74 citations · 78 across the 4 of their papers we have counts for
7 papers
Deep Bayesian Bandits: Exploring in Online Personalized Recommendations
Dalin Guo, Sofia Ira Ktena, Ferenc Huszar +3
Recommender systems trained in a continuous learning fashion are plagued by the feedback loop problem, also known as algorithmic bias. This causes a newly trained model to act gree…
Model Size Reduction Using Frequency Based Double Hashing for Recommender Systems
Caojin Zhang, Yicun Liu, Yuanpu Xie +10
Deep Neural Networks (DNNs) with sparse input features have been widely used in recommender systems in industry. These models have large memory requirements and need a huge amount…
Addressing Delayed Feedback for Continuous Training with Neural Networks in CTR prediction
Sofia Ira Ktena, Alykhan Tejani, Lucas Theis +5
One of the challenges in display advertising is that the distribution of features and click through rate (CTR) can exhibit large shifts over time due to seasonality, changes to ad…
Graph Saliency Maps through Spectral Convolutional Networks: Application to Sex Classification with Brain Connectivity
Salim Arslan, Sofia Ira Ktena, Ben Glocker +1
Graph convolutional networks (GCNs) allow to apply traditional convolution operations in non-Euclidean domains, where data are commonly modelled as irregular graphs. Medical imagin…
Disease Prediction using Graph Convolutional Networks: Application to Autism Spectrum Disorder and Alzheimer's Disease
Sarah Parisot, Sofia Ira Ktena, Enzo Ferrante +4
Graphs are widely used as a natural framework that captures interactions between individual elements represented as nodes in a graph. In medical applications, specifically, nodes c…
DLTK: State of the Art Reference Implementations for Deep Learning on Medical Images
Nick Pawlowski, Sofia Ira Ktena, Matthew C. H. Lee +4
We present DLTK, a toolkit providing baseline implementations for efficient experimentation with deep learning methods on biomedical images. It builds on top of TensorFlow and its…