Paper

  • No "Zero-Shot" Without Exponential Data: Pretraining Concept Frequency Determines Multimodal Model Performance

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    No "Zero-Shot" Without Exponential Data: Pretraining Concept Frequency Determines Multimodal Model Performance

    Samuel Albanie, Matthias Bethge, Adel Bibi, Adhiraj Ghosh, Ameya Prabhu, Yash Sharma, Philip Torr, Vishaal Udandarao p61735-61792 from Advances in Neural Information Processing Systems 37
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  • No Change, No Gain: Empowering Graph Neural Networks with Expected Model Change Maximization for Active Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    No Change, No Gain: Empowering Graph Neural Networks with Expected Model Change Maximization for Active Learning

    Irwin King, Zixing Song, Yifei Zhang p47511-47526 from Advances in Neural Information Processing Systems 36
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  • No Filter: Cultural and Socioeconomic Diversity in Contrastive Vision-Language Models

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    No Filter: Cultural and Socioeconomic Diversity in Contrastive Vision-Language Models

    Ibrahim Alabdulmohsin, Lucas Beyer, Emanuele Bugliarello, Angéline Pouget, Andreas Steiner, Xiao Wang, Xiaohua Zhai p106474-106496 from Advances in Neural Information Processing Systems 37
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  • No Free Delivery Service: Epistemic limits of passive data collection in complex social systems

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    No Free Delivery Service: Epistemic limits of passive data collection in complex social systems

    Maximilian Nickel p102279-102306 from Advances in Neural Information Processing Systems 37
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  • No Free Lunch from Deep Learning in Neuroscience: A Case Study Through Models of the Entorhinal-Hippocampal Circuit

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    No Free Lunch from Deep Learning in Neuroscience: A Case Study Through Models of the Entorhinal-Hippocampal Circuit

    Ila Fiete, Mikail Khona, Rylan Schaeffer p16052-16067 from Advances in Neural Information Processing Systems 35
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  • No Free Lunch in LLM Watermarking: Trade-offs in Watermarking Design Choices

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    No Free Lunch in LLM Watermarking: Trade-offs in Watermarking Design Choices

    Shengyuan Hu, Qi Pang, Virginia Smith, Wenting Zheng p138756-138788 from Advances in Neural Information Processing Systems 37
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  • No Free Lunch Theorem and Black-Box Complexity Analysis for Adversarial Optimisation

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    No Free Lunch Theorem and Black-Box Complexity Analysis for Adversarial Optimisation

    Per Kristian Lehre, Shishen Lin p121570-121597 from Advances in Neural Information Processing Systems 37
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  • No Regrets: Investigating and Improving Regret Approximations for Curriculum Discovery

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    No Regrets: Investigating and Improving Regret Approximations for Curriculum Discovery

    Michael Beukman, Jakob Foerster, Nick Hawes, Bruno Lacerda, Alex Rutherford, Timon Willi p16071-16101 from Advances in Neural Information Processing Systems 37
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  • No Representation Rules Them All in Category Discovery

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    No Representation Rules Them All in Category Discovery

    Sagar Vaze, Andrea Vedaldi, Andrew Zisserman p19962-19989 from Advances in Neural Information Processing Systems 36
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  • No Representation, No Trust: Connecting Representation, Collapse, and Trust Issues in PPO

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    No Representation, No Trust: Connecting Representation, Collapse, and Trust Issues in PPO

    Caglar Gulcehre, Andrea Miele, Skander Moalla, Razvan Pascanu, Daniil Pyatko p69652-69699 from Advances in Neural Information Processing Systems 37
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  • No Train No Gain: Revisiting Efficient Training Algorithms For Transformer-based Language Models

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    No Train No Gain: Revisiting Efficient Training Algorithms For Transformer-based Language Models

    Jean Kaddour, Oscar Key, Matt Kusner, Pasquale Minervini, Piotr Nawrot p25793-25818 from Advances in Neural Information Processing Systems 36
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  • No Train, all Gain: Self-Supervised Gradients Improve Deep Frozen Representations

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    No Train, all Gain: Self-Supervised Gradients Improve Deep Frozen Representations

    Yuki Asano, Andrei Bursuc, Spyros Gidaris, Walter Simoncini p15386-15415 from Advances in Neural Information Processing Systems 37
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