activity
20172020
most citedRethinking Full Connectivity in Recurrent Neural Networks

14 citations · 14 across the 1 of their papers we have counts for

collaborators

7 papers

cs.LG2020

Neural Control Variates

Thomas Müller, Fabrice Rousselle, Jan Novák +1

We propose neural control variates (NCV) for unbiased variance reduction in parametric Monte Carlo integration. So far, the core challenge of applying the method of control variate…

cs.LG201914 cited

Rethinking Full Connectivity in Recurrent Neural Networks

Matthijs Van Keirsbilck, Alexander Keller, Xiaodong Yang

Recurrent neural networks (RNNs) are omnipresent in sequence modeling tasks. Practical models usually consist of several layers of hundreds or thousands of neurons which are fully…

cs.LG2019

Instant Quantization of Neural Networks using Monte Carlo Methods

Gonçalo Mordido, Matthijs Van Keirsbilck, Alexander Keller

Low bit-width integer weights and activations are very important for efficient inference, especially with respect to lower power consumption. We propose Monte Carlo methods to quan…

cs.DC2019

Massively Parallel Construction of Radix Tree Forests for the Efficient Sampling of Discrete Probability Distributions

Nikolaus Binder, Alexander Keller

We compare different methods for sampling from discrete probability distributions and introduce a new algorithm which is especially efficient on massively parallel processors, such…

cs.GR2018

Massively Parallel Stackless Ray Tracing of Catmull-Clark Subdivision Surfaces

Nikolaus Binder, Alexander Keller

We present a fast and efficient method for intersecting rays with Catmull-Clark subdivision surfaces. It takes advantage of the approximation democratized by OpenSubdiv, in which r…

cs.GR2018

Fast, High Precision Ray/Fiber Intersection using Tight, Disjoint Bounding Volumes

Nikolaus Binder, Alexander Keller

Analyzing and identifying the shortcomings of current subdivision methods for finding intersections of rays with fibers defined by the surface of a circular contour swept along a B…