138 code implementations • CVPR 2017 • Christian Ledig, Lucas Theis, Ferenc Huszar, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, Wenzhe Shi
The adversarial loss pushes our solution to the natural image manifold using a discriminator network that is trained to differentiate between the super-resolved images and original photo-realistic images.
Ranked #3 on Image Super-Resolution on VggFace2 - 8x upscaling
6 code implementations • 22 Sep 2016 • Wenzhe Shi, Jose Caballero, Lucas Theis, Ferenc Huszar, Andrew Aitken, Christian Ledig, Zehan Wang
In this note, we want to focus on aspects related to two questions most people asked us at CVPR about the network we presented.
1 code implementation • ICLR 2021 • Ted Moskovitz, Michael Arbel, Ferenc Huszar, Arthur Gretton
A novel optimization approach is proposed for application to policy gradient methods and evolution strategies for reinforcement learning (RL).
1 code implementation • ICML Workshop LifelongML 2020 • Iryna Korshunova, Jonas Degrave, Joni Dambre, Arthur Gretton, Ferenc Huszar
One recent approach to meta reinforcement learning (meta-RL) is to integrate models for task inference with models for control.
no code implementations • 9 Aug 2014 • Ferenc Huszar, David Duvenaud
We show that the criterion minimised when selecting samples in kernel herding is equivalent to the posterior variance in Bayesian quadrature.
no code implementations • NeurIPS 2012 • Neil Houlsby, Ferenc Huszar, Zoubin Ghahramani, Jose M. Hernández-Lobato
We present a new model based on Gaussian processes (GPs) for learning pairwise preferences expressed by multiple users.
no code implementations • 15 Jul 2019 • Sofia Ira Ktena, Alykhan Tejani, Lucas Theis, Pranay Kumar Myana, Deepak Dilipkumar, Ferenc Huszar, Steven Yoo, Wenzhe Shi
The focus of this paper is to identify the best combination of loss functions and models that enable large-scale learning from a continuous stream of data in the presence of delayed labels.
no code implementations • 28 Jul 2020 • Caojin Zhang, Yicun Liu, Yuanpu Xie, Sofia Ira Ktena, Alykhan Tejani, Akshay Gupta, Pranay Kumar Myana, Deepak Dilipkumar, Suvadip Paul, Ikuhiro Ihara, Prasang Upadhyaya, Ferenc Huszar, Wenzhe Shi
The large model size usually entails a cost, in the range of millions of dollars, for storage and communication with the inference services.
no code implementations • 3 Aug 2020 • Dalin Guo, Sofia Ira Ktena, Ferenc Huszar, Pranay Kumar Myana, Wenzhe Shi, Alykhan Tejani
Recommender systems trained in a continuous learning fashion are plagued by the feedback loop problem, also known as algorithmic bias.
no code implementations • 3 Feb 2022 • Tomo Lazovich, Luca Belli, Aaron Gonzales, Amanda Bower, Uthaipon Tantipongpipat, Kristian Lum, Ferenc Huszar, Rumman Chowdhury
We show that we can use these metrics to identify content suggestion algorithms that contribute more strongly to skewed outcomes between users.