259 citations · 424 across the 5 of their papers we have counts for
6 papers · 1 filter
Universally Quantized Neural Compression
Eirikur Agustsson, Lucas Theis
A popular approach to learning encoders for lossy compression is to use additive uniform noise during training as a differentiable approximation to test-time quantization. We demon…
Discriminative Topic Modeling with Logistic LDA
Iryna Korshunova, Hanchen Xiong, Mateusz Fedoryszak +1
Despite many years of research into latent Dirichlet allocation (LDA), applying LDA to collections of non-categorical items is still challenging. Yet many problems with much richer…
Addressing Delayed Feedback for Continuous Training with Neural Networks in CTR prediction
Sofia Ira Ktena, Alykhan Tejani, Lucas Theis +5
One of the challenges in display advertising is that the distribution of features and click through rate (CTR) can exhibit large shifts over time due to seasonality, changes to ad…
Lossy Image Compression with Compressive Autoencoders
Lucas Theis, Wenzhe Shi, Andrew Cunningham +1
We propose a new approach to the problem of optimizing autoencoders for lossy image compression. New media formats, changing hardware technology, as well as diverse requirements an…
A trust-region method for stochastic variational inference with applications to streaming data
Lucas Theis, Matthew D. Hoffman
Stochastic variational inference allows for fast posterior inference in complex Bayesian models. However, the algorithm is prone to local optima which can make the quality of the p…
Supervised learning sets benchmark for robust spike detection from calcium imaging signals
Lucas Theis, Philipp Berens, Emmanouil Froudarakis +6
A fundamental challenge in calcium imaging has been to infer the timing of action potentials from the measured noisy calcium fluorescence traces. We systematically evaluate a range…