Neural Information Processing Systems Foundation, Inc. (NeurIPS)

  • Hierarchical VAEs provide a normative account of motion processing in the primate brain

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Hierarchical VAEs provide a normative account of motion processing in the primate brain

    Hadi Vafaii, Jacob Yates, Daniel Butts p46152-46190 from Advances in Neural Information Processing Systems 36
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  • Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly Detection

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly Detection

    Ruiying Lu, Yujie Wu, Long Tian, Dongsheng Wang, Bo Chen, Xiyang Liu, Ruimin Hu p8487-8500 from Advances in Neural Information Processing Systems 36
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  • Hierarchical Visual Feature Aggregation for OCR-Free Document Understanding

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Hierarchical Visual Feature Aggregation for OCR-Free Document Understanding

    Jaeyoo Park, Jin Choi, Jeonghyung Park, Bohyung Han p105972-105996 from Advances in Neural Information Processing Systems 37
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  • Hierarchically Gated Recurrent Neural Network for Sequence Modeling

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Hierarchically Gated Recurrent Neural Network for Sequence Modeling

    Zhen Qin, Songlin Yang, Yiran Zhong p33202-33221 from Advances in Neural Information Processing Systems 36
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  • Hierarchy-Agnostic Unsupervised Segmentation: Parsing Semantic Image Structure

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Hierarchy-Agnostic Unsupervised Segmentation: Parsing Semantic Image Structure

    Simone Rossetti, Fiora Pirri p98898-98935 from Advances in Neural Information Processing Systems 37
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  • HierSpeech: Bridging the Gap Between Text and Speech by Hierarchical Variational Inference Using Self-Supervised Representations for Speech Synthesis

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    HierSpeech: Bridging the Gap Between Text and Speech by Hierarchical Variational Inference Using Self-Supervised Representations for Speech Synthesis

    Sang-Hoon Lee, Seung-Bin Kim, Ji-Hyun Lee, Eunwoo Song, Min-Jae Hwang, Seong-Whan Lee p16624-16636 from Advances in Neural Information Processing Systems 35
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  • HiFC: High-efficiency Flash-based KV Cache Swapping for Scaling LLM Inference

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    HiFC: High-efficiency Flash-based KV Cache Swapping for Scaling LLM Inference

    Inho Jeong, Sunghyeon Woo, Sol Namkung, Dongsuk Jeon p53093-53122 from Advances in Neural Information Processing Systems 38
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  • HiFlow: Training-free High-Resolution Image Generation with Flow-Aligned Guidance

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    HiFlow: Training-free High-Resolution Image Generation with Flow-Aligned Guidance

    Jiazi Bu, Pengyang Ling, Yujie Zhou, Pan Zhang, Tong Wu, Xiaoyi Dong, Yuhang Zang, Yuhang Cao, Dahua Lin, Jiaqi Wang p160164-160198 from Advances in Neural Information Processing Systems 38
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  • High dimensional, tabular deep learning with an auxiliary knowledge graph

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    High dimensional, tabular deep learning with an auxiliary knowledge graph

    Camilo Ruiz, Hongyu Ren, Kexin Huang, Jure Leskovec p26348-26371 from Advances in Neural Information Processing Systems 36
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  • High Dynamic Range Imaging with Time-Encoding Spike Camera

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    High Dynamic Range Imaging with Time-Encoding Spike Camera

    Zhenkun Zhu, Ruiqin Xiong, Jiyu Xie, Yuanlin Wang, Xinfeng Zhang, Tiejun Huang p101300-101326 from Advances in Neural Information Processing Systems 38
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  • High Precision Causal Model Evaluation with Conditional Randomization

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    High Precision Causal Model Evaluation with Conditional Randomization

    Chao Ma, Cheng Zhang p52615-52627 from Advances in Neural Information Processing Systems 36
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  • High Rank Path Development: an approach to learning the filtration of stochastic processes

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    High Rank Path Development: an approach to learning the filtration of stochastic processes

