Search Results for author:
Found 15 papers, 8 papers with code
Multi-scale Attributed Node Embedding
We present network embedding algorithms that capture information about a node from the local distribution over node attributes around it, as observed over random walks following an approach similar to Skip-gram.
TuckER: Tensor Factorization for Knowledge Graph Completion
Knowledge graphs are structured representations of real world facts.
Ranked #10 on
Link Prediction
on WN18
Multi-relational Poincaré Graph Embeddings
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
Hypernetwork Knowledge Graph Embeddings
Knowledge graphs are graphical representations of large databases of facts, which typically suffer from incompleteness.
Ranked #10 on
Link Prediction
on WN18
Benchmark and Best Practices for Biomedical Knowledge Graph Embeddings
Much of biomedical and healthcare data is encoded in discrete, symbolic form such as text and medical codes.
Adapters for Enhanced Modeling of Multilingual Knowledge and Text
Specifically, we introduce a lightweight adapter set to enhance MLLMs with cross-lingual entity alignment and facts from MLKGs for many languages.
Learning the Prediction Distribution for Semi-Supervised Learning with Normalising Flows
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.
Variational Classification
We present a latent variable model for classification that provides a novel probabilistic interpretation of neural network softmax classifiers.
What the Vec? Towards Probabilistically Grounded Embeddings
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.
Analogies Explained: Towards Understanding Word Embeddings
Word embeddings generated by neural network methods such as word2vec (W2V) are well known to exhibit seemingly linear behaviour, e. g. the embeddings of analogy "woman is to queen as man is to king" approximately describe a parallelogram.
Interpreting Knowledge Graph Relation Representation from Word Embeddings
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.
A Probabilistic Model for Discriminative and Neuro-Symbolic Semi-Supervised Learning
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 $p(y|x)$.
Towards a Theoretical Understanding of Word and Relation Representation
To address this: 1. we theoretically justify the empirical observation that particular geometric relationships between word embeddings learned by algorithms such as word2vec and GloVe correspond to semantic relations between words; and 2. we extend this correspondence between semantics and geometry to the entities and relations of knowledge graphs, providing a model for the latent structure of knowledge graph representation linked to that of word embeddings.
Learning to Drop Out: An Adversarial Approach to Training Sequence VAEs
In principle, applying variational autoencoders (VAEs) to sequential data offers a method for controlled sequence generation, manipulation, and structured representation learning.
A Probabilistic Model to explain Self-Supervised Representation Learning
Self-supervised learning (SSL) learns representations by leveraging an auxiliary unsupervised task, such as classifying semantically related samples, e. g. different data augmentations or modalities.
