咨询与建议

限定检索结果

文献类型

  • 14 篇 期刊文献
  • 4 篇 会议

馆藏范围

  • 18 篇 电子文献
  • 0 种 纸本馆藏

日期分布

学科分类号

  • 11 篇 工学
    • 10 篇 计算机科学与技术...
    • 8 篇 软件工程
    • 1 篇 信息与通信工程
    • 1 篇 控制科学与工程
    • 1 篇 建筑学
    • 1 篇 土木工程
    • 1 篇 测绘科学与技术
    • 1 篇 化学工程与技术
    • 1 篇 交通运输工程
    • 1 篇 网络空间安全
  • 10 篇 管理学
    • 8 篇 图书情报与档案管...
    • 3 篇 管理科学与工程(可...
    • 1 篇 工商管理
  • 2 篇 理学
    • 1 篇 物理学
    • 1 篇 化学

主题

  • 3 篇 embeddings
  • 2 篇 semantics
  • 2 篇 geometry
  • 1 篇 distillation
  • 1 篇 fluorescence mic...
  • 1 篇 dimensionality r...
  • 1 篇 cameras
  • 1 篇 intelligent robo...
  • 1 篇 numerical stabil...
  • 1 篇 fitting
  • 1 篇 question answeri...
  • 1 篇 benchmarking
  • 1 篇 feature extracti...
  • 1 篇 structured query...
  • 1 篇 robot vision sys...
  • 1 篇 pipelines
  • 1 篇 backpropagation
  • 1 篇 pose estimation

机构

  • 12 篇 ai foundation an...
  • 10 篇 skolkovo institu...
  • 7 篇 cnrs université ...
  • 5 篇 hse university
  • 4 篇 moscow institute...
  • 4 篇 noeon research
  • 3 篇 skoltech
  • 3 篇 st. petersburg d...
  • 2 篇 digital environm...
  • 2 篇 isp ras research...
  • 2 篇 ai foundation al...
  • 2 篇 ai foundation an...
  • 2 篇 mts ai group
  • 2 篇 artificial intel...
  • 2 篇 kaust
  • 2 篇 airi
  • 2 篇 st. petersburg d...
  • 1 篇 center for ai te...
  • 1 篇 chongqing univer...
  • 1 篇 st. petersburg d...

作者

  • 10 篇 barannikov sergu...
  • 9 篇 burnaev evgeny
  • 8 篇 kushnareva laida
  • 8 篇 piontkovskaya ir...
  • 8 篇 tulchinskii edua...
  • 6 篇 kuznetsov kristi...
  • 5 篇 nikolenko sergey
  • 4 篇 magai german
  • 4 篇 koshelev iarosla...
  • 3 篇 voznyuk anastasi...
  • 3 篇 selikhanovych da...
  • 2 篇 voronkova daria
  • 2 篇 lefkimmiatis sta...
  • 2 篇 trofimov ilya
  • 2 篇 filippov alexand...
  • 2 篇 gaintseva tatian...
  • 2 篇 wonka peter
  • 2 篇 voynov oleg
  • 2 篇 andriiainen andr...
  • 2 篇 zhussip magauiya

