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Computer Science

D-Index
66
Citations
24071
World Ranking
2276
National Ranking
130

Overview

Sebastian Riedel is affiliated with University College London in the United Kingdom. Their research primarily spans the field of Computer Science, with a focus on Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Management Science and Operations Research, and Control and Systems Engineering.

The main topics covered in their work include:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Multimodal Machine Learning Applications
  • Domain Adaptation and Few-Shot Learning
  • Data Quality and Management
  • Advanced Graph Neural Networks
  • Robot Manipulation and Learning

Sebastian Riedel has published extensively, with a significant number of papers appearing in the following venues:

  • arXiv (Cornell University)
  • Transactions of the Association for Computational Linguistics
  • Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
  • Findings of the Association for Computational Linguistics: ACL 2022
  • Findings of the Association for Computational Linguistics: NAACL 2022

Recent publications by Riedel include:

  • Contextual Personal Intelligence: A New Paradigm for AI That Evolves With You (2025, arXiv)
  • Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity (2022, Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Long Papers)
  • Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context (2024, arXiv)
  • Autoregressive Entity Retrieval (2020, arXiv)
  • Atlas: Few-shot Learning with Retrieval Augmented Language Models (2022, arXiv)

Sebastian Riedel has collaborated frequently with several researchers, including:

  • Pontus Stenetorp
  • Fabio Petroni
  • Patrick Lewis
  • Pasquale Minervini
  • Gautier Izacard

Best Publications

  • Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

    Patrick S. H. Lewis;Ethan Perez;Aleksandra Piktus;Fabio Petroni

  • Language Models as Knowledge Bases

    Fabio Petroni;Tim Rocktäschel;Patrick S. H. Lewis;Anton Bakhtin

  • Convolutional 2D Knowledge Graph Embeddings

    Tim Dettmers;Pasquale Minervini;Pontus Stenetorp;Sebastian Riedel

  • Complex embeddings for simple link prediction

    Théo Trouillon;Johannes Welbl;Sebastian Riedel;Éric Gaussier

  • Modeling relations and their mentions without labeled text

    Sebastian Riedel;Limin Yao;Andrew McCallum

  • Convolutional 2D knowledge graph embeddings

    Tim Dettmers;Pasquale Minervini;Pontus Stenetorp;Sebastian Riedel

  • The CoNLL 2007 Shared Task on Dependency Parsing

    Joakim Nivre;Johan Hall;Sandra K"ubler;Ryan McDonald

  • Relation Extraction with Matrix Factorization and Universal Schemas

    Sebastian Riedel;Limin Yao;Andrew McCallum;Benjamin M. Marlin

  • Constructing Datasets for Multi-hop Reading Comprehension Across Documents

    Johannes Welbl;Pontus Stenetorp;Sebastian Riedel

  • Fact Checking: Task definition and dataset construction

    Andreas Vlachos;Sebastian Riedel

  • TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data

    Pengcheng Yin;Graham Neubig;Wen-tau Yih;Sebastian Riedel

  • Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

    Yao Lu;Max Bartolo;Alastair Moore;Sebastian Riedel

  • MLQA: Evaluating Cross-lingual Extractive Question Answering

    Patrick S. H. Lewis;Barlas Oguz;Ruty Rinott;Sebastian Riedel

  • Scalable Zero-shot Entity Linking with Dense Entity Retrieval

    Ledell Wu;Fabio Petroni;Martin Josifoski;Sebastian Riedel

  • SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications

    Isabelle Augenstein;Mrinal Das;Sebastian Riedel;Lakshmi Vikraman

  • emoji2vec: Learning Emoji Representations from their Description

    Ben Eisner;Tim Rocktäschel;Isabelle Augenstein;Matko Bosnjak

  • Injecting Logical Background Knowledge into Embeddings for Relation Extraction

    Tim Rocktäschel;Sameer Singh;Sebastian Riedel

  • KILT: a Benchmark for Knowledge Intensive Language Tasks

    Fabio Petroni;Aleksandra Piktus;Angela Fan;Patrick S. H. Lewis

  • A simple but tough-to-beat baseline for the Fake News Challenge stance detection task

    Benjamin Riedel;Isabelle Augenstein;Georgios P. Spithourakis;Sebastian Riedel

  • Knowledge graph completion via complex tensor factorization

    Théo Trouillon;Christopher R. Dance;Éric Gaussier;Johannes Welbl

  • Dynabench: Rethinking Benchmarking in NLP.

    Douwe Kiela;Max Bartolo;Yixin Nie;Divyansh Kaushik

  • Knowledge Graph Completion via Complex Tensor Factorization

    Théo Trouillon;Christopher R. Dance;Johannes Welbl;Sebastian Riedel

Frequent Co-Authors

Tim Rocktäschel
Tim Rocktäschel University College London
Andrew McCallum
Andrew McCallum University of Massachusetts Amherst
Sameer Singh
Sameer Singh University of California, Irvine
Isabelle Augenstein
Isabelle Augenstein University of Copenhagen
Wen-tau Yih
Wen-tau Yih Facebook (United States)
Andreas Vlachos
Andreas Vlachos University of Cambridge
Fabrizio Silvestri
Fabrizio Silvestri Sapienza University of Rome
Douwe Kiela
Douwe Kiela Stanford University
Edward Grefenstette
Edward Grefenstette University College London
Yuji Matsumoto
Yuji Matsumoto Nara Institute of Science and Technology

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