924 citations · 4.1k across the 42 of their papers we have counts for
7 papers · 1 filter
PoseAgent: Budget-Constrained 6D Object Pose Estimation via Reinforcement Learning
Alexander Krull, Eric Brachmann, Sebastian Nowozin +3
State-of-the-art computer vision algorithms often achieve efficiency by making discrete choices about which hypotheses to explore next. This allows allocation of computational reso…
Memory Lens: How Much Memory Does an Agent Use?
Christoph Dann, Katja Hofmann, Sebastian Nowozin
We propose a new method to study the internal memory used by reinforcement learning policies. We estimate the amount of relevant past information by estimating mutual information b…
Probabilistic Duality for Parallel Gibbs Sampling without Graph Coloring
Lars Mescheder, Sebastian Nowozin, Andreas Geiger
We present a new notion of probabilistic duality for random variables involving mixture distributions. Using this notion, we show how to implement a highly-parallelizable Gibbs sam…
DSAC - Differentiable RANSAC for Camera Localization
Eric Brachmann, Alexander Krull, Sebastian Nowozin +4
RANSAC is an important algorithm in robust optimization and a central building block for many computer vision applications. In recent years, traditionally hand-crafted pipelines ha…
DeepCoder: Learning to Write Programs
Matej Balog, Alexander L. Gaunt, Marc Brockschmidt +2
We develop a first line of attack for solving programming competition-style problems from input-output examples using deep learning. The approach is to train a neural network to pr…
f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization
Sebastian Nowozin, Botond Cseke, Ryota Tomioka
Generative neural samplers are probabilistic models that implement sampling using feedforward neural networks: they take a random input vector and produce a sample from a probabili…
DISCO Nets: DISsimilarity COefficient Networks
Diane Bouchacourt, M. Pawan Kumar, Sebastian Nowozin
We present a new type of probabilistic model which we call DISsimilarity COefficient Networks (DISCO Nets). DISCO Nets allow us to efficiently sample from a posterior distribution…