Ferenc Huszár | DeepAI

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Neil Houlsby
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∙ 12/15/2022

Meta-Learned Kernel For Blind Super-Resolution Kernel Estimation

Recent image degradation estimation methods have enabled single-image su...

0 Royson Lee, et al. ∙

∙ 10/19/2022

Rethinking Sharpness-Aware Minimization as Variational Inference

Sharpness-aware minimization (SAM) aims to improve the generalisation of...

0 Szilvia Ujváry, et al. ∙

∙ 03/29/2022

Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Data

Learning invariant causal structure often relies on conditional independ...

0 Siyuan Guo, et al. ∙

∙ 02/03/2022

Measuring Disparate Outcomes of Content Recommendation Algorithms with Distributional Inequality Metrics

The harmful impacts of algorithmic decision systems have recently come i...

7 Tomo Lazovich, et al. ∙

∙ 11/22/2021

Depth Without the Magic: Inductive Bias of Natural Gradient Descent

In gradient descent, changing how we parametrize the model can lead to d...

0 Anna Kerekes, et al. ∙

∙ 10/21/2021

Algorithmic Amplification of Politics on Twitter

Content on Twitter's home timeline is selected and ordered by personaliz...

0 Ferenc Huszár, et al. ∙

∙ 10/12/2020

Efficient Wasserstein Natural Gradients for Reinforcement Learning

A novel optimization approach is proposed for application to policy grad...

0 Ted Moskovitz, et al. ∙

∙ 08/03/2020

Deep Bayesian Bandits: Exploring in Online Personalized Recommendations

Recommender systems trained in a continuous learning fashion are plagued...

5 Dalin Guo, et al. ∙

∙ 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 ...

13 Caojin Zhang, et al. ∙

∙ 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...

1 Ira Ktena, et al. ∙

∙ 07/05/2018

Adaptive Paired-Comparison Method for Subjective Video Quality Assessment on Mobile Devices

To effectively evaluate subjective visual quality in weakly-controlled e...

0 Katherine Storrs, et al. ∙

∙ 02/21/2018

A Generative Deep Recurrent Model for Exchangeable Data

We present a novel model architecture which leverages deep learning tool...

0 Iryna Korshunova, et al. ∙

∙ 01/17/2018

Faster gaze prediction with dense networks and Fisher pruning

Predicting human fixations from images has recently seen large improveme...

0 Lucas Theis, et al. ∙

∙ 12/11/2017

On Quadratic Penalties in Elastic Weight Consolidation

Elastic weight consolidation (EWC, Kirkpatrick et al, 2017) is a novel a...

0 Ferenc Huszár, et al. ∙

∙ 03/01/2017

Lossy Image Compression with Compressive Autoencoders

We propose a new approach to the problem of optimizing autoencoders for ...

0 Lucas Theis, et al. ∙

∙ 02/27/2017

Variational Inference using Implicit Distributions

Generative adversarial networks (GANs) have given us a great tool to fit...

0 Ferenc Huszár, et al. ∙

∙ 10/14/2016

Amortised MAP Inference for Image Super-resolution

Image super-resolution (SR) is an underdetermined inverse problem, where...

0 Casper Kaae Sønderby, et al. ∙

∙ 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 ...

0 Wenzhe Shi, et al. ∙

∙ 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...

0 Wenzhe Shi, et al. ∙

∙ 09/15/2016

Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

Despite the breakthroughs in accuracy and speed of single image super-re...

0 Christian Ledig, et al. ∙

∙ 11/16/2015

How (not) to Train your Generative Model: Scheduled Sampling, Likelihood, Adversary?

Modern applications and progress in deep learning research have created ...

0 Ferenc Huszár, et al. ∙

∙ 08/09/2014

Optimally-Weighted Herding is Bayesian Quadrature

Herding and kernel herding are deterministic methods of choosing samples...

0 Ferenc Huszár, et al. ∙

∙ 12/24/2011

Bayesian Active Learning for Classification and Preference Learning

Information theoretic active learning has been widely studied for probab...

0 Neil Houlsby, et al. ∙

∙ 03/09/2011

A Kernel Approach to Tractable Bayesian Nonparametrics

Inference in popular nonparametric Bayesian models typically relies on s...

0 Ferenc Huszár, et al. ∙

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