Search Results for author: Ivana Balažević

Found 9 papers, 5 papers with code

Learning Representations of Entities and Relations

no code implementations31 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.

Fact Checking Information Retrieval +5

Learning the Prediction Distribution for Semi-Supervised Learning with Normalising Flows

1 code implementation6 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.

General Classification Image Classification +3

A Probabilistic Model for Discriminative and Neuro-Symbolic Semi-Supervised Learning

no code implementations10 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 $p(y|x)$.

Image Augmentation Logical Reasoning

Interpreting Knowledge Graph Relation Representation from Word Embeddings

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.

Link Prediction Word Embeddings

Multi-relational Poincaré Graph Embeddings

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.

Entity Embeddings Knowledge Graphs +1

Hypernetwork Knowledge Graph Embeddings

1 code implementation21 Aug 2018 Ivana Balažević, Carl Allen, Timothy M. Hospedales

Knowledge graphs are graphical representations of large databases of facts, which typically suffer from incompleteness.

Knowledge Graph Embeddings Knowledge Graphs +1

What the Vec? Towards Probabilistically Grounded Embeddings

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.

Graph Embedding Word Embeddings

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