Neural Information Processing Systems Foundation, Inc. (NeurIPS)

  • Approximating the Top Eigenvector in Random Order Streams

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Approximating the Top Eigenvector in Random Order Streams

    Praneeth Kacham, David Woodruff p113461-113483 from Advances in Neural Information Processing Systems 37
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  • Approximation Rate of the Transformer Architecture for Sequence Modeling

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Approximation Rate of the Transformer Architecture for Sequence Modeling

    Haotian Jiang, Qianxiao Li p68926-68955 from Advances in Neural Information Processing Systems 37
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  • Approximation-Aware Bayesian Optimization

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Approximation-Aware Bayesian Optimization

    John Cunningham, David Eriksson, Jacob Gardner, Kyurae Kim, Natalie Maus, Geoff Pleiss p21114-21140 from Advances in Neural Information Processing Systems 37
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  • AR-Pro: Counterfactual Explanations for Anomaly Repair with Formal Properties

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    AR-Pro: Counterfactual Explanations for Anomaly Repair with Formal Properties

    Xiayan Ji, Insup Lee, Oleg Sokolsky, Eric Wong, Anton Xue p16133-16159 from Advances in Neural Information Processing Systems 37
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  • ARC: A Generalist Graph Anomaly Detector with In-Context Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    ARC: A Generalist Graph Anomaly Detector with In-Context Learning

    Qingfeng Chen, Shiyuan Li, Yixin Liu, Shirui Pan, Chengqi Zhang, Yu Zheng p50772-50804 from Advances in Neural Information Processing Systems 37
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  • Archaeoscape: Bringing Aerial Laser Scanning Archaeology to the Deep Learning Era

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Archaeoscape: Bringing Aerial Laser Scanning Archaeology to the Deep Learning Era

    Damian Evans, Loic Landrieu, Yohann Perron, Christophe Pottier, Vladyslav Sydorov, Adam Wijker p25888-25912 from Advances in Neural Information Processing Systems 37
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  • Architect: Generating Vivid and Interactive 3D Scenes with Hierarchical 2D Inpainting

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Architect: Generating Vivid and Interactive 3D Scenes with Hierarchical 2D Inpainting

    Jiting Cai, Zhehuan Chen, Chuang Gan, Jiageng Liu, Xiaowen Qiu, Tsun-Hsuan Wang, Yian Wang, Yufei Wang, Zhou Xian p67575-67603 from Advances in Neural Information Processing Systems 37
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  • Arctique: An artificial histopathological dataset unifying realism and controllability for uncertainty quantification

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Arctique: An artificial histopathological dataset unifying realism and controllability for uncertainty quantification

    Jan Albrecht, Philip Bischoff, Jannik Franzen, Vanessa Guarino, Dagmar Kainmueller, Christoph Karg, Nora Koreuber, Claudia Winklmayr, Xiaoyan Yu p71855-71867 from Advances in Neural Information Processing Systems 37
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  • Are Graph Neural Networks Optimal Approximation Algorithms?

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Are Graph Neural Networks Optimal Approximation Algorithms?

    Stefanie Jegelka, Nikolaos Karalias, Eric Lu, Jessica Xu, Morris Yau p73124-73181 from Advances in Neural Information Processing Systems 37
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  • Are High-Degree Representations Really Unnecessary in Equivariant Graph Neural Networks?

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Are High-Degree Representations Really Unnecessary in Equivariant Graph Neural Networks?

    Jiacheng Cen, Wenbing Huang, Anyi Li, Ning Lin, Yuxiang Ren, Zihe Wang p26238-26266 from Advances in Neural Information Processing Systems 37
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  • Are Language Models Actually Useful for Time Series Forecasting?

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Are Language Models Actually Useful for Time Series Forecasting?

    Tim Althoff, Vinayak Gupta, Thomas Hartvigsen, Mike Merrill, Mingtian Tan p60162-60191 from Advances in Neural Information Processing Systems 37
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  • Are Large Language Models Good Statisticians?

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Are Large Language Models Good Statisticians?

    Shiyin Du, Boyan Li, Yuyu Luo, Nan Tang, Yizhang Zhu p62697-62731 from Advances in Neural Information Processing Systems 37
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  • Are Large-scale Soft Labels Necessary for Large-scale Dataset Distillation?

