259 citations · 949 across the 15 of their papers we have counts for
17 papers
Should I send this notification? Optimizing push notifications decision making by modeling the future
Conor O'Brien, Huasen Wu, Shaodan Zhai +3
Most recommender systems are myopic, that is they optimize based on the immediate response of the user. This may be misaligned with the true objective, such as creating long term u…
Learning to Rank For Push Notifications Using Pairwise Expected Regret
Yuguang Yue, Yuanpu Xie, Huasen Wu +4
Listwise ranking losses have been widely studied in recommender systems. However, new paradigms of content consumption present new challenges for ranking methods. In this work we c…
The 2021 RecSys Challenge Dataset: Fairness is not optional
Luca Belli, Alykhan Tejani, Frank Portman +10
After the success the RecSys 2020 Challenge, we are describing a novel and bigger dataset that was released in conjunction with the ACM RecSys Challenge 2021. This year's dataset i…
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…
Privacy-Aware Recommender Systems Challenge on Twitter's Home Timeline
Luca Belli, Sofia Ira Ktena, Alykhan Tejani +12
Recommender systems constitute the core engine of most social network platforms nowadays, aiming to maximize user satisfaction along with other key business objectives. Twitter is…
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…
Smile, Be Happy :) Emoji Embedding for Visual Sentiment Analysis
Ziad Al-Halah, Andrew Aitken, Wenzhe Shi +1
Due to the lack of large-scale datasets, the prevailing approach in visual sentiment analysis is to leverage models trained for object classification in large datasets like ImageNe…
Frame Interpolation with Multi-Scale Deep Loss Functions and Generative Adversarial Networks
Joost van Amersfoort, Wenzhe Shi, Alejandro Acosta +4
Frame interpolation attempts to synthesise frames given one or more consecutive video frames. In recent years, deep learning approaches, and notably convolutional neural networks,…
Checkerboard artifact free sub-pixel convolution: A note on sub-pixel convolution, resize convolution and convolution resize
Andrew Aitken, Christian Ledig, Lucas Theis +3
The most prominent problem associated with the deconvolution layer is the presence of checkerboard artifacts in output images and dense labels. To combat this problem, smoothness c…
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…
Fast Face-swap Using Convolutional Neural Networks
Iryna Korshunova, Wenzhe Shi, Joni Dambre +1
We consider the problem of face swapping in images, where an input identity is transformed into a target identity while preserving pose, facial expression, and lighting. To perform…
Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensation
Jose Caballero, Christian Ledig, Andrew Aitken +4
Convolutional neural networks have enabled accurate image super-resolution in real-time. However, recent attempts to benefit from temporal correlations in video super-resolution ha…
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…