Neural Information Processing Systems Foundation, Inc. (NeurIPS)

  • A Prompt-Based Knowledge Graph Foundation Model for Universal In-Context Reasoning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Prompt-Based Knowledge Graph Foundation Model for Universal In-Context Reasoning

    Yuanning Cui, Wei Hu, Zequn Sun p7095-7124 from Advances in Neural Information Processing Systems 37
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  • A provable control of sensitivity of neural networks through a direct parameterization of the overall bi-Lipschitzness

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A provable control of sensitivity of neural networks through a direct parameterization of the overall bi-Lipschitzness

    Yuri Kinoshita, Taro Toyoizumi p85241-85309 from Advances in Neural Information Processing Systems 37
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  • A Recipe for Charge Density Prediction

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Recipe for Charge Density Prediction

    Kyle Bystrom, Xiang Fu, Tommi Jaakkola, Boris Kozinsky, Albert Musaelian, Andrew Rosen, Tess Smidt, Rui Wang p9727-9752 from Advances in Neural Information Processing Systems 37
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  • A Retrospective on the Robot Air Hockey Challenge: Benchmarking Robust, Reliable, and Safe Learning Techniques for Real-world Robotics

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Retrospective on the Robot Air Hockey Challenge: Benchmarking Robust, Reliable, and Safe Learning Techniques for Real-world Robotics

    Thomas Bonenfant, Haitham Bou-Ammar, Sylvain Calinon, Dong Chen, Niklas Funk, Simon Gröger, Jonas Günster, Julius Jankowski, Amarildo Likmeta, Rudolf Lioutikov, Puze Liu, Ziyuan Liu, Ante Marić, Gerhard Neumann, Miguel Olivares-Mendez, p9690-9726 from Advances in Neural Information Processing Systems 37
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  • A robust inlier identification algorithm for point cloud registration via $\mathbf{\ell_0}$-minimization

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A robust inlier identification algorithm for point cloud registration via $\mathbf{\ell_0}$-minimization

    Cheng Cheng, Yinuo Jiang, Xiuchuan Tang, Ye Yuan p63124-63153 from Advances in Neural Information Processing Systems 37
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  • A SARS-CoV-2 Interaction Dataset and VHH Sequence Corpus for Antibody Language Models

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A SARS-CoV-2 Interaction Dataset and VHH Sequence Corpus for Antibody Language Models

    Akihiro Imura, Ryota Maeda, Ryotaro Tamura, Hirofumi Tsuruta, Hiroyuki Yamazaki p116149-116171 from Advances in Neural Information Processing Systems 37
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  • A scalable generative model for dynamical system reconstruction from neuroimaging data

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A scalable generative model for dynamical system reconstruction from neuroimaging data

    Alena Brändle, Daniel Durstewitz, Georgia Koppe, Eric Volkmann p80328-80362 from Advances in Neural Information Processing Systems 37
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  • A Separation in Heavy-Tailed Sampling: Gaussian vs. Stable Oracles for Proximal Samplers

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Separation in Heavy-Tailed Sampling: Gaussian vs. Stable Oracles for Proximal Samplers

    Krishnakumar Balasubramanian, Murat Erdogdu, Ye He, Alireza Mousavi-Hosseini p65260-65296 from Advances in Neural Information Processing Systems 37
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  • A Siamese Transformer with Hierarchical Refinement for Lane Detection

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Siamese Transformer with Hierarchical Refinement for Lane Detection

    Danny Chen, Dong Han, Zinan Lv, Wenzhe Wang p40892-40912 from Advances in Neural Information Processing Systems 37
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  • A Simple and Adaptive Learning Rate for FTRL in Online Learning with Minimax Regret of $\Theta(T^{2/3})$ and its Application to Best-of-Both-Worlds

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Simple and Adaptive Learning Rate for FTRL in Online Learning with Minimax Regret of $\Theta(T^{2/3})$ and its Application to Best-of-Both-Worlds

    Shinji Ito, Taira Tsuchiya p8477-8514 from Advances in Neural Information Processing Systems 37
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  • A Simple and Optimal Approach for Universal Online Learning with Gradient Variations

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Simple and Optimal Approach for Universal Online Learning with Gradient Variations

    Yu-Hu Yan, Peng Zhao, Zhi-Hua Zhou p11132-11163 from Advances in Neural Information Processing Systems 37
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  • A Simple Framework for Generalization in Visual RL under Dynamic Scene Perturbations

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Simple Framework for Generalization in Visual RL under Dynamic Scene Perturbations

