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Sebastian Ruder

Cohere

82 papers hereh-index 5731.6k citations109 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • sole author2
  • first author15
  • middle author48
  • last author13

Across the 78 of 82 papers where every author was matched, so the position is known.

fields
  • cs.CL74
  • cs.LG7
  • stat.ML1
affiliations
  • Cohere
Homepage
same name
  • Sebastian Ruder — 5 papers, h 3
  • Sebastian Ruder — 4 papers
  • Sebastian Ruder — 3 papers, h 4
  • Sebastian Ruder — 2 papers, h 4

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20162024
most citedAn overview of gradient descent optimization algorithms

4.8k citations · 9.5k across the 67 of their papers we have counts for

collaborators
Showing 2017 · cs.CLShow all

4 papers · 2 filters

cs.CL2017

Learning to select data for transfer learning with Bayesian Optimization

Sebastian Ruder, Barbara Plank

Domain similarity measures can be used to gauge adaptability and select suitable data for transfer learning, but existing approaches define ad hoc measures that are deemed suitable…

cs.CL2017★ 268 cited

A Survey Of Cross-lingual Word Embedding Models

Sebastian Ruder, Ivan Vulić, Anders Søgaard

Cross-lingual representations of words enable us to reason about word meaning in multilingual contexts and are a key facilitator of cross-lingual transfer when developing natural l…

cs.CL2017★ 10 cited

Data Selection Strategies for Multi-Domain Sentiment Analysis

Sebastian Ruder, Parsa Ghaffari, John G. Breslin

Domain adaptation is important in sentiment analysis as sentiment-indicating words vary between domains. Recently, multi-domain adaptation has become more pervasive, but existing a…

cs.CL2017★ 41 cited

Knowledge Adaptation: Teaching to Adapt

Sebastian Ruder, Parsa Ghaffari, John G. Breslin

Domain adaptation is crucial in many real-world applications where the distribution of the training data differs from the distribution of the test data. Previous Deep Learning-base…

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