Paper

  • 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 These the Same Apple? Comparing Images Based on Object Intrinsics

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Are These the Same Apple? Comparing Images Based on Object Intrinsics

    Klemen Kotar, Stephen Tian, Jiajun Wu, Dan Yamins, Hong-Xing Yu p40853-40871 from Advances in Neural Information Processing Systems 36
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  • Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks

    Krishnaram Kenthapadi, Matthäus Kleindessner, Francesco Locatello, Michael Lohaus, Chris Russell p16548-16562 from Advances in Neural Information Processing Systems 35
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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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