29 citations · 35 across the 5 of their papers we have counts for
5 papers
Variational Classification
Shehzaad Dhuliawala, Mrinmaya Sachan, Carl Allen
We present a latent variable model for classification that provides a novel probabilistic interpretation of neural network softmax classifiers. We derive a variational objective to…
Learning to Drop Out: An Adversarial Approach to Training Sequence VAEs
Đorđe Miladinović, Kumar Shridhar, Kushal Jain +4
In principle, applying variational autoencoders (VAEs) to sequential data offers a method for controlled sequence generation, manipulation, and structured representation learning.…
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…
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…