b'Lucas Theis'

research

∙ 05/26/2023

High-Fidelity Image Compression with Score-based Generative Models

Despite the tremendous success of diffusion generative models in text-to...

0 Emiel Hoogeboom, et al. ∙

research

∙ 04/20/2023

Adaptive Greedy Rejection Sampling

We consider channel simulation protocols between two communicating parti...

0 Gergely Flamich, et al. ∙

research

∙ 06/17/2022

Lossy Compression with Gaussian Diffusion

We describe a novel lossy compression approach called DiffC which is bas...

1 Lucas Theis, et al. ∙

research

∙ 02/14/2022

An Introduction to Neural Data Compression

Neural compression is the application of neural networks and other machi...

0 Yibo Yang, et al. ∙

research

∙ 10/29/2021

Optimal Compression of Locally Differentially Private Mechanisms

Compressing the output of ϵ-locally differentially private (LDP) randomi...

2 Abhin Shah, et al. ∙

research

∙ 10/25/2021

Algorithms for the Communication of Samples

We consider the problem of reverse channel coding, that is, how to simul...

0 Lucas Theis, et al. ∙

research

∙ 04/28/2021

A coding theorem for the rate-distortion-perception function

The rate-distortion-perception function (RDPF; Blau and Michaeli, 2019) ...

0 Lucas Theis, et al. ∙

research

∙ 02/18/2021

On the advantages of stochastic encoders

Stochastic encoders have been used in rate-distortion theory and neural ...

0 Lucas Theis, et al. ∙

research

∙ 06/17/2020

Universally Quantized Neural Compression

A popular approach to learning encoders for lossy compression is to use ...

7 Eirikur Agustsson, et al. ∙

research

∙ 09/03/2019

Discriminative Topic Modeling with Logistic LDA

Despite many years of research into latent Dirichlet allocation (LDA), a...

17 Iryna Korshunova, et al. ∙

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...

1 Ira Ktena, et al. ∙

research

∙ 04/02/2019

HoloGAN: Unsupervised learning of 3D representations from natural images

We propose a novel generative adversarial network (GAN) for the task of ...

68 Thu Nguyen-Phuoc, et al. ∙

research

∙ 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. ∙

research

∙ 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. ∙

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...

0 Andrew Aitken, et al. ∙

research

∙ 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. ∙

research

∙ 11/29/2016

Fast Face-swap Using Convolutional Neural Networks

We consider the problem of face swapping in images, where an input ident...

0 Iryna Korshunova, et al. ∙

research

∙ 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. ∙

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 ...

0 Wenzhe Shi, et al. ∙

research

∙ 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. ∙

research

∙ 11/05/2015

A note on the evaluation of generative models

Probabilistic generative models can be used for compression, denoising, ...

0 Lucas Theis, et al. ∙

research

∙ 06/10/2015

Generative Image Modeling Using Spatial LSTMs

Modeling the distribution of natural images is challenging, partly becau...

0 Lucas Theis, et al. ∙

research

∙ 05/28/2015

A Generative Model of Natural Texture Surrogates

Natural images can be viewed as patchworks of different textures, where ...

0 Niklas Ludtke, et al. ∙

research

∙ 05/28/2015

A trust-region method for stochastic variational inference with applications to streaming data

Stochastic variational inference allows for fast posterior inference in ...

0 Lucas Theis, et al. ∙

research

∙ 02/28/2015

Supervised learning sets benchmark for robust spike detection from calcium imaging signals

A fundamental challenge in calcium imaging has been to infer the timing ...

0 Lucas Theis, et al. ∙

research

∙ 11/04/2014

Deep Gaze I: Boosting Saliency Prediction with Feature Maps Trained on ImageNet

Recent results suggest that state-of-the-art saliency models perform far...

0 Matthias Kümmerer, et al. ∙

research

∙ 10/17/2014

Inference and Mixture Modeling with the Elliptical Gamma Distribution

We study modeling and inference with the Elliptical Gamma Distribution (...

0 Reshad Hosseini, et al. ∙

research

∙ 09/20/2011

Mixtures of conditional Gaussian scale mixtures applied to multiscale image representations

We present a probabilistic model for natural images which is based on Ga...

0 Lucas Theis, et al. ∙

research

∙ 11/28/2010

In All Likelihood, Deep Belief Is Not Enough

Statistical models of natural stimuli provide an important tool for rese...

0 Lucas Theis, et al. ∙

Lucas Theis

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