411 citations · 1.7k across the 27 of their papers we have counts for
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A note on the evaluation of generative models
Lucas Theis, Aäron van den Oord, Matthias Bethge
Probabilistic generative models can be used for compression, denoising, inpainting, texture synthesis, semi-supervised learning, unsupervised feature learning, and other tasks. Giv…
Generative Image Modeling Using Spatial LSTMs
Lucas Theis, Matthias Bethge
Modeling the distribution of natural images is challenging, partly because of strong statistical dependencies which can extend over hundreds of pixels. Recurrent neural networks ha…
A Generative Model of Natural Texture Surrogates
Niklas Ludtke, Debapriya Das, Lucas Theis +1
Natural images can be viewed as patchworks of different textures, where the local image statistics is roughly stationary within a small neighborhood but otherwise varies from regio…
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…