activity
20102024
most citedSemi-Supervised Kernel PCA

2 citations · 5 across the 5 of their papers we have counts for

collaborators

5 papers

cs.IR2024

Learning k-Determinantal Point Processes for Personalized Ranking

Yuli Liu, Christian Walder, Lexing Xie

The key to personalized recommendation is to predict a personalized ranking on a catalog of items by modeling the user's preferences. There are many personalized ranking approaches…

cs.CV2023

DualVAE: Controlling Colours of Generated and Real Images

Keerth Rathakumar, David Liebowitz, Christian Walder +2

Colour controlled image generation and manipulation are of interest to artists and graphic designers. Vector Quantised Variational AutoEncoders (VQ-VAEs) with autoregressive (AR) p…

cs.LG20232 cited

R-U-SURE? Uncertainty-Aware Code Suggestions By Maximizing Utility Across Random User Intents

Daniel D. Johnson, Daniel Tarlow, Christian Walder

Large language models show impressive results at predicting structured text such as code, but also commonly introduce errors and hallucinations in their output. When used to assist…

cs.LG20231 cited

Sampled Transformer for Point Sets

Shidi Li, Christian Walder, Alexander Soen +2

The sparse transformer can reduce the computational complexity of the self-attention layers to , whilst still being a universal approximator of continuous sequence-to-sequenc…

cs.LG20102 cited

Semi-Supervised Kernel PCA

Christian Walder, Ricardo Henao, Morten Mørup +1

We present three generalisations of Kernel Principal Components Analysis (KPCA) which incorporate knowledge of the class labels of a subset of the data points. The first, MV-KPCA,…