54 citations · 111 across the 5 of their papers we have counts for
7 papers
Differentiable Programming of Reaction-Diffusion Patterns
Alexander Mordvintsev, Ettore Randazzo, Eyvind Niklasson
Reaction-Diffusion (RD) systems provide a computational framework that governs many pattern formation processes in nature. Current RD system design practices boil down to trial-and…
Texture Generation with Neural Cellular Automata
Alexander Mordvintsev, Eyvind Niklasson, Ettore Randazzo
Neural Cellular Automata (NCA) have shown a remarkable ability to learn the required rules to "grow" images, classify morphologies, segment images, as well as to do general computa…
Image segmentation via Cellular Automata
Mark Sandler, Andrey Zhmoginov, Liangcheng Luo +3
In this paper, we propose a new approach for building cellular automata to solve real-world segmentation problems. We design and train a cellular automaton that can successfully se…
MPLP: Learning a Message Passing Learning Protocol
Ettore Randazzo, Eyvind Niklasson, Alexander Mordvintsev
We present a novel method for learning the weights of an artificial neural network - a Message Passing Learning Protocol (MPLP). In MPLP, we abstract every operations occurring in…
GPGPU Linear Complexity t-SNE Optimization
Nicola Pezzotti, Julian Thijssen, Alexander Mordvintsev +5
The t-distributed Stochastic Neighbor Embedding (tSNE) algorithm has become in recent years one of the most used and insightful techniques for the exploratory data analysis of high…
Associative Domain Adaptation
Philip Haeusser, Thomas Frerix, Alexander Mordvintsev +1
We propose associative domain adaptation, a novel technique for end-to-end domain adaptation with neural networks, the task of inferring class labels for an unlabeled target domain…
Learning by Association - A versatile semi-supervised training method for neural networks
Philip Häusser, Alexander Mordvintsev, Daniel Cremers
In many real-world scenarios, labeled data for a specific machine learning task is costly to obtain. Semi-supervised training methods make use of abundantly available unlabeled dat…