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Are Large-scale Soft Labels Necessary for Large-scale Dataset Distillation?

    Yang He, Lingao Xiao p16406-16437 from Advances in Neural Information Processing Systems 37
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  • Are More LLM Calls All You Need? Towards the Scaling Properties of Compound AI Systems

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Are More LLM Calls All You Need? Towards the Scaling Properties of Compound AI Systems

    Peter Bailis, Lingjiao Chen, Jared Davis, Boris Hanin, Ion Stoica, Matei Zaharia, James Zou p45767-45790 from Advances in Neural Information Processing Systems 37
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  • Are Multiple Instance Learning Algorithms Learnable for Instances?

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Are Multiple Instance Learning Algorithms Learnable for Instances?

    Jaeseok Jang, Hyuk-Yoon Kwon p10575-10612 from Advances in Neural Information Processing Systems 37
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  • Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology

    Alexander Binder, Andreas Kleppe, Dhananjay Tomar p43499-43532 from Advances in Neural Information Processing Systems 37
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  • Are Self-Attentions Effective for Time Series Forecasting?

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Are Self-Attentions Effective for Time Series Forecasting?

    Dongbin Kim, Hoki Kim, Jaewook Lee, Jinseong Park p114180-114209 from Advances in Neural Information Processing Systems 37
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  • Are Uncertainty Quantification Capabilities of Evidential Deep Learning a Mirage?

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Are Uncertainty Quantification Capabilities of Evidential Deep Learning a Mirage?

    Yuheng Bu, Subhro Das, Soumya Ghosh, J. Ryu, Prasanna Sattigeri, Maohao Shen, Gregory Wornell p107830-107864 from Advances in Neural Information Processing Systems 37
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  • Are We on the Right Way for Evaluating Large Vision-Language Models?

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Are We on the Right Way for Evaluating Large Vision-Language Models?

    Lin Chen, Zehui Chen, Xiaoyi Dong, Haodong Duan, Jinsong Li, Dahua Lin, Yu Qiao, Jiaqi Wang, Yuhang Zang, Pan Zhang, Feng Zhao p27056-27087 from Advances in Neural Information Processing Systems 37
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  • Are Your Models Still Fair? Fairness Attacks on Graph Neural Networks via Node Injections

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Are Your Models Still Fair? Fairness Attacks on Graph Neural Networks via Node Injections

    Nuo Chen, Hong Huang, Hai Jin, Zihan Luo, Jiping Zhang, Yongkang Zhou p71568-71595 from Advances in Neural Information Processing Systems 37
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  • ArkVale: Efficient Generative LLM Inference with Recallable Key-Value Eviction

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    ArkVale: Efficient Generative LLM Inference with Recallable Key-Value Eviction

    Beiquan Cao, Renze Chen, Meng Li, Xiuhong Li, Yun Liang, Zhuofeng Wang, Xuechao Wei, Tong Wu, Shengen Yan, Size Zheng p113134-113155 from Advances in Neural Information Processing Systems 37
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  • AROMA: Preserving Spatial Structure for Latent PDE Modeling with Local Neural Fields

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    AROMA: Preserving Spatial Structure for Latent PDE Modeling with Local Neural Fields

    Patrick Gallinari, Etienne Le Naour, Louis Serrano, Jean-Noël Vittaut, Thomas X Wang p13489-13521 from Advances in Neural Information Processing Systems 37
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  • ART: Automatic Red-teaming for Text-to-Image Models to Protect Benign Users

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    ART: Automatic Red-teaming for Text-to-Image Models to Protect Benign Users

    Kangjie Chen, Guanlin Li, Jie Zhang, Shudong Zhang, Tianwei Zhang p91184-91219 from Advances in Neural Information Processing Systems 37
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  • Artemis: Towards Referential Understanding in Complex Videos

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Artemis: Towards Referential Understanding in Complex Videos

    David Doermann, Tianren Ma, Jihao Qiu, Xi Tang, Yunjie Tian, Lingxi Xie, Pengyu Yan, Qixiang Ye, Yuan Zhang p114321-114347 from Advances in Neural Information Processing Systems 37
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