475 citations · 1.8k across the 18 of their papers we have counts for
19 papers
Measuring Disparate Outcomes of Content Recommendation Algorithms with Distributional Inequality Metrics
Tomo Lazovich, Luca Belli, Aaron Gonzales +5
The harmful impacts of algorithmic decision systems have recently come into focus, with many examples of systems such as machine learning (ML) models amplifying existing societal b…
Efficient Wasserstein Natural Gradients for Reinforcement Learning
Ted Moskovitz, Michael Arbel, Ferenc Huszar +1
A novel optimization approach is proposed for application to policy gradient methods and evolution strategies for reinforcement learning (RL). The procedure uses a computationally…
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…
Adaptive Paired-Comparison Method for Subjective Video Quality Assessment on Mobile Devices
Katherine Storrs, Sebastiaan Van Leuven, Steve Kojder +2
To effectively evaluate subjective visual quality in weakly-controlled environments, we propose an Adaptive Paired Comparison method based on particle filtering. As our approach re…
BRUNO: A Deep Recurrent Model for Exchangeable Data
Iryna Korshunova, Jonas Degrave, Ferenc Huszár +3
We present a novel model architecture which leverages deep learning tools to perform exact Bayesian inference on sets of high dimensional, complex observations. Our model is provab…
Faster gaze prediction with dense networks and Fisher pruning
Lucas Theis, Iryna Korshunova, Alykhan Tejani +1
Predicting human fixations from images has recently seen large improvements by leveraging deep representations which were pretrained for object recognition. However, as we show in…
On Quadratic Penalties in Elastic Weight Consolidation
Ferenc Huszár
Elastic weight consolidation (EWC, Kirkpatrick et al, 2017) is a novel algorithm designed to safeguard against catastrophic forgetting in neural networks. EWC can be seen as an app…
Lossy Image Compression with Compressive Autoencoders
Lucas Theis, Wenzhe Shi, Andrew Cunningham +1
We propose a new approach to the problem of optimizing autoencoders for lossy image compression. New media formats, changing hardware technology, as well as diverse requirements an…
Variational Inference using Implicit Distributions
Ferenc Huszár
Generative adversarial networks (GANs) have given us a great tool to fit implicit generative models to data. Implicit distributions are ones we can sample from easily, and take der…
Amortised MAP Inference for Image Super-resolution
Casper Kaae Sønderby, Jose Caballero, Lucas Theis +2
Image super-resolution (SR) is an underdetermined inverse problem, where a large number of plausible high-resolution images can explain the same downsampled image. Most current sin…
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network
Wenzhe Shi, Jose Caballero, Ferenc Huszár +5
Recently, several models based on deep neural networks have achieved great success in terms of both reconstruction accuracy and computational performance for single image super-res…
Is the deconvolution layer the same as a convolutional layer?
Wenzhe Shi, Jose Caballero, Lucas Theis +4
In this note, we want to focus on aspects related to two questions most people asked us at CVPR about the network we presented. Firstly, What is the relationship between our propos…
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Christian Ledig, Lucas Theis, Ferenc Huszar +8
Despite the breakthroughs in accuracy and speed of single image super-resolution using faster and deeper convolutional neural networks, one central problem remains largely unsolved…
How (not) to Train your Generative Model: Scheduled Sampling, Likelihood, Adversary?
Ferenc Huszár
Modern applications and progress in deep learning research have created renewed interest for generative models of text and of images. However, even today it is unclear what objecti…
Optimally-Weighted Herding is Bayesian Quadrature
Ferenc Huszar, David Duvenaud
Herding and kernel herding are deterministic methods of choosing samples which summarise a probability distribution. A related task is choosing samples for estimating integrals usi…
Optimally-Weighted Herding is Bayesian Quadrature
Ferenc Huszár, David Duvenaud
Herding and kernel herding are deterministic methods of choosing samples which summarise a probability distribution. A related task is choosing samples for estimating integrals usi…
Bayesian Active Learning for Classification and Preference Learning
Neil Houlsby, Ferenc Huszár, Zoubin Ghahramani +1
Information theoretic active learning has been widely studied for probabilistic models. For simple regression an optimal myopic policy is easily tractable. However, for other tasks…