Neural Information Processing Systems Foundation, Inc. (NeurIPS)

  • Scalable Neural Video Representations with Learnable Positional Features

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Scalable Neural Video Representations with Learnable Positional Features

    Subin Kim, Sihyun Yu, Jaeho Lee, Jinwoo Shin p12718-12731 from Advances in Neural Information Processing Systems 35
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  • Scalable Optimization in the Modular Norm

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Scalable Optimization in the Modular Norm

    Tim Large, Yang Liu, Minyoung Huh, Hyojin Bahng, Phillip Isola, Jeremy Bernstein p73501-73548 from Advances in Neural Information Processing Systems 37
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  • Scalable Primal-Dual Actor-Critic Method for Safe Multi-Agent RL with General Utilities

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Scalable Primal-Dual Actor-Critic Method for Safe Multi-Agent RL with General Utilities

    Donghao Ying, Yunkai Zhang, Yuhao Ding, Alec Koppel, Javad Lavaei p36524-36539 from Advances in Neural Information Processing Systems 36
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  • Scalable Representation Learning in Linear Contextual Bandits with Constant Regret Guarantees

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Scalable Representation Learning in Linear Contextual Bandits with Constant Regret Guarantees

    Andrea Tirinzoni, Matteo Papini, Ahmed Touati, Alessandro Lazaric, Matteo Pirotta p2307-2319 from Advances in Neural Information Processing Systems 35
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  • Scalable Sensitivity and Uncertainty Analyses for Causal-Effect Estimates of Continuous-Valued Interventions

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Scalable Sensitivity and Uncertainty Analyses for Causal-Effect Estimates of Continuous-Valued Interventions

    Andrew Jesson, Alyson Douglas, Peter Manshausen, Maëlys Solal, Nicolai Meinshausen, Philip Stier, Yarin Gal, Uri Shalit p13892-13907 from Advances in Neural Information Processing Systems 35
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  • Scalable Transformer for PDE Surrogate Modeling

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Scalable Transformer for PDE Surrogate Modeling

    Zijie Li, Dule Shu, Amir Barati Farimani p28010-28039 from Advances in Neural Information Processing Systems 36
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  • Scalarization for Multi-Task and Multi-Domain Learning at Scale

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Scalarization for Multi-Task and Multi-Domain Learning at Scale

    Amelie Royer, Tijmen Blankevoort, Babak Ehteshami Bejnordi p16917-16941 from Advances in Neural Information Processing Systems 36
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  • Scale Alone Does not Improve Mechanistic Interpretability in Vision Models

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Scale Alone Does not Improve Mechanistic Interpretability in Vision Models

    Roland S. Zimmermann, Thomas Klein, Wieland Brendel p57876-57907 from Advances in Neural Information Processing Systems 36
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  • Scale Equivariant Graph Metanetworks

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Scale Equivariant Graph Metanetworks

    Ioannis Kalogeropoulos, Giorgos Bouritsas, Yannis Panagakis p106800-106840 from Advances in Neural Information Processing Systems 37
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  • Scale-Invariant Learning by Physics Inversion

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Scale-Invariant Learning by Physics Inversion

    Philipp Holl, Vladlen Koltun, Nils Thuerey p5390-5403 from Advances in Neural Information Processing Systems 35
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  • Scale-invariant Optimal Sampling for Rare-events Data and Sparse Models

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Scale-invariant Optimal Sampling for Rare-events Data and Sparse Models

    Jing Wang, Haiying Wang, Hao Zhang p98384-98418 from Advances in Neural Information Processing Systems 37
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  • Scale-Space Hypernetworks for Efficient Biomedical Image Analysis

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Scale-Space Hypernetworks for Efficient Biomedical Image Analysis

    Jose Javier Gonzalez Ortiz, John Guttag, Adrian Dalca p12760-12773 from Advances in Neural Information Processing Systems 36
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  • Scale-teaching: Robust Multi-scale Training for Time Series Classification with Noisy Labels

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Scale-teaching: Robust Multi-scale Training for Time Series Classification with Noisy Labels

    Zhen Liu, Ma Peitian, Dongliang Chen, Wenbin Pei, Qianli Ma p33726-33757 from Advances in Neural Information Processing Systems 36
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  • ScaleKD: Strong Vision Transformers Could Be Excellent Teachers

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    ScaleKD: Strong Vision Transformers Could Be Excellent Teachers

    Jiawei Fan, Chao Li, Xiaolong Liu, Anbang Yao p63290-63315 from Advances in Neural Information Processing Systems 37
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  • ScaleLong: Towards More Stable Training of Diffusion Model via Scaling Network Long Skip Connection

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    ScaleLong: Towards More Stable Training of Diffusion Model via Scaling Network Long Skip Connection

    Zhongzhan Huang, Pan Zhou, Shuicheng Yan, Liang Lin p70376-70401 from Advances in Neural Information Processing Systems 36
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  • Scaling & Shifting Your Features: A New Baseline for Efficient Model Tuning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Scaling & Shifting Your Features: A New Baseline for Efficient Model Tuning

    Dongze Lian, Daquan Zhou, Jiashi Feng, Xinchao Wang p109-123 from Advances in Neural Information Processing Systems 35
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  • Scaling Continuous Latent Variable Models as Probabilistic Integral Circuits

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Scaling Continuous Latent Variable Models as Probabilistic Integral Circuits

    Gennaro Gala, Cassio De Campos, Antonio Vergari, Erik Quaeghebeur p10959-10987 from Advances in Neural Information Processing Systems 37
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  • Scaling Data-Constrained Language Models

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Scaling Data-Constrained Language Models

    Niklas Muennighoff, Alexander Rush, Boaz Barak, Teven Le Scao, Nouamane Tazi, Aleksandra Piktus, Sampo Pyysalo, Thomas Wolf, Colin Raffel p50358-50376 from Advances in Neural Information Processing Systems 36
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  • Scaling Law for Time Series Forecasting

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Scaling Law for Time Series Forecasting

    Jingzhe Shi, Qinwei Ma, Huan Ma, Lei Li p83314-83344 from Advances in Neural Information Processing Systems 37
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  • Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations

    Alexander Hägele, Elie Bakouch, Atli Kosson, Loubna Allal, Leandro Von Werra, Martin Jaggi p76232-76264 from Advances in Neural Information Processing Systems 37
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  • Scaling Laws for Hyperparameter Optimization

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Scaling Laws for Hyperparameter Optimization

    Arlind Kadra, Maciej Janowski, Martin Wistuba, Josif Grabocka p47527-47553 from Advances in Neural Information Processing Systems 36
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  • Scaling laws for language encoding models in fMRI

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Scaling laws for language encoding models in fMRI

    Richard Antonello, Aditya Vaidya, Alexander Huth p21895-21907 from Advances in Neural Information Processing Systems 36
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  • Scaling laws for learning with real and surrogate data

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Scaling laws for learning with real and surrogate data

    Ayush Jain, Andrea Montanari, Eren Sasoglu p110246-110289 from Advances in Neural Information Processing Systems 37
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  • Scaling Laws for Reward Model Overoptimization in Direct Alignment Algorithms

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    Scaling Laws for Reward Model Overoptimization in Direct Alignment Algorithms

    Rafael Rafailov, Yaswanth Chittepu, Ryan Park, Harshit Sikchi, Joey Hejna, W. Knox, Chelsea Finn, Scott Niekum p126207-126242 from Advances in Neural Information Processing Systems 37
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