Search Results for author:
Found 10 papers, 4 papers with code
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
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
Is the deconvolution layer the same as a convolutional layer?
In this note, we want to focus on aspects related to two questions most people asked us at CVPR about the network we presented.
Efficient Wasserstein Natural Gradients for Reinforcement Learning
A novel optimization approach is proposed for application to policy gradient methods and evolution strategies for reinforcement learning (RL).
Exchangeable Models in Meta Reinforcement Learning
One recent approach to meta reinforcement learning (meta-RL) is to integrate models for task inference with models for control.
Optimally-Weighted Herding is Bayesian Quadrature
We show that the criterion minimised when selecting samples in kernel herding is equivalent to the posterior variance in Bayesian quadrature.
Collaborative Gaussian Processes for Preference Learning
We present a new model based on Gaussian processes (GPs) for learning pairwise preferences expressed by multiple users.
Addressing Delayed Feedback for Continuous Training with Neural Networks in CTR prediction
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.
Model Size Reduction Using Frequency Based Double Hashing for Recommender Systems
The large model size usually entails a cost, in the range of millions of dollars, for storage and communication with the inference services.
Deep Bayesian Bandits: Exploring in Online Personalized Recommendations
Recommender systems trained in a continuous learning fashion are plagued by the feedback loop problem, also known as algorithmic bias.
Measuring Disparate Outcomes of Content Recommendation Algorithms with Distributional Inequality Metrics
We show that we can use these metrics to identify content suggestion algorithms that contribute more strongly to skewed outcomes between users.