    Hyesong Choi, Dongbo Min, Kwanghoon Sohn, Wonil Song p121790-121826 from Advances in Neural Information Processing Systems 37
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  • A Simple Image Segmentation Framework via In-Context Examples

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Simple Image Segmentation Framework via In-Context Examples

    Hao Chen, Chenchen Jing, Hengtao Li, Yang Liu, Chunhua Shen, Xinlong Wang, Muzhi Zhu p25095-25119 from Advances in Neural Information Processing Systems 37
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  • A Simple Remedy for Dataset Bias via Self-Influence: A Mislabeled Sample Perspective

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Simple Remedy for Dataset Bias via Self-Influence: A Mislabeled Sample Perspective

    Yeonsung Jung, Jin-Hwa Kim, Sung-Yub Kim, Jaeyun Song, Eunho Yang, June Yong Yang p43632-43662 from Advances in Neural Information Processing Systems 37
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  • A Simple yet Scalable Granger Causal Structural Learning Approach for Topological Event Sequences

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Simple yet Scalable Granger Causal Structural Learning Approach for Topological Event Sequences

    Mingjia Li, Shuo Liu, Hong Qian, Aimin Zhou p97124-97140 from Advances in Neural Information Processing Systems 37
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  • A Simple yet Universal Framework for Depth Completion

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Simple yet Universal Framework for Depth Completion

    Hae-Gon Jeon, Jin-Hwi Park p23577-23602 from Advances in Neural Information Processing Systems 37
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  • A Simulation Benchmark for Autonomous Racing with Large-Scale Human Data

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Simulation Benchmark for Autonomous Racing with Large-Scale Human Data

    Marko Bertogna, Nicklas Hansen, Nicola Musiu, Ayoub Raji, Adrian Remonda, Eduardo Veas, Xiaolong Wang p102078-102100 from Advances in Neural Information Processing Systems 37
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  • A Single-Step, Sharpness-Aware Minimization is All You Need to Achieve Efficient and Accurate Sparse Training

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Single-Step, Sharpness-Aware Minimization is All You Need to Achieve Efficient and Accurate Sparse Training

    Fatemeh Afghah, Jingjing Fu, Linke Guo, Jie Ji, Gen Li, Xiaolong Ma, Xiaoyong Yuan p44269-44290 from Advances in Neural Information Processing Systems 37
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  • A Sober Look at the Robustness of CLIPs to Spurious Features

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Sober Look at the Robustness of CLIPs to Spurious Features

    Yongqiang Chen, Bo Han, Yong Lin, Ludwig Schmidt, Qizhou Wang, Tong Zhang p122484-122523 from Advances in Neural Information Processing Systems 37
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  • A StrongREJECT for Empty Jailbreaks

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A StrongREJECT for Empty Jailbreaks

    Pieter Abbeel, Dillon Bowen, Scott Emmons, Elvis Hsieh, Qingyuan Lu, Sana Pandey, Alexandra Souly, Justin Svegliato, Sam Toyer, Tu Trinh, Olivia Watkins p125416-125440 from Advances in Neural Information Processing Systems 37
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  • A Structure-Aware Framework for Learning Device Placements on Computation Graphs

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Structure-Aware Framework for Learning Device Placements on Computation Graphs

    Nesreen Ahmed, Paul Bogdan, Mihai Capotă, Shukai Duan, Nikos Kanakaris, Panagiotis Kyriakis, Guixiang Ma, Shahin Nazarian, Heng Ping, Theodore Willke, Xiongye Xiao, Peiyu Zhang p81748-81772 from Advances in Neural Information Processing Systems 37
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  • A Study of Plasticity Loss in On-Policy Deep Reinforcement Learning

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Study of Plasticity Loss in On-Policy Deep Reinforcement Learning

    Jordan Ash, Arthur Juliani p113884-113910 from Advances in Neural Information Processing Systems 37
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  • A Surprisingly Simple Approach to Generalized Few-Shot Semantic Segmentation

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A Surprisingly Simple Approach to Generalized Few-Shot Semantic Segmentation

    Tadanobu Inoue, Takayuki Katsuki, Daiki Kimura, Takayuki Osogami, Haoxiang Qiu, Tomoya Sakai p27005-27023 from Advances in Neural Information Processing Systems 37
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  • A survey and benchmark of high-dimensional Bayesian optimization of discrete sequences

    Neural Information Processing Systems Foundation, Inc. (NeurIPS)

    A survey and benchmark of high-dimensional Bayesian optimization of discrete sequences

    Simon Bartels, Wouter Boomsma, Miguel González-Duque, Søren Hauberg, Richard Michael, Yevgen Zainchkovskyy p140478-140508 from Advances in Neural Information Processing Systems 37
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