no code implementations • 8 Feb 2024 • Ivana Balažević, Yuge Shi, Pinelopi Papalampidi, Rahma Chaabouni, Skanda Koppula, Olivier J. Hénaff
Most transformer-based video encoders are limited to short temporal contexts due to their quadratic complexity.
no code implementations • 31 Jan 2022 • Ivana Balažević
The first contribution of this thesis is HypER, a convolutional model which simplifies and improves upon the link prediction performance of the existing convolutional state-of-the-art model ConvE and can be mathematically explained in terms of constrained tensor factorisation.
2 code implementations • Findings (ACL) 2022 • Robert L. Logan IV, Ivana Balažević, Eric Wallace, Fabio Petroni, Sameer Singh, Sebastian Riedel
Prompting language models (LMs) with training examples and task descriptions has been seen as critical to recent successes in few-shot learning.
1 code implementation • 6 Jul 2020 • Ivana Balažević, Carl Allen, Timothy Hospedales
In this work, we propose a probabilistically principled general approach to SSL that considers the distribution over label predictions, for labels of different complexity, from "one-hot" vectors to binary vectors and images.
no code implementations • 10 Jun 2020 • Carl Allen, Ivana Balažević, Timothy Hospedales
Much progress has been made in semi-supervised learning (SSL) by combining methods that exploit different aspects of the data distribution, e. g. consistency regularisation relies on properties of $p(x)$, whereas entropy minimisation pertains to the label distribution $p(y|x)$.
no code implementations • ICLR 2021 • 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 inferred.
1 code implementation • NeurIPS 2019 • 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 analogues.
Ranked #38 on Link Prediction on WN18RR
5 code implementations • IJCNLP 2019 • Ivana Balažević, Carl Allen, Timothy M. Hospedales
Knowledge graphs are structured representations of real world facts.
Ranked #10 on Link Prediction on WN18
1 code implementation • 21 Aug 2018 • Ivana Balažević, Carl Allen, Timothy M. Hospedales
Knowledge graphs are graphical representations of large databases of facts, which typically suffer from incompleteness.
Ranked #10 on Link Prediction on WN18
no code implementations • NeurIPS 2019 • Carl Allen, Ivana Balažević, Timothy Hospedales
We show that different interactions between PMI vectors reflect semantic word relationships, such as similarity and paraphrasing, that are encoded in low dimensional word embeddings under a suitable projection, theoretically explaining why embeddings of W2V and GloVe work.