Neural Information Processing Systems Foundation, Inc. (NeurIPS)

  • How many samples are needed to leverage smoothness?

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    How many samples are needed to leverage smoothness?

    Vivien Cabannes, Stefano Vigogna p26776-26809 from Advances in Neural Information Processing Systems 36
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  • How Many Tokens Do 3D Point Cloud Transformer Architectures Really Need?

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    How Many Tokens Do 3D Point Cloud Transformer Architectures Really Need?

    Tuan Tran Anh, Duy M. H. Nguyen, Hoai-Chau Tran, Michael Barz, Khoa D Doan, Roger Wattenhofer, Vien Ngo, Mathias Niepert, Daniel Sonntag, Paul Swoboda p15106-15138 from Advances in Neural Information Processing Systems 38
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  • How Mask Matters: Towards Theoretical Understandings of Masked Autoencoders

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    How Mask Matters: Towards Theoretical Understandings of Masked Autoencoders

    Qi Zhang, Yifei Wang, Yisen Wang p27127-27139 from Advances in Neural Information Processing Systems 35
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  • How Memory in Optimization Algorithms Implicitly Modifies the Loss

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    How Memory in Optimization Algorithms Implicitly Modifies the Loss

    Matias Cattaneo, Boris Shigida p173262-173299 from Advances in Neural Information Processing Systems 38
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  • How Molecules Impact Cells: Unlocking Contrastive PhenoMolecular Retrieval

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    How Molecules Impact Cells: Unlocking Contrastive PhenoMolecular Retrieval

    Philip Fradkin, Puria Azadi, Karush Suri, Frederik Wenkel, Ali Bashashati, Maciej Sypetkowski, Dominique Beaini p110667-110701 from Advances in Neural Information Processing Systems 37
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  • How Particle System Theory Enhances Hypergraph Message Passing

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    How Particle System Theory Enhances Hypergraph Message Passing

    Yixuan Ma, Kai Yi, Pietro Lió, Shi Jin, Yuguang Wang p150000-150031 from Advances in Neural Information Processing Systems 38
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  • How Patterns Dictate Learnability in Sequential Data

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    How Patterns Dictate Learnability in Sequential Data

    Mario Morawski, Anaïs Després, Remi Rehm p26854-26889 from Advances in Neural Information Processing Systems 38
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  • How Powerful Are K-Hop Message Passing Graph Neural Networks

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    How Powerful Are K-Hop Message Passing Graph Neural Networks

    Jiarui Feng, Yixin Chen, Fuhai Li, Anindya Sarkar, Muhan Zhang p4776-4790 from Advances in Neural Information Processing Systems 35
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  • How Re-sampling Helps for Long-Tail Learning?

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    How Re-sampling Helps for Long-Tail Learning?

    Jiang-Xin Shi, Tong Wei, Yuke Xiang, Yu-Feng Li p75669-75687 from Advances in Neural Information Processing Systems 36
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  • How Sampling Impacts the Robustness of Stochastic Neural Networks

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    How Sampling Impacts the Robustness of Stochastic Neural Networks

    Sina Däubener, Asja Fischer p10230-10243 from Advances in Neural Information Processing Systems 35
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  • How Sparse Can We Prune A Deep Network: A Fundamental Limit Perspective

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    How Sparse Can We Prune A Deep Network: A Fundamental Limit Perspective

    Qiaozhe Zhang, Ruijie Zhang, Jun Sun, Yingzhuang Liu p91337-91372 from Advances in Neural Information Processing Systems 37
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  • How to Auto-optimize Prompts for Domain Tasks? Adaptive Prompting and Reasoning through Evolutionary Domain Knowledge Adaptation

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    How to Auto-optimize Prompts for Domain Tasks? Adaptive Prompting and Reasoning through Evolutionary Domain Knowledge Adaptation

    Yang Zhao, Pu Wang, Hao Frank Yang p54314-54349 from Advances in Neural Information Processing Systems 38
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  • How to Boost Any Loss Function

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    How to Boost Any Loss Function

    Richard Nock, Yishay Mansour p15947-15982 from Advances in Neural Information Processing Systems 37
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  • How to build a consistency model: Learning flow maps via self-distillation

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    How to build a consistency model: Learning flow maps via self-distillation

    Nicholas Boffi, Michael Albergo, Eric Vanden-Eijnden p37559-37595 from Advances in Neural Information Processing Systems 38
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  • How to Continually Adapt Text-to-Image Diffusion Models for Flexible Customization?