    Jiajie Tao, Hao Ni, Chong Liu p115309-115350 from Advances in Neural Information Processing Systems 37
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  • High Resolution UDF Meshing via Iterative Networks

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    High Resolution UDF Meshing via Iterative Networks

    Federico Stella, Nicolas Talabot, Hieu Le, Pascal Fua p189339-189369 from Advances in Neural Information Processing Systems 38
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  • High-dimensional (Group) Adversarial Training in Linear Regression

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    High-dimensional (Group) Adversarial Training in Linear Regression

    Yiling Xie, Xiaoming Huo p31708-31735 from Advances in Neural Information Processing Systems 37
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  • High-Dimensional Additive Gaussian Processes Under Monotonicity Constraints

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    High-Dimensional Additive Gaussian Processes Under Monotonicity Constraints

    Andrés López-Lopera, Francois Bachoc, Olivier Roustant p8041-8053 from Advances in Neural Information Processing Systems 35
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  • High-dimensional Asymptotics of Denoising Autoencoders

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    High-dimensional Asymptotics of Denoising Autoencoders

    Hugo Cui, Lenka Zdeborová p11850-11890 from Advances in Neural Information Processing Systems 36
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  • High-Dimensional Asymptotics of Feature Learning: How One Gradient Step Improves the Representation

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    High-Dimensional Asymptotics of Feature Learning: How One Gradient Step Improves the Representation

    Jimmy Ba, Murat Erdogdu, Taiji Suzuki, Zhichao Wang, Denny Wu, Greg Yang p37932-37946 from Advances in Neural Information Processing Systems 35
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  • High-Dimensional Calibration from Swap Regret

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    High-Dimensional Calibration from Swap Regret

    Maxwell Fishelson, Noah Golowich, Mehryar Mohri, Jon Schneider p56149-56181 from Advances in Neural Information Processing Systems 38
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  • High-dimensional Contextual Bandit Problem without Sparsity

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    High-dimensional Contextual Bandit Problem without Sparsity

    Junpei Komiyama, Masaaki Imaizumi p49416-49427 from Advances in Neural Information Processing Systems 36
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  • High-Dimensional Limit Theorems for SGD: Effective Dynamics and Critical Scaling

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    High-Dimensional Limit Theorems for SGD: Effective Dynamics and Critical Scaling

    Gerard Ben Arous, Reza Gheissari, Aukosh Jagannath p25349-25362 from Advances in Neural Information Processing Systems 35
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  • High-dimensional neuronal activity from low-dimensional latent dynamics: a solvable model

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    High-dimensional neuronal activity from low-dimensional latent dynamics: a solvable model

    Valentin Schmutz, Ali Haydaroğlu, Shuqi Wang, Yixiao Feng, Matteo Carandini, Kenneth D Harris p131228-131262 from Advances in Neural Information Processing Systems 38
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  • High-Fidelity Audio Compression with Improved RVQGAN

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    High-Fidelity Audio Compression with Improved RVQGAN

    Rithesh Kumar, Prem Seetharaman, Alejandro Luebs, Ishaan Kumar, Kundan Kumar p27980-27993 from Advances in Neural Information Processing Systems 36
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  • High-order Equivariant Flow Matching for Density Functional Theory Hamiltonian Prediction

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    High-order Equivariant Flow Matching for Density Functional Theory Hamiltonian Prediction

    Seongsu Kim, Nayoung Kim, Dongwoo Kim, Sungsoo Ahn p14907-14949 from Advances in Neural Information Processing Systems 38
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  • High-Order Flow Matching: Unified Framework and Sharp Statistical Rates

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    High-Order Flow Matching: Unified Framework and Sharp Statistical Rates

    Maojiang Su, Jerry Yao-Chieh Hu, Yi-Chen Lee, Ning Zhu, Jui-Hui Chung, Shang Wu, Zhao Song, Minshuo Chen, Han Liu p51192-51304 from Advances in Neural Information Processing Systems 38
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