Augmented Lagrangian Methods for Conic Optimization: Quadratic Growth and Linear Convergence

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Inexact Augmented Lagrangian Methods for Conic Optimization: Quadratic Growth and Linear Convergence

    Lijun Ding, Feng-Yi Liao, Yang Zheng p41013-41050 from Advances in Neural Information Processing Systems 37
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  • Infer Induced Sentiment of Comment Response to Video: A New Task, Dataset and Baseline

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Infer Induced Sentiment of Comment Response to Video: A New Task, Dataset and Baseline

    Guoguang Du, Baoyu Fan, Zhenhua Guo, Xuanjing Huang, Qi Jia, Liang Jin, Rengang Li, Lu Liu, Cong Xu, Yaqian Zhao p103737-103750 from Advances in Neural Information Processing Systems 37
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  • Inference of Neural Dynamics Using Switching Recurrent Neural Networks

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Inference of Neural Dynamics Using Switching Recurrent Neural Networks

    Shreya Saxena, Yongxu Zhang p131456-131481 from Advances in Neural Information Processing Systems 37
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  • Inference via Interpolation: Contrastive Representations Provably Enable Planning and Inference

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Inference via Interpolation: Contrastive Representations Provably Enable Planning and Inference

    Benjamin Eysenbach, Sergey Levine, Vivek Myers, Ruslan Salakhutdinov p58901-58928 from Advances in Neural Information Processing Systems 37
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  • Inferring Neural Signed Distance Functions by Overfitting on Single Noisy Point Clouds through Finetuning Data-Driven based Priors

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Inferring Neural Signed Distance Functions by Overfitting on Single Noisy Point Clouds through Finetuning Data-Driven based Priors

    Chao Chen, Zhizhong Han, Yu-Shen Liu p60872-60896 from Advances in Neural Information Processing Systems 37
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  • Inferring stochastic low-rank recurrent neural networks from neural data

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Inferring stochastic low-rank recurrent neural networks from neural data

    Manuel Gloeckler, Jakob H Macke, Matthijs Pals, Felix Pei, A Erdem Sağtekin p18225-18264 from Advances in Neural Information Processing Systems 37
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  • InfiBench: Evaluating the Question-Answering Capabilities of Code Large Language Models

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    InfiBench: Evaluating the Question-Answering Capabilities of Code Large Language Models

    Shijie Geng, Yibo He, Ziyue Hua, Linyi Li, Zhenwen Li, Guanghan Ning, Siwei Wang, Tao Xie, Hongxia Yang, Hao Yu p128668-128698 from Advances in Neural Information Processing Systems 37
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  • Infinite Limits of Multi-head Transformer Dynamics

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Infinite Limits of Multi-head Transformer Dynamics

    Blake Bordelon, Hamza Chaudhry, Cengiz Pehlevan p35824-35878 from Advances in Neural Information Processing Systems 37
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  • Infinite-Dimensional Feature Interaction

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Infinite-Dimensional Feature Interaction

    Xiang Chen, Maoliang Li, Jinjun Xiong, Chenhui Xu, Zirui Xu, Fuxun Yu, Zihao Zheng p92381-92400 from Advances in Neural Information Processing Systems 37
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  • Inflationary Flows: Calibrated Bayesian Inference with Diffusion-Based Models

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Inflationary Flows: Calibrated Bayesian Inference with Diffusion-Based Models

    Daniela de Albuquerque, John Pearson p96847-96891 from Advances in Neural Information Processing Systems 37
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  • InfLLM: Training-Free Long-Context Extrapolation for LLMs with an Efficient Context Memory

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    InfLLM: Training-Free Long-Context Extrapolation for LLMs with an Efficient Context Memory

    Xu Han, Yankai Lin, Zhiyuan Liu, Maosong Sun, Chaojun Xiao, Guangxuan Xiao, Pengle Zhang, Zhengyan Zhang p119638-119661 from Advances in Neural Information Processing Systems 37
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  • InfoRM: Mitigating Reward Hacking in RLHF via Information-Theoretic Reward Modeling

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    InfoRM: Mitigating Reward Hacking in RLHF via Information-Theoretic Reward Modeling

    Rong Bao, Liang Ding, Yuchun Miao, Dacheng Tao, Lefei Zhang, Sen Zhang p134387-134429 from Advances in Neural Information Processing Systems 37
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  • Information Re-Organization Improves Reasoning in Large Language Models

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Information Re-Organization Improves Reasoning in Large Language Models

    Xiaoxia Cheng, Weiming Lu, Zeqi Tan, Wei Xue p130214-130236 from Advances in Neural Information Processing Systems 37
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  • Information-theoretic Generalization Analysis for Expected Calibration Error

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Information-theoretic Generalization Analysis for Expected Calibration Error

    Masahiro Fujisawa, Futoshi Futami p84246-84297 from Advances in Neural Information Processing Systems 37
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  • Information-theoretic Limits of Online Classification with Noisy Labels

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Information-theoretic Limits of Online Classification with Noisy Labels

    Ananth Grama, Wojciech Szpankowski, Changlong Wu p113937-113962 from Advances in Neural Information Processing Systems 37
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  • Infusing Self-Consistency into Density Functional Theory Hamiltonian Prediction via Deep Equilibrium Models

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Infusing Self-Consistency into Density Functional Theory Hamiltonian Prediction via Deep Equilibrium Models

    Lin Huang, Chang Liu, Bin Shao, Zun Wang, Xinran Wei, Lijun Wu, He Zhang, Nianlong Zou p89652-89681 from Advances in Neural Information Processing Systems 37
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  • Infusing Synthetic Data with Real-World Patterns for Zero-Shot Material State Segmentation

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Infusing Synthetic Data with Real-World Patterns for Zero-Shot Material State Segmentation

    Alan Aspuru-Guzik, Manuel Drehwald, Sagi Eppel, Jolina Li p60237-60259 from Advances in Neural Information Processing Systems 37
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  • Initialization is Critical to Whether Transformers Fit Composite Functions by Reasoning or Memorizing

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Initialization is Critical to Whether Transformers Fit Composite Functions by Reasoning or Memorizing

    Pengxiao Lin, Zhiwei Wang, Zhi-Qin Xu, Yaoyu Zhang, Zhongwang Zhang p14093-14126 from Advances in Neural Information Processing Systems 37
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  • Initializing Services in Interactive ML Systems for Diverse Users

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Initializing Services in Interactive ML Systems for Diverse Users

    Avinandan Bose, Mihaela Curmei, Sarah Dean, Maryam Fazel, Daniel Jiang, Jamie Morgenstern, Lillian Ratliff p57701-57732 from Advances in Neural Information Processing Systems 37
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  • Initializing Variable-sized Vision Transformers from Learngene with Learnable Transformation

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Initializing Variable-sized Vision Transformers from Learngene with Learnable Transformation

    Xin Geng, Shiyu Xia, Xu Yang, Yuankun Zu p43341-43366 from Advances in Neural Information Processing Systems 37
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