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Sebastian Riedel
Sebastian Riedel
Honorary Professor @ University College London, Researcher @ DeepMind
Dirección de correo verificada de cs.ucl.ac.uk - Página principal
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Retrieval-augmented generation for knowledge-intensive nlp tasks
P Lewis, E Perez, A Piktus, F Petroni, V Karpukhin, N Goyal, H Küttler, ...
Advances in Neural Information Processing Systems 33, 9459-9474, 2020
45732020
Complex embeddings for simple link prediction
T Trouillon, J Welbl, S Riedel, É Gaussier, G Bouchard
International conference on machine learning, 2071-2080, 2016
37002016
Convolutional 2d knowledge graph embeddings
T Dettmers, P Minervini, P Stenetorp, S Riedel
Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018
30442018
Language models as knowledge bases?
F Petroni, T Rocktäschel, P Lewis, A Bakhtin, Y Wu, AH Miller, S Riedel
arXiv preprint arXiv:1909.01066, 2019
27972019
Gemini: a family of highly capable multimodal models
G Team, R Anil, S Borgeaud, JB Alayrac, J Yu, R Soricut, J Schalkwyk, ...
arXiv preprint arXiv:2312.11805, 2023
20702023
Modeling relations and their mentions without labeled text
S Riedel, L Yao, A McCallum
Machine Learning and Knowledge Discovery in Databases: European Conference …, 2010
16222010
Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity
Y Lu, M Bartolo, A Moore, S Riedel, P Stenetorp
arXiv preprint arXiv:2104.08786, 2021
9712021
The CoNLL 2007 shared task on dependency parsing
J Nivre, J Hall, S Kübler, R McDonald, J Nilsson, S Riedel, D Yuret
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural …, 2007
8842007
Relation extraction with matrix factorization and universal schemas
S Riedel, L Yao, A McCallum, BM Marlin
Proceedings of the 2013 conference of the North American chapter of the …, 2013
8072013
Few-shot learning with retrieval augmented language models
G Izacard, P Lewis, M Lomeli, L Hosseini, F Petroni, T Schick, ...
arXiv preprint arXiv:2208.03299 2 (3), 2022
615*2022
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
M Reid, N Savinov, D Teplyashin, D Lepikhin, T Lillicrap, J Alayrac, ...
arXiv preprint arXiv:2403.05530, 2024
6142024
Unsupervised dense information retrieval with contrastive learning
G Izacard, M Caron, L Hosseini, S Riedel, P Bojanowski, A Joulin, ...
arXiv preprint arXiv:2112.09118, 2021
5872021
Constructing datasets for multi-hop reading comprehension across documents
J Welbl, P Stenetorp, S Riedel
Transactions of the Association for Computational Linguistics 6, 287-302, 2018
5732018
TaBERT: Pretraining for joint understanding of textual and tabular data
P Yin, G Neubig, W Yih, S Riedel
arXiv preprint arXiv:2005.08314, 2020
5682020
Fact checking: Task definition and dataset construction
A Vlachos, S Riedel
Proceedings of the ACL 2014 workshop on language technologies and …, 2014
5532014
Scalable zero-shot entity linking with dense entity retrieval
L Wu, F Petroni, M Josifoski, S Riedel, L Zettlemoyer
arXiv preprint arXiv:1911.03814, 2019
5382019
Autoregressive entity retrieval
N De Cao, G Izacard, S Riedel, F Petroni
arXiv preprint arXiv:2010.00904, 2020
5072020
KILT: a benchmark for knowledge intensive language tasks
F Petroni, A Piktus, A Fan, P Lewis, M Yazdani, N De Cao, J Thorne, ...
arXiv preprint arXiv:2009.02252, 2020
4992020
End-to-end differentiable proving
T Rocktäschel, S Riedel
Advances in neural information processing systems 30, 2017
4892017
MLQA: Evaluating cross-lingual extractive question answering
P Lewis, B Oğuz, R Rinott, S Riedel, H Schwenk
arXiv preprint arXiv:1910.07475, 2019
4752019
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Artículos 1–20