research
∙
02/17/2022
Should I send this notification? Optimizing push notifications decision making by modeling the future
Most recommender systems are myopic, that is they optimize based on the ...
share
research
∙
01/19/2022
Learning to Rank For Push Notifications Using Pairwise Expected Regret
Listwise ranking losses have been widely studied in recommender systems....
research
∙
08/03/2020
Deep Bayesian Bandits: Exploring in Online Personalized Recommendations
Recommender systems trained in a continuous learning fashion are plagued...
research
∙
07/28/2020
Model Size Reduction Using Frequency Based Double Hashing for Recommender Systems
Deep Neural Networks (DNNs) with sparse input features have been widely ...
research
∙
04/28/2020
Privacy-Preserving Recommender Systems Challenge on Twitter's Home Timeline
Recommender systems constitute the core engine of most social network pl...
research
∙
07/15/2019
Addressing Delayed Feedback for Continuous Training with Neural Networks in CTR prediction
One of the challenges in display advertising is that the distribution of...
research
∙
07/14/2019
Smile, be Happy :) Emoji Embedding for Visual Sentiment Analysis
Due to the lack of large-scale datasets, the prevailing approach in visu...
research
∙
11/16/2017
Frame Interpolation with Multi-Scale Deep Loss Functions and Generative Adversarial Networks
Frame interpolation attempts to synthesise intermediate frames given one...
research
∙
07/10/2017
Checkerboard artifact free sub-pixel convolution: A note on sub-pixel convolution, resize convolution and convolution resize
The most prominent problem associated with the deconvolution layer is th...
research
∙
03/01/2017
Lossy Image Compression with Compressive Autoencoders
We propose a new approach to the problem of optimizing autoencoders for ...
research
∙
11/29/2016
Fast Face-swap Using Convolutional Neural Networks
We consider the problem of face swapping in images, where an input ident...
research
∙
11/16/2016
Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensation
Convolutional neural networks have enabled accurate image super-resoluti...
research
∙
10/14/2016
Amortised MAP Inference for Image Super-resolution
Image super-resolution (SR) is an underdetermined inverse problem, where...
research
∙
09/22/2016
Is the deconvolution layer the same as a convolutional layer?
In this note, we want to focus on aspects related to two questions most ...
research
∙
09/16/2016
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network
Recently, several models based on deep neural networks have achieved gre...
research
∙
09/15/2016