642 citations · 951 across the 12 of their papers we have counts for
12 papers
Context-Aware Multimodal Pretraining
Karsten Roth, Zeynep Akata, Dima Damen +2
Large-scale multimodal representation learning successfully optimizes for zero-shot transfer at test time. Yet the standard pretraining paradigm (contrastive learning on large amou…
PaliGemma: A versatile 3B VLM for transfer
Lucas Beyer, Andreas Steiner, André Susano Pinto +32
PaliGemma is an open Vision-Language Model (VLM) that is based on the SigLIP-So400m vision encoder and the Gemma-2B language model. It is trained to be a versatile and broadly know…
Towards In-context Scene Understanding
Ivana Balažević, David Steiner, Nikhil Parthasarathy +2
In-context learning$\unicode{x2013}$the ability to configure a model's behavior with different prompts$\unicode{x2013}$has revolutionized the field of natural language processing,…
Learning Representations of Entities and Relations
Ivana Balažević
Encoding facts as representations of entities and binary relationships between them, as learned by knowledge graph representation models, is useful for various tasks, including pre…
Learning the Prediction Distribution for Semi-Supervised Learning with Normalising Flows
Ivana Balažević, Carl Allen, Timothy Hospedales
As data volumes continue to grow, the labelling process increasingly becomes a bottleneck, creating demand for methods that leverage information from unlabelled data. Impressive re…
Benchmark and Best Practices for Biomedical Knowledge Graph Embeddings
David Chang, Ivana Balazevic, Carl Allen +3
Much of biomedical and healthcare data is encoded in discrete, symbolic form such as text and medical codes. There is a wealth of expert-curated biomedical domain knowledge stored…
Interpreting Knowledge Graph Relation Representation from Word Embeddings
Carl Allen, Ivana Balažević, Timothy Hospedales
Many models learn representations of knowledge graph data by exploiting its low-rank latent structure, encoding known relations between entities and enabling unknown facts to be in…
Multi-relational Poincaré Graph Embeddings
Ivana Balažević, Carl Allen, Timothy Hospedales
Hyperbolic embeddings have recently gained attention in machine learning due to their ability to represent hierarchical data more accurately and succinctly than their Euclidean ana…
TuckER: Tensor Factorization for Knowledge Graph Completion
Ivana Balažević, Carl Allen, Timothy M. Hospedales
Knowledge graphs are structured representations of real world facts. However, they typically contain only a small subset of all possible facts. Link prediction is a task of inferri…
Hypernetwork Knowledge Graph Embeddings
Ivana Balažević, Carl Allen, Timothy M. Hospedales
Knowledge graphs are graphical representations of large databases of facts, which typically suffer from incompleteness. Inferring missing relations (links) between entities (nodes)…
What the Vec? Towards Probabilistically Grounded Embeddings
Carl Allen, Ivana Balažević, Timothy Hospedales
Word2Vec (W2V) and GloVe are popular, fast and efficient word embedding algorithms. Their embeddings are widely used and perform well on a variety of natural language processing ta…
Language Detection For Short Text Messages In Social Media
Ivana Balazevic, Mikio Braun, Klaus-Robert Müller
With the constant growth of the World Wide Web and the number of documents in different languages accordingly, the need for reliable language detection tools has increased as well.…