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    How to Continually Adapt Text-to-Image Diffusion Models for Flexible Customization?

    Jiahua Dong, Wenqi Liang, Hongliu Li, Duzhen Zhang, Meng Cao, Henghui Ding, Salman Khan, Fahad Khan p130057-130083 from Advances in Neural Information Processing Systems 37
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  • How to Data in Datathons

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    How to Data in Datathons

    Carlos Mougan, Richard Plant, Clare Teng, Marya Bazzi, Alvaro Cabrejas Egea, Ryan Chan, David Salvador Jasin, Martin Stoffel, Kirstie Whitaker, Jules Manser p10440-10456 from Advances in Neural Information Processing Systems 36
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  • How to Fine-tune the Model: Unified Model Shift and Model Bias Policy Optimization

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    How to Fine-tune the Model: Unified Model Shift and Model Bias Policy Optimization

    Hai Zhang, Hang Yu, Junqiao Zhao, Di Zhang, Xiao Zhang, Hongtu Zhou, Chang Huang, Chen Ye p59252-59272 from Advances in Neural Information Processing Systems 36
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  • How to Learn a Star: Binary Classification with Starshaped Polyhedral Sets

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    How to Learn a Star: Binary Classification with Starshaped Polyhedral Sets

    Marie-Charlotte Brandenburg, Katharina Jochemko p92234-92260 from Advances in Neural Information Processing Systems 38
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  • How to Scale Your EMA

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    How to Scale Your EMA

    Dan Busbridge, Jason Ramapuram, Pierre Ablin, Tatiana Likhomanenko, Eeshan Gunesh Dhekane, Xavier Suau Cuadros, Russell Webb p73122-73174 from Advances in Neural Information Processing Systems 36
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  • How to Select Which Active Learning Strategy is Best Suited for Your Specific Problem and Budget

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    How to Select Which Active Learning Strategy is Best Suited for Your Specific Problem and Budget

    Guy Hacohen, Daphna Weinshall p13395-13407 from Advances in Neural Information Processing Systems 36
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  • How to Solve Contextual Goal-Oriented Problems with Offline Datasets?

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    How to Solve Contextual Goal-Oriented Problems with Offline Datasets?

    Ying Fan, Jingling Li, Adith Swaminathan, Aditya Modi, Ching-An Cheng p99433-99463 from Advances in Neural Information Processing Systems 37
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  • How to Talk So AI Will Learn: Instructions, Descriptions, and Autonomy

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    How to Talk So AI Will Learn: Instructions, Descriptions, and Autonomy

    Theodore Sumers, Robert Hawkins, Mark Ho, Tom Griffiths, Dylan Hadfield-Menell p34762-34775 from Advances in Neural Information Processing Systems 35
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  • How to Train Your LLM Web Agent: A Statistical Diagnosis

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    How to Train Your LLM Web Agent: A Statistical Diagnosis

    Dheeraj Vattikonda, Santhoshi Ravichandran, Emiliano Penaloza, Hadi Nekoei, Thibault de Chezelles, Megh Thakkar, Nicolas Gontier, Miguel Muñoz-Mármol, Sahar Omidi Shayegan, Stefania Raimondo, Steve (Xue) Liu, Alexandre Drouin, et al. p59259-59285 from Advances in Neural Information Processing Systems 38
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  • How to Turn Your Knowledge Graph Embeddings into Generative Models

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    How to Turn Your Knowledge Graph Embeddings into Generative Models

    Lorenzo Loconte, Nicola Di Mauro, Robert Peharz, Antonio Vergari p77713-77744 from Advances in Neural Information Processing Systems 36
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