语言

  • 14 篇 英文
  • 4 篇 其他
检索条件"机构=AI Foundation and Algorithm Lab"
18 条 记 录,以下是11-20 订阅
排序:
Robust ai-Generated Text Detection by Restricted Embeddings
Robust AI-Generated Text Detection by Restricted Embeddings
收藏 引用
2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024
作者: Kuznetsov, Kristian Tulchinskii, Eduard Kushnareva, Laida Magai, German Barannikov, Serguei Nikolenko, Sergey Piontkovskaya, Irina AI Foundation and Algorithm Lab Russia HSE University Russia Noeon Research Japan Skolkovo Institute of Science and Technology Russia CNRS Université Paris Cité France ISP RAS Research Center for Trusted Artificial Intelligence Moscow Russia St. Petersburg Department The Steklov Institute of Mathematics Russia
Growing amount and quality of ai-generated texts makes detecting such content more difficult. In most real-world scenarios, the domain (style and topic) of generated data and the generator model are not known in advan... 详细信息
来源: 评论
LISTENING TO THE WISE FEW: SELECT-AND-COPY ATTENTION HEADS FOR MULTIPLE-CHOICE QA
arXiv
收藏 引用
arXiv 2024年
作者: Tulchinskii, Eduard Kushnareva, Laida Kuznetsov, Kristian Voznyuk, Anastasia Andriiainen, Andrei Piontkovskaya, Irina Burnaev, Evgeny Barannikov, Serguei Skolkovo Institute of Science and Technology Russia AI Foundation and Algorithm Lab Moscow Institute of Physics and Technology Russia CNRS Université Paris Cité France
A standard way to evaluate the abilities of LLM involves presenting a multiple-choice question and selecting the option with the highest logit as the model’s predicted answer. However, such a format for evaluating LL... 详细信息
来源: 评论
Scalar Function Topology Divergence: Comparing Topology of 3D Objects
arXiv
收藏 引用
arXiv 2024年
作者: Trofimov, Ilya Voronkova, Daria Tulchinskii, Eduard Burnaev, Evgeny Barannikov, Serguei Skolkovo Institute of Science and Technology Moscow Russia AIRI Moscow Russia CNRS IMJ Paris Cité University France AI Foundation and Algorithm Lab Moscow Russia
We propose a new topological tool for computer vision - Scalar Function Topology Divergence (SFTD), which measures the dissimilarity of multi-scale topology between sublevel sets of two functions having a common domai... 详细信息
来源: 评论
Intrinsic Dimension Estimation for Robust Detection of ai-Generated Texts
arXiv
收藏 引用
arXiv 2023年
作者: Tulchinskii, Eduard Kuznetsov, Kristian Kushnareva, Laida Cherniavskii, Daniil Nikolenko, Sergey Burnaev, Evgeny Barannikov, Serguei Piontkovskaya, Irina Skolkovo Institute of Science and Technology Russia AI Foundation and Algorithm Lab Russia Russia CNRS Université Paris Cité France St. Petersburg Department the Steklov Institute of Mathematics Russia
Rapidly increasing quality of ai-generated content makes it difficult to distinguish between human and ai-generated texts, which may lead to undesirable consequences for society. Therefore, it becomes increasingly imp...
来源: 评论
Robust ai-Generated Text Detection by Restricted Embeddings
arXiv
收藏 引用
arXiv 2024年
作者: Kuznetsov, Kristian Tulchinskii, Eduard Kushnareva, Laida Magai, German Barannikov, Serguei Nikolenko, Sergey Piontkovskaya, Irina AI Foundation and Algorithm Lab Russia HSE University Russia Noeon Research Japan Skolkovo Institute of Science and Technology Russia CNRS Université Paris Cité France ISP RAS Research Center for Trusted Artificial Intelligence Moscow Russia St. Petersburg Department of the Steklov Institute of Mathematics Russia
Growing amount and quality of ai-generated texts makes detecting such content more difficult. In most real-world scenarios, the domain (style and topic) of generated data and the generator model are not known in advan... 详细信息
来源: 评论
ai-generated text boundary detection with RoFT
arXiv
收藏 引用
arXiv 2023年
作者: Kushnareva, Laida Gaintseva, Tatiana Magai, German Barannikov, Serguei Abulkhanov, Dmitry Kuznetsov, Kristian Tulchinskii, Eduard Piontkovskaya, Irina Nikolenko, Sergey AI Foundation and Algorithm Lab Russia Digital Environment Research Institute Queen Mary University of London United Kingdom HSE University Russia Noeon Research Japan Skolkovo Institute of Science and Technology Russia CNRS Université Paris Cité France St. Petersburg Department The Steklov Institute of Mathematics Russia
Due to the rapid development of large language models, people increasingly often encounter texts that may start as written by a human but continue as machine-generated. Detecting the boundary between human-written and...
来源: 评论
Intrinsic dimension estimation for robust detection of ai-generated texts  23
Intrinsic dimension estimation for robust detection of AI-ge...
收藏 引用
Proceedings of the 37th International Conference on Neural Information Processing Systems
作者: Eduard Tulchinskii Kristian Kuznetsov Laida Kushnareva Daniil Cherniavskii Sergey Nikolenko Evgeny Burnaev Serguei Barannikov Irina Piontkovskaya Skolkovo Institute of Science and Technology Russia AI Foundation and Algorithm Lab Russia Artificial Intelligence Research Institute (AIRI) Russia St. Petersburg Department of the Steklov Institute of Mathematics Russia Skolkovo Institute of Science and Technology Russia and Artificial Intelligence Research Institute (AIRI) Russia Skolkovo Institute of Science and Technology Russia and CNRS Université Paris Cité France
Rapidly increasing quality of ai-generated content makes it difficult to distinguish between human and ai-generated texts, which may lead to undesirable consequences for society. Therefore, it becomes increasingly imp...
来源: 评论
Improving Interpretability and Robustness for the Detection of ai-Generated Images
arXiv
收藏 引用
arXiv 2024年
作者: Gaintseva, Tatiana Kushnareva, Laida Magai, German Piontkovskaya, Irina Nikolenko, Sergey Benning, Marting Barannikov, Serguei Slabaugh, Gregory Digital Environment Research Institute Queen Mary University of London United Kingdom AI Foundation and Algorithm Lab Russia HSE University Russia Noeon Research Japan Skolkovo Institute of Science and Technology Russia CNRS Universite Paris Cite France St. Petersburg Department Steklov Institute of Mathematics Russia Department of Computer Science University College London United Kingdom
With growing abilities of generative models, artificial content detection becomes an increasingly important and difficult task. However, all popular approaches to this prcoblem suffer from poor generalization across d... 详细信息
来源: 评论