9 citations · 17 across the 8 of their papers we have counts for
8 papers
Active learning for affinity prediction of antibodies
Alexandra Gessner, Sebastian W. Ober, Owen Vickery +2
The primary objective of most lead optimization campaigns is to enhance the binding affinity of ligands. For large molecules such as antibodies, identifying mutations that enhance…
Improving Antibody Humanness Prediction using Patent Data
Talip Ucar, Aubin Ramon, Dino Oglic +3
We investigate the potential of patent data for improving the antibody humanness prediction using a multi-stage, multi-loss training process. Humanness serves as a proxy for the im…
NESS: Node Embeddings from Static SubGraphs
Talip Ucar
We present a framework for learning Node Embeddings from Static Subgraphs (NESS) using a graph autoencoder (GAE) in a transductive setting. NESS is based on two key ideas: i) Parti…
Parameter Averaging for Feature Ranking
Talip Ucar, Ehsan Hajiramezanali
Neural Networks are known to be sensitive to initialisation. The methods that rely on neural networks for feature ranking are not robust since they can have variations in their ran…
SubTab: Subsetting Features of Tabular Data for Self-Supervised Representation Learning
Talip Ucar, Ehsan Hajiramezanali, Lindsay Edwards
Self-supervised learning has been shown to be very effective in learning useful representations, and yet much of the success is achieved in data types such as images, audio, and te…
One-Shot Learning for Language Modelling
Talip Ucar, Adrian Gonzalez-Martin, Matthew Lee +1
Humans can infer a great deal about the meaning of a word, using the syntax and semantics of surrounding words even if it is their first time reading or hearing it. We can also gen…
Inverse Graphics: Unsupervised Learning of 3D Shapes from Single Images
Talip Ucar
Using generative models for Inverse Graphics is an active area of research. However, most works focus on developing models for supervised and semi-supervised methods. In this paper…
Bridging the ELBO and MMD
Talip Ucar
One of the challenges in training generative models such as the variational auto encoder (VAE) is avoiding posterior collapse. When the generator has too much capacity, it is prone…