171 citations · 454 across the 29 of their papers we have counts for
5 papers · 1 filter
Separating Self-Expression and Visual Content in Hashtag Supervision
Andreas Veit, Maximilian Nickel, Serge Belongie +1
The variety, abundance, and structured nature of hashtags make them an interesting data source for training vision models. For instance, hashtags have the potential to significantl…
Fast Linear Model for Knowledge Graph Embeddings
Armand Joulin, Edouard Grave, Piotr Bojanowski +2
This paper shows that a simple baseline based on a Bag-of-Words (BoW) representation learns surprisingly good knowledge graph embeddings. By casting knowledge base completion and q…
Complex and Holographic Embeddings of Knowledge Graphs: A Comparison
Théo Trouillon, Maximilian Nickel
Embeddings of knowledge graphs have received significant attention due to their excellent performance for tasks like link prediction and entity resolution. In this short paper, we…
Learning Visually Grounded Sentence Representations
Douwe Kiela, Alexis Conneau, Allan Jabri +1
We introduce a variety of models, trained on a supervised image captioning corpus to predict the image features for a given caption, to perform sentence representation grounding. W…
Poincaré Embeddings for Learning Hierarchical Representations
Maximilian Nickel, Douwe Kiela
Representation learning has become an invaluable approach for learning from symbolic data such as text and graphs. However, while complex symbolic datasets often exhibit a latent h…